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Ericsson Technology Review: issue 1, 2020

The document discusses emerging innovations in telecommunications, particularly focusing on 5G technologies and privacy-aware machine learning through federated learning. It emphasizes the potential of federated learning to enhance functionality while addressing security concerns by allowing the training of machine learning models without transferring sensitive user data. Additionally, the document outlines strategies for mobile network operators to efficiently transition from existing networks to 5G, highlighting the importance of edge computing and AI in improving network performance.

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ERICSSON TECHNOLOGY C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | V O L U M E 1 0 1 I 2 0 2 0 – 0 1 5GNEWRADIO EVOLUTION PRIVACY-AWARE MACHINE LEARNING NEXT-GENERATION EDGE-CLOUD ECOSYSTEM
CONTENTS ✱ #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 5 08 PRIVACY-AWARE MACHINE LEARNING WITH LOW NETWORK FOOTPRINT Federated learning makes it possible to train machine learning models without transferring potentially sensitive user data from devices or local deployments to a central server. As such, it addresses security and privacy concerns at the same time that it improves functionality and performance. 16 5G NEW RADIO RAN AND TRANSPORT CHOICES THAT MINIMIZE TCO By deploying self-built transport in the RAN area instead of using leased lines, mobile network operators gain access to the full range of 5G New Radio RAN architecture options and minimize their total cost of ownership (TCO). 26 CREATING THE NEXT-GENERATION EDGE-CLOUD ECOSYSTEM Edge computing has great potential to help communication service providers improve content delivery, enable extreme low-latency use cases and meet stringent legal requirements on data security and privacy. 36 ENHANCING RAN PERFORMANCE WITH ARTIFICIAL INTELLIGENCE Artificial intelligence has a key role to play in helping operators achieve a high degree of automation, increase network performance and shorten time to market for new features. Our research shows that graph-based frameworks for both network design and network optimization can generate considerable benefits. 48 5G MIGRATION STRATEGY: FROM EPS TO 5G SYSTEM The necessary migration from existing Evolved Packet System (EPS) deployments to combined 4G-5G networks that provide seamless voice and data services requires a carefully tailored, holistic strategy that includes all network domains and considers each operator’s specific needs per domain. 58 5G NEW RADIO EVOLUTION The enhancements in the 3GPP releases 16 and 17 of 5G New Radio include both extensions to existing features as well as features that address new verticals and deployment scenarios. Operation in unlicensed spectrum, intelligent transportation systems, Industrial Internet of Things, and non-terrestrial networks are just a few of the highlights. 16 Training (global) Training Inference Data lake (global) Pipelines Data Model distribution Aggregated weights Ericsson Customer Local deployment 1 Training Inference Local deployment 2 Training Inference Local deployment 3 Local storage Local storage Local storage 08 Configuration data Data processing Diagnostics Network Optimization Performance data Cell trace data Extract - transform - load Identification and classification Accessibility and load issues Mobility issues Coverage issues Interference issues Root-cause analytics and insights Accessibility and load Mobility Coverage Interference Recommendations and actions Accessibility and load Mobility Coverage Interference 36 48 CU DU MT DU MT DU F1 Donor node IAB node Backhaul based on IAB Access link Donor node IAB node IAB Conventional backhaul Access link Backhaul based on IAB IAB node F1 58 Application execution enviro Third-party edge application e.g. image recognition, rendering Devices 5G radio access Edge data Distributed cloud infrastruc Connectivity infrastructure 26
EDITORIAL ✱ #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 7 ✱ EDITORIAL ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 Ericsson Technology Review brings you insights into some of the key emerging innovations that are shaping the future of ICT. Our aim is to encourage an open discussion about the potential, practicalities, and benefits of a wide range of technical developments, and provide insight into what the future has to offer. a d d r e s s Ericsson SE -164 83 Stockholm, Sweden Phone: +46 8 719 00 00 p u b l i s h i n g All material and articles are published on the Ericsson Technology Review website: www.ericsson.com/ericsson-technology-review p u b l i s h e r Erik Ekudden e d i t o r s Tanis Bestland, lead editor (Nordic Morning) tanis.bestland@nordicmorning.com e d i t o r i a l b o a r d Håkan Andersson, Anders Rosengren, Mats Norin, Magnus Buhrgard, Gunnar Thrysin, Håkan Olofsson, Dan Fahrman, Robert Skog, Patrik Roseen, Jonas Högberg, John Fornehed, Kjell Gustafsson, Jan Hägglund, Per Willars and Sara Kullman a r t d i r e c t o r Liselotte Stjernberg (Nordic Morning) p r o j e c t m a n a g e r Susanna O’Grady (Nordic Morning) l ay o u t Liselotte Stjernberg (Nordic Morning) i l l u s t r at i o n s Jenny Andersén (Nordic Morning) s u b e d i t o r s Ian Nicholson (Nordic Morning) Paul Eade (Nordic Morning) i s s n : 0 0 1 4 - 0 17 1 Volume: 101, 2020 AUTOMATIONANDTIGHTINTEGRATION… ARECRITICALTOACHIEVINGCOST-EFFICIENT DEPLOYMENTS ERIK EKUDDEN SENIOR VICE PRESIDENT, CHIEF TECHNOLOGY OFFICER AND HEAD OF GROUP FUNCTION TECHNOLOGY ■ mobile data traffic volumes are expected to increase by a factor of four by 2025, and 45 percent of that traffic will be carried by 5G networks. To deliver on customer expectations in this rapidly changing environment, communication service providers (CSPs) must overcome challenges in three key areas: building sufficient capacity, resolving operational inefficiencies through automation and artificial intelligence (AI), and improving service differentiation. Fortunately, the contents of this issue of ETR magazine provide insights about how to tackle all three. For many operators, the introduction of the 5G System (5GS) to provide wide-area services in existing Evolved Packet System (EPS) deployments isacriticalsteptowardcreatingafull-service,future- proof 5GS in the longer term. Our article on the topic provides an overview of all the aspects that operators need to consider when putting together a robust EPS-to-5GS migration strategy and offers guidance on how to adapt the transition to address a CSP’s specific needs per domain. To cope with the large increase in required bit rate per site and achieve a cost-efficient rollout of 5G New Radio (NR), CSPs also need a good understanding of the different RAN architecture and transport network alternatives available to them. In this issue, we present all the available options and explain why automation and tight integration between the RAN and the transport network are critical to achieving cost- efficient deployments. The surge in data volume that will come from the massive number of devices enabled by 5G has made edge computing more important than ever before. Beyond its abilities to reduce ADDRESSING 5G CHALLENGES TOGETHER network traffic and improve user experience, edge computing will also play a critical role in enabling use cases for ultra-reliable low-latency communication in industrial manufacturing and a variety of other sectors. Our article on the topic explores how to deliver distributed edge computing solutions that can host different kinds of platforms and applications and provide a high level of flexibility for application developers. The integration of AI into current and future generations of cellular access will be critical to achieving Ericsson’s vision of creating a cellular network that constantly adapts itself both to customer requirements and to the static and dynamic characteristics of different scenarios. The AI article in this issue explains how AI can be applied most effectively in three RAN performance improvement domains: network design, network optimization and RAN algorithms. This issue of the magazine also features an article about federated learning (FL) – a smarter, more resource-efficient way for CSPs to ensure consistent QoE. The article demonstrates that it is possible to migrate from a conventional machine learning model to an FL model and significantly reduce the amount of information that is exchanged between different parts of the network, thereby enhancing privacy without negatively impacting accuracy. Ericsson is deeply committed to helping CSPs and other stakeholders understand and plan for the many new 5G NR opportunities that are on the horizon. The significant enhancements to 5G NR in 3GPP releases 16 and 17 are certain to play a critical role in expanding both the availability and the applicability of 5G NR in both industry and public services. Our article on this topic analyzes the most notable new developments in these coming releases, and shares our insights about the future beyond release 17. We hope you enjoy this issue of ETR magazine and we’d be delighted if you shared it with your colleagues and business partners. You can find both PDF and HTML versions of all the articles at: www.ericsson.com/ericsson-technology-review
8 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 9 ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 2 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 3 Federated learning makes it possible to train machine learning models without transferring potentially sensitive user data from devices or local deployments to a central server. As such, it addresses privacy concerns at the same time that it improves functionality. Depending on the complexity of the neural network, it can also dramatically reduce the amount of data needed while training a model. KONSTANTINOS VANDIKAS, SELIM ICKIN, GAURAV DIXIT, MICHAEL BUISMAN, JONAS ÅKESON Reliance on artificial intelligence (AI) and automation solutions is growing rapidly in the telecom industry as network complexity continues to expand. The machine learning (ML) models that many mobile network operators (MNOs) use to predict and solve issues before they affect user QoE are just one example. ■Animportantaspectofthe5Gevolutionisthe transformationofengineerednetworksinto continuouslearningnetworksinwhichself- adapting,scalableandintelligentagentscanwork independentlytocontinuouslyimprovequalityand performance.Theseemerging“zero-touch networks”are,andwillcontinuetobe,heavily dependentonMLmodels. Thereal-worldperformanceofanyMLmodel dependsontherelevanceofthedatausedtotrainit. ConventionalMLmodelsdependonthemass transferofdatafromthedevicesordeploymentsites toacentralservertocreatealarge,centralized dataset.Evenincaseswherethecomputationis decentralized,thetrainingofconventionalML modelsstillrequireslarge,centralizeddatasetsand missesoutonusingcomputationalresourcesthat maybeavailableclosertowheredataisgenerated. WhileconventionalMLdeliversahighlevelof accuracy,itcanbeproblematicfromadatasecurity perspective,duetolegalrestrictionsand/orprivacy concerns.Further,thetransferofsomuchdata requiressignificantnetworkresources,whichmeans thatlackofbandwidthanddatatransfercostscanbe anissueinsomesituations.Evenincaseswhereall therequireddataisavailable,relianceona centralizeddatasetformaintenanceandretraining purposescanbecostlyandtimeconsuming. Forbothprivacyandefficiencyreasons,Ericsson believesthatthezero-touchnetworksofthefuture mustbeabletolearnwithoutneedingtotransfer voluminousamountsofdata,performcentralized computationand/orriskexposingsensitive information.Federatedlearning(FL),withitsability todoMLinadecentralizedmanner,isapromising approach. TobetterunderstandthepotentialofFLina telecomenvironment,wehavetesteditinanumber ofusecases,migratingthemodelsfrom conventional,centralizedMLtoFL,usingthe accuracyoftheoriginalmodelasabaseline.Our researchindicatesthattheusageofasimpleneural networkyieldsasignificantreductioninnetwork utilization,duetothesharpdropintheamountof datathatneedstobeshared. Aspartofourwork,wehavealsoidentifiedthe propertiesnecessarytocreateanFLframeworkthat canachievethehighscalabilityandfaulttolerance requiredtosustainseveralFLtasksinparallel. Anotherimportantaspectofourworkinthisarea hasbeenfiguringouthowtotransferanMLmodel thataddressesaspecificandcommonproblem, pretrainedwithinanFLmechanismonexisting networknodestonewlyjoinednetworknodes,so thattheytoocanbenefitfromwhathasbeenlearned previously. Theconceptoffederatedlearning ThecoreconceptbehindFListotrainacentralized modelondecentralizeddatathatneverleavesthe localdatacenterthatgeneratedit.Ratherthan transferring“thedatatothecomputation,”FL transfers“thecomputationtothedata.”[1] Initssimplestform,anFLframeworkmakesuse ofneuralnetworks,trainedlocallyascloseas possibletowherethedataisgenerated/collected. Suchinitialmodelsaredistributedtoseveraldata sourcesandtrainedinparallel.Oncetrained,the weightsofallneuronsoftheneuralnetworkare transportedtoacentraldatacenter,wherefederated averagingtakesplaceandanewmodelisproduced andcommunicatedbacktoalltheremoteneural networksthatcontributedtoitscreation. WITH LOW NETWORK FOOTPRINT Privacy-aware machinelearning Terms and abbreviations AI – Artificial Intelligence | AUC – Area Under the Curve | FL – Federated Learning | ML – Machine Learning | MNO – Mobile Network Operator | ROC – Receiver Operating Characteristic
10 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 11 ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 4 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 5 Figure2illustratesthebasicsystemdesign. Afederationistreatedasataskrun-to-completion, enablingasingleresourcedefinitionofall parametersofthefederationthatislaterdeployedto differentcloud-nativeenvironments.Theresource definitionforthetaskdealsbothwithvariantand invariantpartsofthefederation. Thevariantpartshandlethecharacteristicsofthe FLmodelanditshyperparameters.Theinvariant partshandlethespecificsofcommoncomponents thatcanbereusedbydifferentFLtasks.Invariant partsincludeamessagequeue,themasteroftheFL taskandtheworkerstobedeployedandfederatedin differentdatacenters. Workers(processesrunninginlocaldeployments) aretightlycoupledwiththeunderlyingMLplatform thatisusedtotrainthemodel,whichisimmutable duringthefederation.InourFLexperiments,we selectedTensorFlowtotraintheneuralnetwork, whichisdesignedtobeinterchangeablewithother MLplatformssuchasPyTorch.Communication betweenthemasterandtheworkersisprotected usingTransportLayerSecurityencryptionwith one-timegeneratedpublic/privatekeysthatare discardedassoonasanFLtaskiscompleted. Invariantcomponentscanbereusedbydifferent FLtasks.FLtaskscanrunsequentiallyorinparallel dependingontheavailabilityofresources.Master andworkerprocessesareimplementedasstateless components.Thisdesignchoiceleadstoamore robustframework,sinceitallowsforanFLtaskto failwithoutaffectingotherongoingFLtasks. Faulttolerance Toreducethecomplexityofthecodebaseforboth themasteroftheFLtaskandtheworkersandto keepourimplementationstateless,wechosea messagebustobethesinglepointoffailureinthe designofourFLframework.Thisdesignchoiceis furthermotivatedbytheresearchintocreating highlyscalableandfault-tolerantmessagebusesby combiningleader-electiontechniquesand/orby relyingonpersistentstoragetomaintainthestateof themessagequeueincaseofafailure[4]. Themessageexchangebetweenthemasterofthe FLtaskandtheworkersisimplementedintheform ofassignedtaskssuchas“computenewweights”and “averageweights.”Eachtaskispushedintothe messagequeueandhasadirectrecipient.The recipientmustacknowledgethatithasreceivedthe task.Iftheacknowledgementisnotmade,thetask remainsinthequeue.Incaseofafailure,messages remaininthemessagequeuewhileKubernetes restartsthefailedprocess.Oncetheprocessreaches arunningstateagain,themessagequeue retransmitsanyunacknowledgedtasks. Techniquessuchassecureaggregation[2]and differentialprivacy[3]canbeappliedtofurther ensuretheprivacyandanonymityofthedataorigin. FLcanbeusedtotestandtrainnotonlyon smartphonesandtablets,butonalltypesofdevices. Thismakesitpossibleforself-drivingcarstotrainon aggregatedreal-worlddriverbehavior,forexample, andhospitalstoimprovediagnosticswithout breachingtheprivacyoftheirpatients. Figure1illustratesthebasicarchitectureofanFL lifecycle.Thelightbluedashedlinesindicatethat onlytheaggregatedweightsaresenttotheglobal datalake,asopposedtothelocaldataitself,asisthe caseinconventionalMLmodels.Asaresult,FL makesitpossibletoachievebetterutilizationof resources,minimizedatatransferandpreservethe privacyofthosewhoseinformationisbeing exchanged. ThemainchallengewithanFLapproachisthat thetransitionfromtrainingaconventionalML modelusingacentralizeddatasettoseveralsmaller federatedonesmayintroduceabiasthatimpactsthe accuracyoriginallyachievedbyusingacentralized dataset.Theriskforthisisgreatestinlessreliable andmoreephemeralfederationsthatspanoverto mobiledevices. Itisreasonabletoexpectdatacentersusedby MNOstobesignificantlymorereliablethandevices intermsofdatastorage,computationalresources andgeneralavailability.However,itisimportantto ensurehighfaulttolerance,ascorresponding processesmaystillfailduetolackofresources, softwarebugsorotherissues. Federatedlearningframeworkdesign OurFLframeworkdesignconceptiscloud-native, builtonafederationofKubernetes-baseddata centerslocatedindifferentpartsoftheworld. Weassumerestrictedaccesstoallowforthe executionofcertainprocessesthatarevitaltoFL. Figure 2 Basic design of an FL platform Message bus FL master FL worker 1 FL worker 2 FL worker 3 FL worker N... Figure 1 Overview of federated learning Training (global) Training Inference Data lake (global) Pipelines Data Model distribution Aggregated weights Ericsson Customer Local deployment 1 Training Inference Local deployment 2 Training Inference Local deployment 3 Local storage Local storage Local storage
12 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 13 ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 6 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 7 Preventivemaintenanceusecase HardwarefaultpredictionisatypicalMLusecase foranMNO.Inthiscase,theaimistopredict whethertherewillbeahardwarefaultataradiounit withinthenextsevendaysbasedondatagenerated intheeight-weekintervalprecedingtheprediction time.TheinputstotheMLmodelconsistofmore than500featuresthatareaggregationsofmultiple performancemanagementcounters,fault managementdatasuchasalarms,weatherdataand thedate/timesincethehardwarehasbeenactivein thefield. Threetrainingscenarios Weperformedtheexperimentsinthreescenarios– centralizedML,isolatedMLandFL. CentralizedMListhebenchmarkscenario.The datasetsfromallfourworkernodesaretransferred toonemasternode,andmodeltrainingisperformed there.Thetrainedmodelisthentransferredand deployedbacktothefourworkernodesforinference. Inthisscenario,allworkernodesuseexactlythe samepretrainedMLmodel. IntheisolatedMLscenario,nodataistransferred fromtheworkernodestoamasternode.Instead, eachworkernodetrainsonitsowndatasetand operatesindependentlyfromtheothers. IntheFLscenario,theworkernodestrainontheir individualdatasetsandsharethelearnedweights fromtheneuralnetworkmodelviathemessage queue.Thesaturationofthemodelaccuraciesis achievedafter15roundsoftheweight-sharingand weight-averagingprocedure.Inthisway,theworker nodescanlearnfromeachotherwithouttransferring theirdatasets. Thepropertiesofeachtrainingscenarioare summarizedinFigure3,TableA. Accuracyresults TableBinFigure3presentstheresultsintheformof medianROCAUC(receiveroperatingcharacteristic areaunderthecurve)scoresobtainedthroughmore than100independentexperiments.Thescores achievedintheFLscenarioaresimilartothose achievedinthecentralizedandisolatedones,while thevarianceoftheFLscoresissignificantlylower comparedwiththeothertwoscenarios. TheresultsinTableBshowthatitisworker1 (south)thatbenefitsfromFL.Theyalsosuggestthat anisolatedMLapproachcanberecommendedin caseswheretheindividualdatasetshaveenough datafortraining.Theonlydrawbackisthatbecause theisolatednodesneverreceiveanyinformation fromothernodes,theywillbemoreconservativein theirresponsetochangesinthedata,withtheriskof potentiallyhigherblindspotsintheindividual datasets. Theimpactofaddingnewworkers Tofacilitatetheaddingofnewworkersatalatertime, informationaboutthecurrentroundmustbe maintainedinthemessageexchangebetweenthe masterandtheworkers.WhenanFLtaskstarts,all workersregistertoroundID0,whichtriggersthe mastertoinitializetherandomweightsand broadcastthesamedistributiontoallworkers.All workerstraininparallelandcontributetothesame traininground.Astheroundsincrease,thefederated model’smaturityincreasesuntilasaturationpointis reached. IfthecurrentroundIDisgreaterthan0,themaster isawarethattheprocessofaveragingofweights hastakenplaceatleastonce,whichmeansthatthe modelisnotatarandominitialstate.Whenanew workerjoinstheFLtask,itsendsitsroundIDas0. Figure 3 Tables relating to the hardware fault prediction use case Centralized Isolated Federated Centralized median (std) Isolated median (std) Federated median (std) Downlink consumption Uplink consumption NoPrivacy preserved Use of overall data Data transfer cost Weight transfer cost Yes Yes 0.91 (0.15)Worker 1 (region 1) 0.89 (0.12) 0.95 (0.05) 0.92 (0.8)Worker 2 (region 2) 0.93 (0.08) 0.93 (0.03) 0.95 (0.16)Worker 3 (region 3) 0.95 (0.13) 0.97 (0.07) 0.97 (0.13)Worker 4 (region 4) 0.97 (0.11) 0.96 (0.05) 0.93 (0.13)Overall 0.93 (0.11) 0.95 (0.05) Federated (MB)Centralized (MB) Table D – Network footprint Table C – Network footprint formulas for each training scenario Table B – ROC AUC scores of workers throughout three scenarios Table A – Summary of scenario definitions FL message size (MB) Rounds Rounds Master 0 0 Worker ID 0 0 Master N * R * Model₀ N * R * Model₀ Worker ID R * Model₀ R * Model₀ i: worker ID N: number of workers R: number of rounds needed until accuracy convergence Model₀: Size of ML model ni : size of dataset in worker ID Worker ID Model₀ ni Master Centralized ML Isolated ML FL ∑ N * Model₀ N i=0 ni Yes No Yes High None None None None Low Workers 19.22,000 0.26 15 4
14 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 ✱ PRIVACY-AWARE MACHINE LEARNING 10 ERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 Konstantinos Vandikas ◆ is a principal researcher at Ericsson Research whose work focuses on the intersection between distributed systems and AI. His background is in distributed systems and service-oriented architectures. He has been with Ericsson Research since 2007, actively evolving research concepts from inception to commercialization. Vandikas has 23 granted patents and over 60 patent applications. He has authored or coauthored more than 20 scientific publications and has participated in technical committees at several conferences in the areas of cloud computing, the Internet of Things and AI. He holds a Ph.D. in computer science from RWTH Aachen University, Germany. Selim Ickin ◆ joined Ericsson Research in 2014 and is currently a senior researcher in the AI department in Sweden. His work has been mostly around building ML prototypes in diverse domains such as to improve network-based video streaming performance, to reduce subscriber churn rate for a video service provider and to reduce network operation cost. He holds a B.Sc. in electrical and electronics engineering from Bilkent University in Ankara, Turkey, as well as an M.Sc. and a Ph.D. in computing from Blekinge Institute of Technology in Sweden. He has authored or coauthored more than 20 publications since 2010. He also has patents in the area of ML within the scope of radio network applications. Gaurav Dixit ◆ joined Ericsson in 2012. He currently heads the Automation and AI Development function for Business Area Managed Services. In earlier roles he was a member of the team that set up the cloud product business within Ericsson. He holds an MBA from the Indian Institute of Management in Lucknow, India, and the Università Bocconi in Milan, Italy, as well as a B.Tech. in electronics and communication engineering from the National Institute of Technology in Jalandhar, India. Michael Buisman ◆ is a strategic systems director at Business Area Managed Services whose work focuses on ML and AI. He joined Ericsson in 2007 and has more than 20 years of experience of delivering new innovations in the telecom industry that drive the transition to a digital world. For the past two years, Buisman and his team have been developing a managed services ML/AI solution that is now being deployed to several customers globally. Buisman holds a BA from the University of Portsmouth in the UK and an MBA from St. Joseph’s University in Philadelphia in the US. Jonas Åkeson ◆ joined Ericsson in 2005. In his current role, he drives the implementation of AI and automation in the three areas that integrate Ericsson’s Managed Services business. He holds an M.Sc. in engineering from Linköping Institute of Technology, Sweden, and a higher education diploma in business economics from Stockholm University, Sweden. theauthors
16 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 17 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 2 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 3 By deploying self-built transport in the RAN area instead of using leased lines, mobile network operators gain access to the full range of 5G New Radio RAN architecture options and minimize their total cost of ownership (TCO). ANN-CHRISTINE ERIKSSON, MATS FORSMAN, HENRIK RONKAINEN, PER WILLARS, CHRISTER ÖSTBERG The 5G evolution is well underway – leading mobile network operators (MNOs) in several regions of the world have already launched the first commercial 5G NR networks, and large-scale deployments are expected in the years ahead. The use of self-built transport in denser areas with a suitable RAN architecture will play a key role in ensuring cost-efficiency. ■Acost-efficient5GNRdeploymentrequires MNOstotakeseveralfactorsintoconsideration. Mostobviously,theyneedtomakesurethatthe5G NRdeploymentcomplementstheirexisting4GLTE networkandmakesuseofbothcurrent4GLTEand new5GNRspectrumassets.Beyondthat,itisvital toconsiderthevariousRANarchitectureoptions availableandthewaysinwhichthetransport networkneedstoevolvetosupportthem,alongwith thelargeincreaseinuserdataratespersite. Whileurbanareaswithhighuserdensitywillbe thefirstpriorityfor5GNRdeployments,suburban andruralareaswillnotbefarbehind.Thesethree areatypeshavedifferentpreconditionssuchas availabletransportsolutions,inter-sitedistance (ISD),trafficdemandandspectrumneedsthatmust betakenintoconsiderationatanearlystageinthe deploymentprocess. Predicted5Gtraffic 5Gisprojectedtoreach40percentpopulation coverageand1.9billionsubscriptionsby2024[1], correspondingto20percentofallmobile subscriptions.Thosefiguresindicatethatitwillbe thefastestglobalrolloutsofar.Thetotalmobiledata trafficgeneratedbysmartphonesiscurrentlyabout 90percentandisestimatedtoreach95percentby theendof2024.Withthecontinuedgrowthof smartphoneusage,totalworldwidemobiledata trafficispredictedtoreachabout130exabytesper month–fourtimeshigherthanthecorresponding figurefor2019–and35percentofthistrafficwillbe carriedby5GNRnetworks. Thegrowingdatademandsformobilebroadband cangenerallybemetwithlimitedsitedensification [2].Therearebenefitstodeploying5GNRmid- bands(3-6GHz)atexisting4Gsites,resultingina significantperformanceboostandmaximalreuseof siteinfrastructureinvestments.Bymeansofmassive MIMO(multiple-input,multiple-output) techniques,suchasbeamformingandmulti-user MIMO,higherdownlinkcapacitycanbeachieved alongwithimproveddownlinkdatarates–both outdoorsandindoors. Deepindoorcoverageismaintainedthrough interworkingwithLTEand/orNRonlowbands usingdualconnectivityorcarrieraggregation. Furtherspeedandcapacityincreasescanbe attainedbydeploying5GNRathighbands (26-40GHz),alsoknownasmmWave.Ifadditional spectrumdoesnotsatisfythetrafficdemand (dueto,forexample,theintroductionoffixed wirelessaccess)densificationwithsolutions suchasstreetsitesmayberequired. Increasinguserdataratesperantennasite Theintroductionofnewspectrumfor5GNRwill increasethecarrierbandwidthsfromthe5MHz, 10MHzand20MHzusedforLTEto50MHzand 100MHzforthemidbands(3-6GHz)and 400/800MHzforthehighbands(24-40GHz), allowingforgigabit-per-seconddataratesperuser equipment(UE).Inurbanareas,thetotalamount ofspectrumwillgrowfromafewtensorhundreds ofmegahertztoseveralhundredorthousand megahertzperantennasite. Simultaneously,trafficdemandspersubscriber willincreaseexponentially.Allinall,thisimpliesthat thebitratedemandsinthebackhaulandfronthaul transportnetworkwillincreasesignificantly(per antennasite,forexample).Thebitratedemandwill bemultiplegigabitspersecond,comparedwiththe fewhundredmegabitspersecondincurrentmobile networks. Thespectrumincreaseperantennasitewillbe lessinsuburbanareas,whileinruralareasrefarming ofcurrentspectrumorspectrumsharingbetween LTEandNRwillbemorecommon.RANtransport networkswillneedtoevolvetoaddresstheincrease inaccumulateduserdatarates,particularlyinurban areas,andinmanysuburbanonesaswell. Transportnetworkoptions EvolvingthetransportnetworkinthelocalRAN areaisanimportantfirststepwhendeploying5G ontopofLTE. Inmostcases,themobilebackhaultransportfor DistributedRAN(DRAN)–thearchitecture traditionallyusedtobuildmobilenetworks–has beenarentedpacket-forwardingservice,Ethernet orIPbased,typicallycalledaleasedlineand providedbytraditionalfixednetworkoperators. Anotheroptioniswhitefiber,anopticalwavelength serviceofferedbymanytraditionalfixednetwork operators. Insteadofleasingatransportservice,some mobileoperatorsdeployself-builttransport solutionsusingmicrowavelinks,whichusually enablesshortinstallationleadtime.Integrated AccessandBackhaul(IAB)isanotheroptionfor self-builttransportin5G.WithIAB,themobile spectrumisalsousedforbackhaul,whichis especiallyrelevantforhigh-frequencybandswhere thebandwidthmaybehundredsofmegahertz. Alternatively,itispossibleforamobileoperatorto deployaself-builttransportsolutionontopof physicalfiber(knownasdarkfiber)thatisavailable forrentfromfixednetworkoperators,ormore recentlyfrompurefibernetworkoperatorsand municipalnetworks.Themobileoperatorthen buildsandownsthetransportequipmentina RANarea,definedasthelocalurbanareainacity andthesuburbanareasclosetocities. Urbanareastendtohavemultiplefibernetwork operatorsthatdeployfibertoeverystreet,which meansthatdarkfiberisreadilyavailableforrent. Whiledarkfiberislesscommoninsuburbanareas, CHOICES THAT MINIMIZE TCO 5GNewRadio RAN&transport TRAFFICDEMANDSPER SUBSCRIBERWILLINCREASE EXPONENTIALLY
18 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 19 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 4 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 5 infrastructureinvestments.Thebackhaul–thatis, thetransportbetweentheRANandthecore network(CN)–usesanS1/NGinterface[3]. DRANiswellsuitedforuseinallareas(urban, suburbanandrural)andcanusealargevarietyof transportsolutions.DRANreuseslegacy infrastructureinvestments,suchasexistingsites andoperationsandmaintenancestructure,andis ofparticularvalueinareaswherethepopulation densityislowandtheusersarescattered. Theutilizationofstatisticalvariationsintraffic forthedimensioningofself-builtpackettransport intheRANareatransportnetworkisanother benefitofDRAN. Wheredensificationisneededforcoverageor capacity,DRANstreetsitesfitwelltogetherwiththe existingDRANmacrosites.SpecificDRANunits tailoredforstreetsites,denotedasRBUinFigure1, havebenefitssuchasintegratedbasebandfunctions, simpleinstallationandreducedstreetsitespace. CentralizedRAN CentralizedRAN(CRAN)ischaracterizedby centralizedbasebandformultiplepiecesofradio equipment.WithaCRANdeployment,the basebandunitslocatedinacentralsiteandtheradio equipmentlocatedattheantennasitesare interconnectedwithatransportnetwork denominatedfronthaul,eitherCommonPublic RadioInterface(CPRI)orevolvedCPRI(eCPRI)[4]. InareaswithsmallISDsandaccesstodarkfiber (urbanandinsomecasesdensesuburbanareas), centralizingandpoolingthebasebandunitstoan aggregationsitecanbeagoodoption.Theuseof CRANcanleadtoreducedcostsforsitespaceand energyconsumptionattheantennasites,aswellas easierinstallation,operationandmaintenance. CRANprovidesefficientcoordination(via interbandcarrieraggregationandCoMP– coordinatedmultipoint–forexample)between physicallyseparatedantennasites.Italsoenables dimensioningofabasebandpooltohandlemoreand largerantennasitesduetostatisticalvariationsof trafficpersite,whichalsomakesbasebandresource expansioneasierwhentrafficgrowsintheCRAN area.Resilienceandenergyefficiencyareother benefits,asthebasebandpoolservesmanyantenna sites.Thestatisticalvariationoftrafficpersitemay alsobeutilizedinRANareatransportnetwork dimensioning. InenvironmentswhereCRANisdeployed,adark fibertransportsolutionisrequiredforthefronthaul. Theconnectedradiositesalsoneedtobewithinthe latencylimitrequiredbythebasebandunits.Theuse ofdarkfiberisagoodfitwiththenewwideNR frequencybandsandtheexpansionofthefronthaul duetotheuseofadvancedantennasystems[5]. WhendeployingCRAN,itismostbeneficialto connectsitesinthesameareatothesamebaseband pool.Incaseswhereitisdifficulttodeployadark fibertransportsolution,eitheraDRANorahigh- layersplitvirtualizedRAN(HLS-VRAN) architecturemaybedeployedforthosesites, coexistingwithotherCRAN-connectednodes. Toachievethebenefitsofstatisticalmultiplexing oftrafficto/fromtheradioequipmentinthe transportnetworkandinthebasebandpool,itis necessarytouseanEthernet-basedfronthaulsuch aseCPRI[4].Theradioequipmentattheantenna sitesmayeitherhavesupportforeCPRIorinclude aconverterfromCPRItoeCPRI.Itisalsopossible tomixeCPRIandCPRIradioequipment,usingan opticalfronthaultransportsolution,butwithout transportmultiplexinggains. CRANrequiressuitablesites(suchascentral officesites)tocolocatethebasebandunits.Thesize anddensityofthesecentralofficesitesdependson eachsituation,butatypicalcasecouldbecentral officesiteswithanISDoflessthan1kmuptoafew kilometersinanarea. Higher-layersplitappliedasavirtualizedRAN deployment ForbothDRANandCRAN,itispossibletoadda VRANbyimplementinganHLSwherethegNB itsavailabilityissteadilyincreasing.Inruralareas, thereisoftenonlyonefiberoperator,andfiberis onlydeployedtospecificsitessuchasbusinesses andschools.Inthesecases,darkfiberisusuallynot providedasaservice. Ontopofdarkfiber,mobileoperatorscandeploy anoptical(passiveoractive)orapacket-forwarding solution.Thepassiveopticalsolutionusescolored smallform-factorpluggabletransceivers(SFPs)in theendpointsandopticalfiltersinbetweenforadd/ droptosubtendedsites/equipmentalongthefiber path.AnactiveopticalsystemusesgraySFPsinthe endpointsandactiveopticalswitchingequipmentto generatewavelengthsandperformopticalswitching onthesites/equipmentonthefiber.Thepacket- forwardingsolutioncanbeanEthernetorIP solutionwithpacket-forwardingcapabilities onallsites/equipmentalongthefiberpath. RANarchitectureoptions Figure1illustratesDRANalongwiththeother RANarchitectureoptionsavailableforusein5G NR.Theoptionthatismostappropriatefora particulardeploymentwilllargelydependonthe typeofdeploymentarea(urban,suburbanorrural) andtheavailabilityofdarkfiber. Inalloptions,outdoorsitedeploymentscanbe eithermacrosites(typicallymountedonrooftopsor antennamastscoveringalargerarea)orstreetsites (typicallymountedonpoles,wallsorstrands coveringsmallerareasorspots). TheflexibilityoflocatingRANfunctionalityin differentlocationsin5GNRRANarchitectureand theabilitytosupportmoreradiositesincreasesthe needfornetworkautomation,makingitnecessaryto simplifytheinstallation,deploymentandoperation ofboththeRANandtransportpieces.Forexample, theautomationcapabilitiesusedtosimplify installationintheRANmustalsobeintroducedinto transporttoimprovetheinteractionbetweenthetwo. DistributedRAN DRANwithunitaryeNodeBbasestationshasbeen thedominantarchitecturefor4GLTE.DRANwill alsobeacommonlyusedarchitecturein5GNR deployments,withthebenefitofreusingthelegacy Figure 1 RAN architecture deployment options CU DU gNB Antenna/ hub site CU DU RBU Macro site Street siteHLS-RBU HLS-gNB Backhaul Fronthaul CPRI/eCPRI Fronthaul CPRI/eCPRI Backhaul S1/NG Backhaul F1Backhaul S1/NG Backhaul S1/NG Core network Centralized RAN Distributed RAN Distributed RAN + Virtualized RAN (HLS) Centralized RAN + Virtualized RAN (HLS) Small/ street site CU DU Central office site DU Central office site DU Small/ street site CU Data center DU HLS-gNB Antenna/ hub site DU HLS-RBU Small/ street site Backhaul F1 THEUSEOFDARKFIBERIS AGOODFITWITHTHENEWWIDE NRFREQUENCYBANDS...
20 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 21 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 6 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 7 isdividedintoacentralunit(CU)anddistributed units(DUs).ThisisknownasHLS-VRAN. TheDUsandtheCUareseparatedbytheF1 interface,carriedonabackhaultransportnetwork. ThesearedenotedHLS-gNBformacroand HLS-RBUforstreetsitesinFigure1. Whenacloudinfrastructurealreadyexistsin thenetwork,theHLS-VRANdeploymentmaybe beneficialfromanoperationalandmanagement pointofview.ForaDRANdeployment,adding HLS-VRANcouldresultindualconnectivitygains ifitisexpectedthatitwillbecommonforUEstobe connectedtodifferentbasebandsites. Inareaswhereastreetsitedeploymentisneeded asacoverageorcapacitycomplementtothemacro sitedeployment,astreetHLS-VRANdeployment fitswellwithmacroHLS-VRAN.SpecificHLS- VRANunitstailoredforstreetsites,denotedas HLS-RBUinFigure1,havethesamebenefits astheRBU. 5GNewRadiototalcostofownership Amobileoperator’sTCOfor5GNRintroductionin aRANareaincludesbothcapitalexpenses(one- timecosts)andoperatingexpenses(recurringcosts). Typicalcapitalexpensesincluderadio/RANand transportequipment,siteconstruction,installation costsandsiteacquisition.Typicaloperating expensesincludecostsforaleasedline,darkfiber rental,spectrumforwirelesstransport,siterental, energyconsumption,operationandmaintenance costsandvendorsupport.SincetheRANareatype anddeploymentsolutionalternativesaffecttheTCO, itisusefultocomparetheTCOofthedeployment solutionalternativesindifferentRANareas. BasedonEricssoncustomerpriceinformation andinternalanalysis,Figure2presentstherelative operatorTCOcoveringallcapitalexpensesand operatingexpensesforanurbanlocalRANareaina high-costmarket.Differentregionsandcustomers havevariationsincoststructure.Localdeviations canbesignificant,leadingtoreduceddifferencesbut withthesamerelationintherelativecoststructures. Thelargestcostcomponentsaretransportrentcost, siterental,energyconsumptionandradio/RAN equipment.Thegraphindicatesthatusingself-built transportinthelocalRANareaisamuchmorecost- efficientapproachthanusingaleasedlinetoevery site,bothinDRANandCRANarchitectures.The costdifferenceisespeciallylargeinhigh-costmarkets. Thereasonforthisisthattheintroductionof5G NRsignificantlyincreasestheradiobandwidth comparedwithpreviousgenerations,whichresults inincreasedtransportbitratedemands.While typicaltransportbandwidthtoaradiositeranged from10sto100sofMbpsin2G-4G,itistypicallyup tomultiplegigabitspersecondin5G.Inthelower rangeofthebandwidthscale,thetraditionalleased linecosthasbeenmanageable.Butatsiteswherethe requiredtransportbitratereachesgigabits-per- secondrates,therelativecostfortheleasedline increasesdramatically,accountingforasmuchas 70-80percentoftheRANareaTCO. Thesecondlargestcostinthe“DRANwithleased linetoeverysite”example(andthelargestinthe othertwoexamples)issiterental.Somescenarios willrequiredensificationwithnewsites,whichcould beamixofbothmacrositesandsmallersitetypes (streetsites).However,networkdensificationislikely tofacechallengesduetothehighcostofsiterental andlimitedsiteavailability. Thereare,however,ongoingdiscussionsin severalregionsaboutregulatingthehighsiterental feeforantennasites,whichwouldsignificantly increasetheopportunitytodensifywithnewsites. Thecleartrendoftowercompaniestakingoverthe operationofphysicalsitesandofferingsitesharing mayalsodecreasesiterentcost. RANequipmentandenergyrankasthethirdand fourthlargestcostsinallthreeexamples.Thesecost componentsaredependentonthedeployedRAN architecture.Duetodifferentpricesindifferent marketsandareas,DRANismorecost-efficientin somecases,whileCRANisinothers.Thisexplains whythechoicemaydifferbetweenMNOs. Leasedlineversusdarkfiber Leasedlineisahighvaluetypeofserviceandthefee increaseswiththerequiredbitrate,makingitabig challengefor5GRAN,astheneededtransport bitratesaremuchhigherthaninprevious generations.Whitefiberhasbasicallythesamecost challengesasleasedlines,becauseitisaservicewith aServiceLevelAgreement. Figure 2 Relative operator TCO for 5G NR introduction in an urban local RAN area DRAN leased line to every site DRAN self-built transport in local RAN area Leased line cost Dark fiber rent Site rent RAN equipment Energy All other TCO costs CRAN self-built transport in local RAN area Terms and abbreviations CN – Core Network | CO – Central Office | CPRI – Common Public Radio Interface | CRAN – Centralized RAN | CU – Central Unit | DRAN – Distributed RAN | DU – Distributed Unit | eCPRI – Evolved CPRI | F1 – Interface CU – DU | gNB – GNodeB | HLS – Higher-Layer Split | IAB – Integrated Access and Backhaul | ISD – Inter-Site Distance | LoS – Line-of-Sight | MNO - Mobile Network Operator | NG – Interface gNB - CN | NR – New Radio | RBU – Radio Base Unit | S1–InterfaceeNB-CN| SFP – Small Form-factor Pluggable Transceiver | TCO – Total Cost of Ownership | UE – User Equipment | VRAN – Virtualized RAN USINGSELF-BUILT TRANSPORTINTHELOCAL RANAREAISAMUCHMORE COST-EFFICIENTAPPROACH
22 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 23 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 8 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 9 Darkfiberrentalalsohasaratherhighcost structure,butthetransportfeeisindependentof bitratesandinsteadbasedonthefiberdistance. DarkfibersolutionsthereforefitwellinRANareas withshortdistancesandarepreferablydeployed,so thatthesamefibercanbeshared,tosomeextent,by multiplesites.Figure3illustratesthedifference betweenatraditionalleased-lineapproachandself- builttransportbasedondarkfiber.Figure4shows whichofthesetwotransportsolutionsismostcost- efficientdependingondataratetositeandsitedistance. Aself-builttransportnetworkbasedondarkfiber maybedeployedwithdifferentfiberandradiosite structuressuchasstar,subtendorringtopology.The mostcost-efficienttopologyissubtending,where multiplesitessharefiber.Ifnetworkresiliencyis required,aringtopologyissuitableattheexpenseof greaterfiberlength.Apurestartopologygives maximumresiliencebuthasthegreatestfiberlength andisthereforethemostexpensivechoice. Figure4illustratesthetypicalfiberlengthpersite, wheretheshortestlengthsappearinurbanareas usingthesubtendingtopology,andthelongest distancesinsuburbanareasusingthestartopology. Figure4alsoshowsthetypicaluserdataratesfor5G. Darkfiberismorecost-efficientthanleasedlinesin denserareaswherethefiberlengthpersiteislow, andthedataratesarehigh.Ifthefiberlength becomeslonger,orthedataratesaresmaller,leased linesaremorecost-efficient. Forthedifferenttechnologyoptionsontopofdark fiber,thepassiveopticalsolutionisthemostcost- efficientself-builtopticalsolution.Thisassumesthat thenumberofsitesandequipmentsubtendedonthe fiberiswithinthescalingofwavelengthsinthe system. Thealternativeself-builtpacket-basedsolution hastheadvantagesofstatisticalmultiplexing throughoutthenetworkandcanbeanL2Ethernet switchedand/orL3IProutedsolution.Itassumes thatallradioequipmentsupportsapacket- forwardinginterface. Alternatively,whendarkfiberisnotavailableor toocostly,wirelesstransportsuchasIABor microwavelinksmaybeused.Theserequireline- of-sight(LoS)ornear-LoS. Conclusion Ouranalysisindicatesthatduetothelargeincrease inrequiredbitratepersitefor5GNR,theuseof traditionalleasedlinesastransporttoeveryradio/ antennasiteintheRANwillbeassociatedwitha highcostindenserareas.Self-builttransportinthe RANareaisasignificantlymorecost-efficient alternativeformobileoperators.Darkfiberis oneself-builttransportalternative;microwave linksisanother. Sincedarkfibercostscaleswithdistancerather thanbandwidth,andthetrendwith5Gistoward shortersite-to-sitedistancesandhigherbitrates, darkfiberwillbesignificantlymorecost-efficient thanleasedlinesinmanyscenarios.Further,the largenumberoffiberprovidershasboosted availabilityandcompetition,resultinginadecrease infiberrentalcostinmosturbanareas,aswellasin somesuburbanones.BeyondtheRANareawhere thelocaltrafficisaggregatedandself-builttransport isterminated,traditionalleasedlineservicestothe mobilecorecontinuetobeareasonablesolution. DistributedRAN(DRAN),whichworkswell overbothfiberandwirelesstransportsolutions, willcontinuetobethedominantdeployment architectureinmostsituations.CentralizedRAN (CRAN)isaninterestingdeploymentarchitecture forregionsorhigh-trafficareaswheredarkfiber transportisavailable.CRANoffersoperational Figure 4 Relative costs for leased lines and dark fiber Dark fiber most cost-efficient Date rate to site (Gbps) 10 5 10 5 1 Typical fiber length per urban/suburban site Typical 5G user data rates Equal TCO Fiber length (km) Leased line most cost-efficient Figure 3 Traditional leased-line approach versus self-built transport in local RAN area Leased-line sites to CN Self-built to aggregation site, leased lines to CN Local RAN area A few hundred meters -> a few kilometers CN CN Agg/ CO
24 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 25 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 10 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 11 Ann-Christine Eriksson ◆ is a senior specialist in RAN and service layer interaction at Business Area Networks. She joined Ericsson in 1988 and has worked with research and development within RAN of the 2G, 3G, 4G and 5G mobile network generations. Her focus areas include QoS, radio resource handling and RAN architecture. In her current role, she focuses on evaluating different 5G RAN architecture deployment options with the goal of optimizing RAN efficiency, performance and cost. Eriksson holds an M.Sc. in physical engineering from KTH Royal Institute of Technology in Stockholm, Sweden. Mats Forsman ◆ joined Ericsson in 1999 to work with intelligent networks. Since then he has worked within the areas of IP, broadband and optical networks. Today, his focus is on new concepts for transport within mobile networks at Business Area Networks; one such concept area is 5G RAN transport and automation. Forsman holds an M.Sc. in mathematics and natural science from Umeå University, Sweden. Henrik Ronkainen ◆ joined Ericsson in 1989 to work with software development in telecom control systems but soon followed the journey of mobile systems evolution as a software and system architect for the 2G and 3G RAN systems. With the introduction of HSDPA, he worked as a system architect for 3G and 4G UE modems but rejoined Business Area Networks in late 2014, focusing on analysis and solutions for the architecture, deployment and functionality targeted for the 5G RAN. Ronkainen holds a B.Sc. in electrical engineering from the Faculty of Engineering at Lund University, Sweden. Per Willars ◆ is an expert in network architecture and radio network functionality at Business Area Networks. He joined Ericsson in 1991 and has worked intensively with RAN issues ever since. This includes leading the definition of 3G RAN, before and within the 3GPP, and more lately indoor solutions. He has also worked with service layer research and explored new business models. In his current role, he analyzes the requirements for 5G RAN (architecture and functionality) with the aim of simplifying 5G. Willars holds an M.Sc. in electrical engineering from KTH Royal Institute of Technology. Christer Östberg ◆ is an expert in the physical layer of radio access at Business Area Networks. He joined Ericsson in 1997 with a 10-year background in developing 2G prototypes and playing an instrumental role during the preassessment of 3G. At Ericsson, Östberg began with algorithm development and continued as a system architect, responsible for modem parts of 3G and 4G UE platforms. He joined Business Area Networks in 2014, focusing on analysis and solutions for the architecture, deployment and functionality targeted for the 5G RAN. Östberg holds an M.Sc. in electrical engineering from the Faculty of Engineering at Lund University. theauthors benefitsbypoolingallbasebandtoacentralsite, whichresultsinpotentialcostsavingsinsiterental andenergy,andmaximizestheopportunityfor inter-sitecoordinationfeatures.Incaseswherea networkhasanexistingcloudinfrastructure,the operatormaybenefitfromaddingahigh-layersplit virtualizedRANdeploymenttoaDRANorCRAN architecture. Becausetheflexibilityofthe5GNRarchitecture enablesmuchgreaterdistributionofequipmentand sitesthaneverbefore,itisnecessarytosimplifythe installation,deploymentandoperationofboththe RANanditstransport.Ahighdegreeofautomation andtightintegrationbetweenthetwowillbecritical toachievingcost-efficientdeployments. Further reading ❭ Learn more about building 5G networks at: https://www.ericsson.com/en/5g/5g-networks References 1. Ericsson Mobility Report, June 2019, available at: https://www.ericsson.com/en/mobility-report/reports/ june-2019 2. Ericsson Technology Review, The advantages of combining 5G NR with LTE, November 5, 2018, Kronestedt, F, et al., available at: https://www.ericsson.com/en/ericsson-technology-review/archive/2018/ the-advantages-of-combining-5g-nr-with-lte 3. 3GPP, TS Group RAN; NR; Overall Description; Stage 2, available at: https://portal.3gpp.org/ desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3191 4. CPRI Common Public Radio Interface, available at: http://cpri.info/index.html 5. Ericsson white paper, Advanced antenna systems for 5G networks, available at: https://www.ericsson. com/en/white-papers/advanced-antenna-systems-for-5g-networks ...AUTOMATIONANDTIGHT INTEGRATIONWILLBECRITICAL TOACHIEVINGCOST-EFFICIENT DEPLOYMENTS
26 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 27 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 2 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 3 Edge computing has great potential to help communication service providers improve content delivery, enable extreme low-latency use cases and meet stringent legal requirements on data security and privacy. To succeed, they need to deliver solutions that can host different kinds of platforms and provide a high level of flexibility for application developers. PÉTER SUSKOVICS, BENEDEK KOVÁCS, STEPHEN TERRILL, PETER WÖRNDLE As well-established, trusted partners that already provide device connectivity, mobility support, privacy, security and reliability, the telecommunications industry and communication service providers (CSPs) more broadly have a competitive advantage in edge computing. This advantage is compounded by their ability to reach out globally to all edge sites with relative ease. ■Themainbenefitofedgecomputingistheability tomoveworkloadsfromdevicesintothecloud, whereresourcesarelessexpensiveanditiseasierto benefitfromeconomiesofscale.Atthesametime, itispossibletooptimizelatencyandreliabilityand achievesignificantsavingsinnetworkcommunication resourcesbylocatingcertainapplicationcomponents attheedge,closetothedevices.Toefficientlymeet applicationandserviceneedsforlowlatency, reliabilityandisolation,edgecloudsaretypically locatedattheboundarybetweenaccessnetworksor on-premisesforlocaldeployments. Sinceitsinventionadecadeago,edgecomputing hasmainlybeenusedtoimproveconsumerQoEby reducingnetworklatencyandpotentialcongestion points tospeedupcontentdelivery.Italsolowers operatorcostsbyreducingpeeringtraffic. Now,asa resultofthesurgeindatavolumethatwillcomefrom themassivenumberofdevicesenabledbyNew Radio,therolloutof5Ghasmadeedgecomputing moreimportantthaneverbefore. Beyonditsabilitiestoreducepeeringtrafficand improveuserexperienceinareassuchasvideo, augmentedreality,virtualreality,mixedrealityand gaming,edgecomputingalsoplaysakeyrolein enablingultra-reliablelow-latencycommunication usecasesinindustrialmanufacturing.Italsohelps operatorsmeetstringentlegalrequirementsondata securityandprivacythataremakingitincreasingly problematictostoredatainaglobalcloud. Edge-computingapplicationswillhavediffering requirementsdependingonwhichdriverhas motivatedthem,andtheywillbebuiltaround differentecosystemsthatutilizeplatformsthatmay beecosystem-specific.Forexample,theplatforms andapplicationprogramminginterfaces(APIs)for smartmanufacturingaredifferentfromthose requiredforgamingandotherconsumer-segment- relatedusecases,whichcanbebasedonweb-scale platformsandAPIs.Arobustedge-computing solutionmustbeabletohostplatformsofdifferent kindsandprovideahighlevelofflexibilityfor applicationdevelopers. Keyfactorsshapingtheedge-cloudecosystem Ontopofbeingabletomeettherequirementsof emerging5Gusecases,thereareotherimportant factorstoconsiderwhendesigninganedge- computingsolution,namely: ❭ Application design trends, life-cycle management and platform capabilities ❭ Expectations on management and orchestration ❭ Edge-computing industry status. Applicationdesigntrends,life-cycle managementandplatformcapabilities Cloud-nativedesignprincipleshavebecomea commondesignpatternformodernapplications– bothfortelecomworkloads[1]aswellasother services.Themodular,microservice-based architectureofcloudnativeapplicationsenables significantefficiencygainsandinnovationpotential whenpairedwithanexecutionenvironmentanda managementsystemdesignedtohandlecloud- nativeapplications. Reuseofgenericmicroservicedesignsacross differentapplicationsandenhancedplatform servicesallowsdeveloperstofocusoncoreaspects oftheservicewithregardtoqualityandinnovation. Next-generation edge-cloud ecosystem CREATING THE Edge computing Edge computing is a form of cloud computing that pushes the data processing power (compute) out to the edge devices rather than centralizing compute and storage in a single data center. This reduces latency and network contention between the equipment and the user, which increases responsiveness. Efficiency may also improve because only the results of the data processing need to be transported over networks, which consumes far less network bandwidth than traditional cloud computing architectures. The Internet of Things – which uses edge sensors to collect data from geographically dispersed areas – is the most common use case for edge computing. Hyperscale cloud providers are extending their ecosystem toward the edge, and as part of the Industry 4.0 transformation enterprises are establishing use-case-specific development environments for their edge. The Cloud Native Computing Foundation [2] is gaining traction across all these development ecosystems, enabling portability of applications to private and public clouds.
28 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 29 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 4 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 5 differentdomainsofmanagementandorchestration –rangingfromhardwaretovirtualization infrastructuretoradioandcorenetwork applications,togetherwithedge-application platformorchestration–mustallworktogether inanoptimalmanner. Edge-computingindustrystatus Edgecomputingisdependentonfunctionalities inmultipledomains.Forexample,thefirststepin applicationdeploymentistoensurethatruntime isavailableintheappropriateplace,whichputs requirementsontheorchestrationlayerandplacement capabilities,aswellasonbusinessinterfaces. Oncetheruntimeisdeployed,anchoringand connectivityarerequiredtoconfigurethenecessary localbreakoutpointsandsteerthetraffictowhere theedgeruntimerequiresit.Mostofthese functionalitiesarenotspecifictoedgecomputing andhaveeitherbeenaddressedbyindustry standardizationoropensource.Figure1presents themostrelevantstandardizationandopen-source forumsforthird-partyedgeapplications. Onthenetworkingside,the3GPPhasbeen addressingedge-computingrequirementssince release14,bothfromtheconnectivityperspective aswellasfromaserviceandexposureperspective. Addressingedgecomputingunderthe3GPPisthe onlyguaranteetosecurefullcompatibilitywith existingtelecommunicationnetworkdeployments andtheirfutureevolution[3]. Intheimplementationdomain,ETSI(theEuropean TelecommunicationsStandardsInstitute)Network FunctionsVirtualization(NFV)[4]definesthe infrastructure,orchestrationandmanagement, whileTMForumleadsthewayforthedigital transformationofCSPs. WhenitcomestoruntimeandAPIs,the fragmentationoftheusecasesisstandingintheway ofthevisionofoneruntimeandonetypeofAPI. Somedeveloperswilluseawidelyadoptedruntime likeKubernetes,especiallyitsversionscertifiedby theCNCF,orembraceweb-scaleplatforms, whilesomeverticalswillprobablydevelop,orset requirementson,theirownplatformand/orAPIs. The5G-ACIA(5GAllianceforConnected IndustriesandAutomation)consortium[5] isonesuchexample.Acomparableinitiativeinthe automotivesectoristheAECC(AutomotiveEdge ComputingConsortium)[6]. Byutilizingstandardcomponentsand telecommunicationinfrastructurethatisalready Theincreasedamountofindividualsoftware modulesandthedemandtomanagethem efficientlyimpliestheuseofcontainertechnology topackageandexecutethosesoftwaremodules. Kuberneteshasbecometheplatformofchoicefor container-based,cloud-nativeapplicationsinboth thetelecomindustryaswellasforgeneral-purpose services.Northboundmanagementsystemsfor telecomedgeworkloadsaswellasnon-telecomedge workloadsdelegatesomelife-cyclemanagement functionalitytoKubernetes,thusreducing complexityinthosemanagementsystems. TheCloudNativeComputingFoundation (CNCF)ecosystemhasbecomeafocalpointfor developersaimingtobuildmodern,scalablecloud- nativeapplicationsandinfrastructure.Embracinga certifiedKubernetesplatformisthebestwayto becomecompatiblewiththeCNCFecosystemand therebyutilizethespeedofinnovationandvariety ofapplicationsbeingdeveloped. Expectationsonmanagement andorchestration Theprimaryroleofmanagementandorchestration istoassureandoptimizetheapplicationplatform, 3GPP-definedconnectivity,cloudinfrastructureand transport,aswellasensuringtheoptimalplacement oftheedgeapplication. Putinthesimplestterms,edgecomputingisan optimizationchallengeatscalethatconsistsof severaldifferentaspects.Thefirstissupporting theconsumerexperiencebyplacingappropriate functionality–suchaslatency-sensitiveapplications –attheedge.Thesecondaspectisensuringthatthe usersareconnectedtotheseapplications.Thethird aspectisreducingthestressontransportresources andcontributingtonetworkefficiencybyplacing certaintypesofcachingfunctionsattheedge. Whileitmayseemidealfromaperformance perspectivetoplaceallapplicationsattheedge, edgeresourcesarelimitedandprioritizationsmust bemade.Fromanoptimizationperspective,itisvital toplaceonlytheapplicationsthatwillprovidethe mostbenefitattheedge.Determiningthebest locationforthemanagementfunctionality–thatis, theanalyticsfunctionalitythatcanreducetraffic backhaulatthecostoflocalprocessing–isacritical aspectoftheoptimizationprocess.Insomecases, localdeploymentofthemanagementfunctionality maybenecessarytomeetservicecontinuity expectations. Arelatedconsiderationisthelife-cycle managementoftheedgeapplicationsandtheedge applicationplatform,whichmustbeefficiently onboardedfromacentrallocation,distributed andinstantiatedtothecorrectlocations.The responsibilitiesforthiscandifferdepending ontheagreementbetweentheedgeapplication platformproviderandtheCSP.Whendeploying theedgeapplicationsandtheedgeapplication platform,appropriateconnectivitytoboththeradio andthebroadernetworkmustbeestablished. Anedge-computingsolutionmustbeableto managemanydistributededgesitesthateachhave theirownneedsbasedonlocalusagepatterns. Themassivescalethatarisesfromthispresentsa multidimensionalchallenge.Toovercomeit,the Figure 1 Relevant standardization and open-source forums Third-party edge application Application runtime environment (CNCF) Management and orchestration (TM Forum and ETSI) Connectivity infrastructure (3GPP) Distributed cloud infrastructure (ETSI) Terms and abbreviations API – Application Programming Interface | CNCF – Cloud Native Computing Foundation | CSP – Communication Service Provider | DNS – Domain Name System | IoT – Internet of Things | NFV – Network Functions Virtualization | ONAP – Open Networking Automation Platform | UPF –User Plane Function | VNF – Virtual Network Function | WAN –Wide Area Network IT IS VITAL TO PLACE ONLY THE APPLICATIONS THAT WILL PROVIDE THE MOST BENEFIT AT THE EDGE
30 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 31 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 6 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 7 inplace,aCSPwillbepreparedtohostanytypeof third-partyapplicationorapplicationplatform. Ourhigh-levelsolutionproposal Basedonourunderstandingofthekeyfactors shapingtheedge-cloudecosystem,wehavedefined threemainprinciplesthatunderpinourapproachto edgecomputing: ❭ Reuse industrialized and proven capabilities whenever possible. ❭ Ensure backward compatibility. ❭ Capitalize on existing ecosystems. Thefirstprincipleisareminderthatmanyofthe functionalitiesneededtoenableedgecomputingare notspecifictoedgecomputing.Theyhavebeenused andimprovedovertime,andtheyshouldbereused whereappropriate.Further,thefirstprinciple discouragestheadoptionofhighlyspecialized solutionsearlyintheprocess,inlightofthecurrent marketfragmentationandtheuncertaintiesabout thewinningusecasesinthissegment. Thesecondprinciplehighlightstheimportance ofensuringthatitispossibletodeployexisting applicationsthatwouldbenefitfromedge deploymentwithoutrequiringarewriteonboth thedeviceandbackendsides. Thethirdprinciplepushesustomakethe transferofapplicationsfromacentralcloud totheedgeastransparentlyaspossibletothe developers.Thismeansthereshouldbenochanges tothelife-cyclemanagementoftheapplications,and existingplatforms(alongwithanyspecializedones) shouldcontinuetobeusedforapplication managementandtoprovidetheservicesthe developersneed. Withtheseprinciplestoguideus,weproposea solutionwiththecapabilitiestoonboardedge applicationsandedgeapplicationplatformsintoa CSPenvironment,whichcanbedistributedtothe edgedatacenter,centraldatacenterorpubliccloud. Figure2depictsthehigh-levelarchitecture. Thedark-blueboxesrepresentthemaincomponents ofoursolutionandthepurpleonesindicatethird- partyapplications. Wedesignedthissolutiontomeetfourkey criteria: 1. The solution must be able to host different kinds of platforms for different application types. 2. To harmonize with existing developer communities, the execution environment must be CNCF certified (when it is provided by the CSP). 3. To address scaling and mobility issues, the orchestration and management solution of the runtime environment must be aligned with similar functionalities of the network. 4. The solution must both be compatible with 4G and 5G standards and avoid introducing a new layer of complexity (only simple and necessary APIs should be provided). Thesolutionisbasedonthedistributedcloud infrastructureforvirtualnetworkfunctions(VNFs) andtheETSINFVorchestrationandmanagement functionalities.Thesameorchestrationand managementfunctionsareusedfortheconnectivity infrastructure,distributedcloudinfrastructure, wideareanetwork(transport)orchestrationandthe orchestrationandmanagementoftheapplication executionenvironment.Thisalsoensuresthatthere isauserplanefunction(UPF)availableclosetothe applicationruntimeattherightscalinglevelthatthe sessionmanagementfunctioncanselect. Toenabletransparentconnectivitybetweenthe edgeapplicationandthedevice,theconnectivity infrastructureinoursolutionis3GPPcompatible. Asaresult,noedge-solution-specificenhancements areneededinthedevice. Theexposurefunctionalityprovidesthemain APIstothethird-partydevelopers,ofwhichthere aretwomaintypes.ThefirstsetofAPIsisforthe businessrelationwiththeoperator,toenablethe onboardingandmanagementoftheruntime environmentitselfandtoconfigureandmonitorthe connectivitythroughaggregatedAPIsbuiltontop ofthe3GPP’sservicecapabilityexposurefunction, networkexposurefunctionandoperationssupport systems/businesssupportsystemsAPIs. TheothersetofAPIscanbeexposedto third-partydevelopersforthedeploymentand managementoftheapplicationsthemselves.We proposethat,forthistypeofAPI,aCNCF-certified Kubernetesdistributionshouldbeofferedinaway similartohowitisprovidedonweb-scaleclouds today.Thisapproachharmonizeswiththetrends andprovidesdeveloperswithgreaterflexibility. Runtimeenvironment Toprovideabroadbaselinefortheadoption ofapplicationsattheedge,oursolutionprovides customizableKubernetesdistributioninaddition totheabilitytoonboardarbitrarythird-party runtimeenvironments. OneofthemainbenefitsofKubernetesinmany differentusecasesisitsmodularity.Theplugins availableinitsruntimeenvironmentallowahigh degreeofcustomizationtofitaspecifictypeof workload.Weknow,however,thatindustrial applicationsoftenrelyondedicatedruntime environmentsthatprovidetailor-made characteristics,whichmeansthattheedge willgenerallyconsistofseveraldifferentruntime environments.Asaresult,webelievethatefficient managementofamultitudeofdifferentruntime environmentsisoneofthemostimportant capabilitiesoftheedge-computingsolution. Networkingandconnectivityaspects Networkingrequirementsinedgedeployments aremainlyaboutfacilitatingconnectivitybetween attacheddevicesandcentralservices(traditional networking),attacheddevicesandedgeapplications, andedgeapplicationsandcentralservices. Thedemandsonconnectivitytypicallyvary betweendifferenttypesofedgeapplications–both withregardtothetypeofconnectivityaswellasthe requiredcharacteristics.Theexecutionenvironment, infrastructure,UPFsandmanagementsystemsmust providetherequiredconnectivityservicesflexibly andefficiently. Kubernetesprovidesavarietyofcontainer networkinterfacestomanageconnectivityboth betweenmicroservicesandtoexternalendpoints. Figure 2 High-level architecture of an edge-computing solution for a typical application Application execution environment Third-party edge application e.g. image recognition, rendering Managing the edge application Internet / IntranetWAN Devices 5G radio access Edge data center Operator data center Public cloud or private cloud Consuming connectivity and cloud servicesDistributed cloud infrastructure Management and orchestration Exposure of services Third-party application management functionality Third-party central application e.g. AI training Connectivity infrastructure
32 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 33 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 8 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 9 Furtherconnectivityfeaturesfornorth-southtraffic areenabledbyingressandegresscontrollers. Theunderlyinginfrastructureisexpectedto providebasiclayer-2andlayer-3connectivityto supporttheKubernetesnetworkinglayer.This significantlyreducesthemanagementcomplexityfor theunderlyinginfrastructureandbypassestheneed tointegratetheKuberneteslayerintoanylowerlayer infrastructuremanagementsystem. Thereareseveraltechnologiesin4Gand5Gto providelocalbreakoutfunctionality.Thepacketcore VNFs(suchastheUPF)canprovidelocalbreakout capabilitiesforthetraffictoberoutedtothe applicationsintheedgelocations.Distributed AnchorPointisagenericsolutionavailabletoday thatsuccessfullyaddressesmanyusecasesand requiresnofurtherstandardization. Lookingfurtherahead,SessionBreakoutisa DomainNameSystem(DNS)-basedsolutionfor dynamicbreakoutthatstillneedsindustry alignment.Itisexpectedtosolveissuesinmany usecases(includingenterprisebreakout). SessionBreakoutcanprovideoptimaltraffic-routing accordingtoaServiceLevelAgreement,forexample. 3GPPstandardizationwillbeneededtoaddress DNS/IPandexposureuse. MultipleSessionsisatargetsolutionthatrequires furtherindustryalignmentandsupportindevices (iOSandAndroid,forexample).Basedonservice- peeringprinciples,itwouldmapapplicationsto specificsessionsontheuserequipmentside, therebymeetingtheneedsofallusecasesalong withoperators’expectationsfornetworkslicing. Networkmanagement,orchestration andassuranceaspects Distributedcloudandedgecapabilitiesrequire thesupportofseverallayersinthenetwork:the transportlayer,thevirtualizationinfrastructure layer,theaccessandcoreconnectivitylayerandthe edgeapplicationlayer.Figure3showshowthese fourlayersfittogetherinthecontextofconsumer devices(ontheleft)anddistributedsites(atthe bottom).Theedge-applicationplatform (theruntimeenvironment)sitsbetweenthe thirdandfourthlayers,supportedbynetwork management,orchestrationandassurance. Severalorchestrationandmanagementaspects mustbeconsidered,particularlywithrespectto edgeapplications(thepurpleboxesinFigure3), theedge-applicationplatform,VNFs(shownasdark grayboxesinFigure3),virtualizationinfrastructure andthedistributionofmanagementfunctionality. Therearetwogeneralapproachestohandling edgeapplications.Thefirstistotreatthemlikean operator’sVNF.Third-partyedgeapplicationsthat willbeexecutedontheedge-applicationplatform requireadifferentapproach.Theseapplications willbecentrallyonboarded,thendistributed, andlife-cyclemanagedbytheedgeplatform. Wheninstantiating,theCSP’sorchestrationand managementwillcreatetheconnectivitytothe consumerdeviceovertheradionetworkaswellas theconnectivitytotheinternet.Theapplication managementandoverallassurancecanbe performedbytheedge-applicationprovider orbytheCSP. Theedge-applicationplatform(s)canbe onboardedandmanagedbytheCSPlikeaVNF, inthesamewaythatanyVNFisonboardedand operatedonanyvirtualinfrastructure.TheCSPwill exposecapabilitiestoinstantiatetheedgeapplication platforminstanceswhenandwhererequired. VNFs(includingcloud-nativeVNFs)are onboarded,designedandlife-cyclemanagedina similarwaytocentraldatacenterdeployments, withtwoadditionalconsiderations:transport betweensitesandappropriatedistributionof VNFstotheedgetooptimizeuserexperience. Thesecapabilitiesalreadyexistinproductsbased onETSIManagementandOrchestration(MANO) [7]and/ortheOpenNetworkAutomationPlatform (ONAP)[8]. Finally,inadistributedenvironment,itcanbe usefultodistributecertainmanagement functionalitiessuchasanalyticsandartificial intelligencefunctionsthatcanperformlocalanalysis anddeliverprocessedinsights,ratherthan backhaulingunnecessarydatatoacentralserver. Inthefuture,orchestrationandconfiguration capabilitiesmayalsobeabletoperformlocalhealing actionstosupporteitherefficientedgeoperationsor edge-servicecontinuity,evenwhencommunication totheedgesitehasbeenlost. Conclusion Edgecomputingwillplayavitalroleinenabling awiderangeof5Gusecasesandhelpingservice providersmeetstringentlegalrequirementsondata securityandprivacy.Beyonditsabilitiestoreduce peeringtrafficandimproveuserexperienceinareas suchasvideo,augmentedreality,virtualreality, mixedrealityandgaming,edge-computingisalso neededtoenableultra-reliablelow-latency communicationusecasesinindustrial manufacturingandavarietyofothersectors.To meetthesediverseneeds,communicationservice providersmustbeabletodeliveredge-computing solutionsthatcanhostdifferentkindsofplatforms andprovideahighlevelofflexibilityforapplication developers. Itisourviewthatsuccessfuldevelopmentofan edge-computingsolutionrequiresasolid understandingoftheusecases,associated deploymentoptionsandapplication-developer communities.Itisofcriticalimportancethatthe solutionisabletoonboardthird-partyapplications and/orapplicationenvironments,utilizingmethods definedbyoperationssupportsystems standardizationbodiessuchasTMForum. Ratherthanbuildinganewapplicationecosystem andplatform,westronglyrecommendreusing industrializedandprovencapabilities,utilizing themomentumcreatedwithCNCF,andensuring backwardcompatibility.Figure 3 Edge deployment and orchestration Edge application platform Edge application layer Virtualization infrastructure layer Access and core connectivity layer Transport layer Distributed sites Business support systems Edge application management Network management, orchestration & assurance IN THE FUTURE, ORCHESTRATION AND CONFIGURATIONCAPABILITIES MAYALSOBEABLETOPERFORM LOCAL HEALING ACTIONS
34 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 35 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 10 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 11 Further reading ❭ Going beyond edge computing, available at: https://www.ericsson.com/en/digital-services/trending/ distributed-cloud ❭ Cloud native applications, available at: https://www.ericsson.com/en/digital-services/trending/cloud-native ❭ How to orchestrate your journey to Cloud Native, available at: https://www.ericsson.com/en/blog/2019/5/ how-to-orchestrate-your-journey-to-cloud-native ❭ Is cloud native design really needed in telecom?, available at: https://www.ericsson.com/en/blog/2019/1/ are-cloud-native-design-really-needed-in-telecom References 1. Ericsson Technology Review, Cloud-native application design in the telecom domain, June 5, 2019, Saavedra Persson, H; Kassaei, H, available at: https://www.ericsson.com/en/reports-and-papers/ericsson- technology-review/articles/cloud-native-application-design-in-the-telecom-domain 2. Cloud Native Computing Foundation (CNCF), available at: https://www.cncf.io 3. 3GPP, 3GPP SA6 accelerates work on new verticals!, June 7, 2019, Chitturi, S, available at: https://www.3gpp.org/news-events/2045-sa6_verticals 4. ETSI, Network Functions Virtualisation (NFV), available at: https://www.etsi.org/technologies/nfv 5. 5G Alliance for Connected Industries and Automation (5G ACIA), available at: https://www.5g-acia.org/ 6. Automotive Edge Computing Consortium (AECC), available at: https://aecc.org/ 7. ETSI, Open Source MANO, available at: https://www.etsi.org/technologies/nfv/open-source-mano 8. Open Network Automation Platform, available at: https://www.onap.org/ theauthors Péter Suskovics ◆ joined Ericsson in 2007 as a software developer and participated in several productdevelopmentgroups through contributor and leader roles. The main technology areas were IP, operations and maintenance, NFV, performance management, 5G and the Internet of Things (IoT). As a strong proponent of open source, Suskovics now works as a system architect in the field of cloud, 5G and the IoT in Business Area Digital Services with a major focus on technology and innovation projects. He holds an M.Sc. in information engineering (2008) and completed his Ph.D.innetworkoptimization (2011) at the Budapest University of Technology and Economics, Hungary. Benedek Kovács ◆ joined Ericsson in 2005 as a software developer and tester, and later worked as a system engineer. He was the innovation manager of the Budapest R&D site 2011-13, where his primary role was to establish an innovative organizational culture and launch internal start-ups based on worthy ideas. Kovács went on to serve as the characteristics, performance management and reliability specialist in the development of the 4G VoLTE solution. Today he works on 5G networks and distributed cloud, as well as coordinating global engineering projects. Kovács holds an M.Sc. in information engineering and a Ph.D. in mathematics fromtheBudapestUniversity of Technology and Economics. Stephen Terrill ◆ is a senior expert in automation and management, with more than 20 years of experience working with telecommunications architecture, implementation and industry engagement. His work has included both architecture definition and posts within standardization organizations such as ETSI, the 3GPP, ITU-T (ITU Telecommunication Standardization Sector) and IETF (Internet Engineering Task Force). In recent years, his work has focused on the automation and evolution of operations support systems, and he has been engaged in open source on ONAP’s Technical Steering Committee and as ONAP architecture chair. Terrill holds an M.Sc., a B.E. (Hons.) and a B.Sc. from the University of Melbourne, Australia. Peter Wörndle ◆ is a technology expert in the area of NFV with responsibility for NFV technology evolution, technology strategy and architecture, as well as cloud-native and edge technologies. Since joining Ericsson in 2007, he has held different positions in R&D and IT, working mainly with cloud and virtualization in R&D, IT operations and standardization. Wörndle holds an M.Eng. in electrical engineering and communication from RWTH Technical University in Aachen, Germany, and currently serves as the vice-chair of the ETSI NFV Technical Steering Committee.
36 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 37 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 2 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 3 Artificial intelligence (AI) has a key role to play in helping operators achieve a high degree of automation, increase network performance and shorten time to market for new features. Our research demonstrates that graph-based frameworks for both network design and network optimization can generate considerable benefits for operators. Even greater benefits can be achieved in the longer term through a comprehensive AI-based RAN redesign. FRANCESCO DAVIDE CALABRESE, PHILIPP FRANK, EUHANNA GHADIMI, URSULA CHALLITA, PABLO SOLDATI Advanced 5G use cases and services in areas such as ultra-reliable low latency communications, massive machine-type communications and enhanced mobile broadband place heavy demands on RANs in terms of performance, latency, reliability and efficiency. ■Thewidevarietyofnetworkrequirements,paired withagrowingnumberofcontrolparametersof modernRANs,hasgivenrisetoanoverlycomplex systemforwhichvendorsarefindingitincreasingly difficulttowritemaintenance,operationand fast-controlsoftware.Thereisaclearneedtoboth simplifythemanagementandprovisioningofthe differentservicesandimprovetheperformance oftheservicesoffered. Thetechnicalobjectivesofsimplificationand performanceimprovementcanberoughlymapped tothebusinessobjectivesofreducingoperatingand capitalexpensesrespectively,whichtranslateinto reducedcost-per-byteforcommunicationservice providersandincreasedQoSforconsumers. EmbracingAItechniquesforthedesignof cellularsystemshasthepotentialtoaddressmany challengesinthecontextofbothsimplificationand performanceimprovement[1],makingitpossibleto achievenewobjectivesthatarebeyondthereachof classicaloptimizationandrule-basedapproaches. Intermsofsimplification,AIhasalreadyshown thecapabilitytosignificantlyimprovefunctionalities suchasanomalydetection,predictivemaintenance andthereductionofsiteinterventionsthrough automatedsiteinspectionswithdrones. PerformanceimprovementintheRANisagreater challenge,asitrequiresthereplacementofclassic rule-basednetworkfunctionalitieswiththeir AI-basedcounterparts.Additionalrequirements includeflexibleandprogrammabledatapipelines fordatacollectionandstorage;frameworksforthe creation(training),execution(inference)and updatingofthemodels;theadoptionofgraphical processingunitsfortraining;andthedesign ofnewchipsetsforinference. ThreedomainsforRANperformance improvement ImprovingRANperformanceinvolvesupdatingthe RAN’scontrolparametersacrosstime,frequency andspacetoadapttheRANoperationtobothstatic networkcharacteristics,suchasthe3Dgeometry ofthesurroundingsanddynamicnetworkchanges inchannel,usersandtrafficdistributions.Akey prerequisitetosuccessfullyapplyAIinthiscontext isadeepunderstandingofthenatureandroleof differentclassesofparametersaffectingnetwork performance,aswellasthecomplexityofand potentialtoimproveeachclass. EnhancingRAN performance EMBRACING AI TECHNIQUES FOR THE DESIGN OF CELLULAR SYSTEMS HAS THE POTENTIAL TO ADDRESS MANY CHALLENGES Artificial intelligence Artificial intelligence (AI) has experienced an extraordinary renaissance in recent years. The abundance of data and computational capacity that are available today have finally made decades-old techniques like deep learning practically feasible. Substantial investments from both the public and private sectors have fueled the growth of an ecosystem comprised of libraries, platforms, publications and so on that has propelled the field forward and facilitated access to AI techniques for practitioners in various areas. While the theoretical advances of the AI discipline often occur in domains such as image processing and games, the strengths exhibited by the resulting AI systems – such as the ability to optimize across multiple variables and identify patterns over complex time series – have attracted attention in many industries. In finance, manufacturing and logistics, for example, such capabilities show great potential to improve performance, reduce costs and speed up time to market. withAI
38 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 39 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 4 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 5 RANalgorithmsdomain TheRANalgorithmsdomainfocusesonoptimizing theL3toL1controlparametersthatdirectlyaffect thesignaltransmittedto/fromtheuser.Examples includehandoverandconnectivitydecisionsandthe allocationtousersofresourcessuchasmodulation andcodingscheme,resourceblocks,powerand beams.TheL3toL1algorithmsadaptthese parametersonafasttimescale,forindividual networkentities(cellsandUEs,forexample),tothe rapidlychangingenvironmentconditionsinterms ofchannel,traffic,userdistributionandsoon. OurpathtowardAI-basedRANoptimization AnaturalfirststeptowardawideintegrationofAI inRANproductsforperformanceenhancement istheadoptionofAI-basedsolutionsinthenetwork designandoptimizationdomains.Optimizingthe RANbytuningthenetworkhyperparametersis saferandeasierthanredesigningtheRANalgorithms withAI-basedsolutions,asitconsistsofanouter controlloopthatdoesnotmodifytheRANalgorithm designitselfbutonlytunesitsbehavior. Figure2demonstrateshowdifferentnetwork hyperparametervaluesresultindifferentbehaviors fortheunderlyingRANalgorithm,whichare representedbydifferentshapes.However,the performanceimprovementachievablebyAI-based networkoptimizationremainslimitedbythe underlyingdesignoftheRANalgorithmsandthe frequencyatwhichnetworkhyperparametersc anbeadapted,whichaffectstheextenttowhich thesystemcanbecontrolled. AtEricsson,ourlong-termgoalistocreatean all-encompassingAI-basedframeworkthatspans thefullhierarchyofcontrol–thatis,notonly networkdesignandoptimizationbutalso, importantly,AI-basedRANalgorithms. ExamplesofAIapplicationsintoday’snetworks Basedonourlong-standingresearchinthearea ofhowAIcanbeusedtoimproveRANperformance, EricssonhasdevelopedpowerfulAI-based frameworksfornetworkdesignandnetwork optimization,aswellasseveralotherAI-based solutionsforspecificusecases. Figure1illustratesthemaindomainsfor performanceimprovementthatwehaveidentifiedat Ericsson:networkdesign,networkoptimizationand RANalgorithms.Thedomainsarecharacterized basedonthetypeofparametersinvolved,thetype andnumberofnetworkentitiesandthefrequency atwhichupdatestypicallytakeplace. Networkdesigndomain Thenetworkdesigndomainfocusesonimproving theparametersthatdefinenetworkdeployment– suchasthenumberandlocationofnewcells,the associationsofcellstobaseband(BB)units,the selectionofBBunitstoformanelasticRAN (E-RAN)configuration,andsoon.Networkdesign traditionallyreliesonplanningtoolsandthedomain knowledgeofengineersandisperformedrather infrequently,suchaswhennewcellsareadded toanexistingnetwork. Networkoptimizationdomain Thenetworkoptimizationdomainfocusesontuning networkhyperparameters.Whiletheterm hyperparameterhasbeenstronglyassociatedwith machinelearninginrecentyears,itgenerallyrefers toanyparameterusedtocontrolthebehaviorofan underlyingalgorithm.Thehyperparameters ofthealgorithmaretunedtoproduce,forthesame measuredinput,adifferentoutputthatismore appropriateforthegivenscenario. Whilenetworkhyperparametersencompass boththecorenetworkandtheRAN,ourfocus hereisonRANhyperparameterssuchasstatic/ semi-staticconfigurationparametersforcellsand userequipmentaswellasthehyperparameters ofRANalgorithms. Networkhyperparametersareoptimizedto slowlyadapttheRANalgorithmstodifferent networkscenariosandconditionsandbringthe performanceofacertainareaofthenetwork (aparticularclusterofcells,forexample)intoa steadystatewhereinspecifickeyperformance indicators(KPIs)areimproved.Examplesinclude hyperparametersforself-organizingnetworks algorithmsandL3algorithms(mobility,load balancingandsoon)forcoordinationalgorithms (suchascoordinatedmulti-point(CoMP), multi-connectivity,carrieraggregation(CA)and supplementaryuplink),aswellasforL1/L2 algorithms(uplinkpowercontrol,linkadaptation, schedulingandthelike). Figure 1 Main performance improvement domains Domain Parameter type Network entities Update frequency Network design Deployment parameters Basebands, cells, RAN configurations, and so on Monthly/ weekly Network optimization Network hyperparameters Cell clusters/ individual cells Weekly/ daily/hourly RAN algorithms L3 to L1 transmission parameters Cells and user equipment Seconds/ milliseconds Figure 2 Impact of different hyperparameter values on the behavior of the underlying algorithms AI-based network optimization Network hyperparameters L3 to L1 transmission parameters Measurements and reports Rule-based RAN algorithms
40 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 41 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 6 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 7 Networkdesignframework Inboth4Gand5G,ourcentralizedRAN(C-RAN) andE-RANinterconnectBBunitstoallowoptimal coordinationacrosstheentirenetworkina centralized,distributedorhybridnetwork architecture.ToensurethatC-RANandE-RAN performanceisinlinewithcustomerexpectations, athoroughnetwork(re)designisrequired.Inthis regard,AItechniquesbasedonadvancednetwork graphmethodologiesareappliedtounderstand andcharacterizethecomplexradionetworkandits underlyingstructures,suchastherelationsbetween cellsandBBunits.Thisapproachleadstoanoptimal designthatmaximizesconsumerthroughput throughoptimizedCoMPandCAtechniques,and thedesignisalsofuture-proofintermsofcapacity andtechnologyexpansions.Thedesigncanbesplit intotwomainsteps. Inthefirststep,withC-RAN,BBoperationis shiftedfromsitelocationtoacentralizedBBhub. TheC-RANdesignthereforefocusesonthe reconfigurationoftheexistingdistributedRAN architecturetoacentralizedarchitecture,where cellsaregroupedinacentralizedhub.Thisisdone insuchawayastocreatetheoptimalcoordination amongcellsbelongingtothesameBBunit, resultinginhigherspectrumefficiencyand improvedconsumerexperience. C-RANconfigurationdesignisahighlycomplex taskanddifficulttosolveusingatraditionalnetwork designapproach.Thisisbecausefindinganoptimal cellgroupingthatmaximizesnetworkperformance amongalargenumberofpossiblecellgrouping combinationsrequiresnumerousaspectstobe considered,suchas: ❭ Intra and inter-frequency cell coverage overlap and neighbor signal strength ❭ Signal quality and diversity to improve coordination techniques ❭ Distance between cells ❭ Frequency band distribution per BB unit ❭ BB capacity design ❭ Future cells/sites deployment. UsinganAI-basednetworkgraphanalysis, naturalandhiddenstructureswithincellrelations (alsoknownascommunities)canbediscovered. Basedonthevariousnetworkindicatorslisted above,thestrengthofeachcellrelationshipcanbe measuredbyaweightfactor.Thehighertheweight factor,themorelikelyitisthatthesecellsshouldbe groupedtogetherintothesameBBunit. Inthesecondstep,E-RANenablesflexible coordinationbetweenBBunitsirrespectiveofthe BBdeployment.SimilartotheC-RANdesign,an AI-basednetworkgraphapproachcanalsobe appliedheretoobtainoptimalBBclustersconsisting ofasetofinterconnectedBBunitsforborderless coordinationacrosstheentirenetwork. Figure3showstheperformanceimprovement ina4GnetworkoperatedbyanAsianoperatorfor threeKPIsafteranautomatedE-RANredesign. Thefirstbargraphindicatesthattheconnections inCAmodeusingthreecomponentcarriers(CCs) increasedby30percent.Themiddlebargraph showsthatthedatavolumecarriedbyanysecondary cellincreasedby22percent,whilethebargraphon therightshowsthatdownlinkcellthroughput increasedby4.3percent.However,themost valuablebenefitisthattheE-RANdesignisentirely automatedandperformedinminutesratherthant hemonthsofworkthatwouldberequired byhumanexperts. Networkoptimizationframework Themonitoringandcontrolofnetworkperformance istraditionallyhandledbyateamofengineers supportedbyexpertsystemstargetedatoptimizing particularareasofthenetwork(typicallyacluster ofcells).Assuch,networkperformanceisoften optimizedbyusingamixofmanualandautomated rule-basedinstructionscombinedwithpredetermined thresholdsforeachnetworkperformancemetric. Theserulesandthresholdsarecompletelybased onhumanobservationsandexpertise. However,oursolutionsdemonstratethatitis possibletocreateafullyscalableandautomated closed-loopAI-basedsolutionfornetwork optimizationconsistingofautomatednetworkdata processing,networkissueidentificationand classification,detailedroot-causereasoning andautomatedparameterconfiguration Terms and abbreviations AI – Artificial Intelligence | BB – Baseband | C-RAN – Centralized RAN | CA – Carrier Aggregation | CC – Component Carrier | CoMP – Coordinated Multi-Point | E-RAN – Elastic RAN | KPI –Key Performance Indicator | L1 – Layer 1 | L2 – Layer 2 | L3 – Layer 3 | RL – Reinforcement Learning Figure 3 Performance improvement of three KPIs after an automated E-RAN design CA configuration 100.00 - 43 - 13.4 - 12.9 - 35 - 60.76 - 48.61 - 15.21 - 11.03 - Baseline AI-based Baseline AI-based Baseline AI-based Secondary cell data volume Downlink cell throughput 30% 1 CC 2 CCs 3 CCs E-RAN DESIGN IS ENTIRELY AUTOMATED
42 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 43 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 8 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 9 recommendations.Figure4illustratesthe operationsflowforEricsson’snetwork optimizationframework. State-of-the-artunsupervisedandsemi- supervisedlearningtechniquescombinedwith expertdomainknowledgeleadtoanefficient annotationofnormalandabnormalperformance patternsthatcanbeutilizedlaterforissue identificationandclassificationusingsupervised learningtechniques.Byintegratingnetwork topologiesandconfigurationswithhundredsof performancemetricsandtheirtwo-dimensional correlationintimeandspace,itispossibleto generateaknowledgegraphthatrevealsthespecific rootcausesthatleadtoanidentifiednetworkissue. Closingtheautomatedloop,networkparameter changesareautomaticallysuggestedtoresolvethe specificrootcauseandfurtherimproveperformance. AI-basedusecases Anon-exhaustivelistofAI-basedusecasesthat Ericssonhasinvestigatedincludeshandover[2],link adaptation[3],transmissionoptimizationinC-RAN, interferencemanagement,roguedronedetection[4] andfederatedlearninginRANforprivacy awareness[5].Twooftheusecasesthatareof particularinterestinthecontextofRAN optimizationarethepredictionofperformance onasecondarycarrierusingprimarycarrierdata[6] andantennatilting. Secondarycarrierprediction Theuseofbothhigh-frequencybandssuchas 28GHzandhighermillimeter-wavebandswill continuetoincreasein5Gradionetworksandin futuregenerations.Alargernumberofbands provideshighercapacitybutresultsinlarger measurementoverhead.Forinstance,initial deploymentsonthe28GHzfrequencybands willprovidespottycoverage.Foruserstobeableto makeuseofpotentiallyspottycoverageonhigher frequencies,theUEsneedtoperforminter-frequency measurements,whichcouldleadtohighmeasurement overhead.WehaveusedAItechniquestopredict coverageonthe28GHzbandbasedonmeasurements attheservingcarrier(forexampleat3.5GHz). Thisapproachdecreasedthemeasurementsona secondarycarrier,thusreducingtheenergy consumptionandthedelayforactivatingfeatures likeCA,inter-frequencyhandoverandloadbalancing. Antennatilting AI-basedantennatiltingdeservesparticular attentionamongnetworkoptimizationusecases,as itpromisestoenhancethecoverageandcapacityof mobilenetworksbyadjustingbasestationantennas’ electricaltiltbasedonthedynamicsofthenetwork environment.Unliketheconventionalantennatilt approachthatfollowsarule-basedpolicy,AI techniquesenableaself-evolvingpolicy,learning fromfeedbackthroughnetworkKPIs.Using reinforcementlearning(RL),anagentistrainedto dynamicallycontroltheelectricaltiltofmultiplebase stationsjointlysoastoimprovethesignalqualityofa cellandreducetheinterferenceonneighboringcells inresponsetochangesintheenvironment,suchas trafficandmobilitypatterns.Thisresultsinan overallimprovementofnetworkperformanceand QoEfortheuserswhilereducingoperationalcosts. Nextsteps EricssoncontinuestoinvestsignificantR&D resourcesintheuseofAIinallthreeRAN performanceimprovementdomains.Weexpect toseenotableadvancementsinthenetworkdesign andnetworkoptimizationdomainsinthenearterm, whileatthesametimeweareincreasinglyshifting ourfocustothecriticallyimportantRAN algorithmsdomain. Networkdesign Inthenetworkdesigndomain,wearecurrently workingtomakeaspectssuchascell-to-BBand BB-to-BBconnectionssoftwaredefined.This developmentwouldenabletheintegrationof automatedAI-basednetworkdesigninaclosedloop, wherethenetworkcontinuouslyreshapesitsgraph dependingonchangingtrafficpatternsorthe additionofnewnodestothenetwork. Networkoptimization Inthenetworkoptimizationdomain,ournear-term goalistoextendtheframeworktooptimizealarger numberofhyperparametersatahigherupdate frequency.Inthemid-term,weaimtointegratethese newcapabilitiesintoourproductsandultimately makethemanativepartofourproductoffering. RANalgorithms AddressingtheoptimizationoftheRANalgorithms domainisvitaltoourlong-termvisionofcreatingan all-encompassingsingleAI-basedcontrollerthat spansthefullhierarchyofcontrol.Thebenefitof suchacontrollerwouldbetheinherentcapabilityto optimizemultipletransmissionparametersacross layerssimultaneously.Thecreationofacontroller Figure 4 Flow of operations for Ericsson’s network optimization framework Configuration data Data processing Diagnostics Network Optimization Performance data Cell trace data Extract - transform - load Identification and classification Accessibility and load issues Mobility issues Coverage issues Interference issues Root-cause analytics and insights Accessibility and load Mobility Coverage Interference Recommendations and actions Accessibility and load Mobility Coverage Interference ERICSSON CONTINUES TO INVEST SIGNIFICANT R&D RESOURCES IN THE USE OF AI
44 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 45 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 10 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 11 withtheabilitytolearndirectlythroughexploration ofthestatespacewouldremovetheboundaries imposedbyhuman-designedalgorithms,makingit possibletoidentifybettercombinationsof transmissionparameterswithinalayerand acrosslayers.Moreover,acontrollerwiththe abilitytolearnfromdatawouldinherently betunedtotheenvironmentandbefree ofnetworkhyperparameters,whichwouldlead tosimplificationofthesoftwarestack. Nonetheless,replacingL3toL1RANalgorithms withasingleAI-basedcontrollerpresentsmore challengesthannetworkdesignandoptimization, onseverallevels.Onechallengeisthatfast parameterchangesintroducetheproblemof transients,andthereforerequiretheAIcontroller topredicttheshort-termstateevolutionofthe systemduetochannelandtrafficchanges,for example,aswellastheactionstheAIcontroller itselfsubmitstothesystem. Anotherchallengeistheneedtoredefinethe radioaccessprobleminawaythatenableslearning throughinteractionwiththeRANenvironment. Today’sdivide-and-conquerapproachforproviding radioaccesstoUEsbybreakingdowntheproblem intomanysubproblemsofmanageablecomplexity, anddesigningspecificsolutionsforeach subproblem,isdifficulttoapplywhenusing AI-basedcontrollers.Inotherwords,stayingwithin thecurrentfragmentedRANframeworkwith differentAI-basedcontrollers,eachtryingto optimizeaRANfeaturewhilelearningthrough interactionwiththesameRANenvironment, wouldpreventthesystemfromlearningand jeopardizesystemperformance. Onepossibleapproachtoaddresssuchchallenges wouldbetoadoptRLastheframeworkofchoicefor RANcontrol.RLhasthenecessarycapabilitiesto dealwithtransients,butitremainschallengingto deployitinthecontextofthecurrentfragmented RANframework.Tothisend,oneapproachwould betoredefinetheproblemanddeviseasolutionwith asinglestageofstateestimationandasinglestageof downstreamend-to-endcontrol.Thisdesignchoice wouldenableastateestimationascloseaspossible tothetruesystemstateandacontrollercapable ofjointoptimizationoverseveraltransmission parameters. Additionally,atrueAI-basedredesignofthe systemwouldrequireend-to-endintegrationofthe differentlayersofthecontrolhierarchyinsuchaway thatslower(higher)layersofcontrol(suchasnetwork design)canmakedecisionstoimproveoverall systemperformanceasafunctionofthemodels learnedforthefaster(lower)layersofcontrol(such asRANalgorithms). Conclusion Interestinartificialintelligence(AI)isgrowing rapidlyinthetelecomindustryasoperatorslookfor waystoautomateRANoperations,boostnetwork performanceandshortenthetimetomarketfornew features.Itisimportanttonote,however,thatthe successfuluseofAItooptimizetheperformance ofaradiocommunicationnetworkrequiresadeep understandingbothofthenatureandroleofthe differentclassesofparametersthataffectnetwork performance,aswellasthecomplexityand optimizationpotentialofeachclass. AtEricsson,ourlong-termaimistoredefinethe overallconceptofradioaccesscontrolwiththeintent tocreateacellularnetworkthatconstantlyadapts itselftothestaticanddynamiccharacteristicsofthe scenariosaswellastherequirementsofthe customers.Tohelpgetusthere,wehaveidentified threemainRANperformanceimprovement domainsbasedonthetypeofparametersinvolved, thetypeandnumberofnetworkentitiesandthe frequencyatwhichupdatestypicallytakeplace. Ourworkdemonstratesthatgraph-based frameworksforbothnetworkdesignandnetwork optimizationcangenerateconsiderablebenefitsin termsofimprovedperformance,simplified managementandshortertimetomarket.Looking furtherahead,weexpectthatthecreationofasingle AIcontrollerthatreplacesRANalgorithmswillplay akeyroleinacomprehensiveAI-basedRAN redesignandultimatelymakeitpossibletoachieve performancetargetsthatareunreachableina traditionalrule-baseddesign. Further reading ❭ Ericsson pioneers machine learning network design for SoftBank, available at: https://www.ericsson.com/ en/press-releases/2018/5/ericsson-pioneers-machine-learning-network-design-for-softbank ❭ How will AI enable the switch to 5G?, available at: https://www.ericsson.com/en/networks/offerings/ network-intelligence-and-automation/ai-report ❭ Towards zero-touch networks, available at: https://www.ericsson.com/en/ai-and-automation ❭ Ericsson launches unique AI functionality to boost radio access networks, available at: https://www.ericsson.com/en/news/2019/10/ericsson-ai-to-boost-ran ❭ AI in 5G networks: Highlights from our latest report, available at: https://www.ericsson.com/en/ blog/2019/5/ai-in-5g-networks-report-key-highlights ❭ Automated network operations, available at: https://www.ericsson.com/en/digital-services/offerings/ network-automation ❭ How to connect the dots of future network AI, available at: https://www.ericsson.com/en/blog/2019/7/ connect-the-dots-of-future-network-AI ❭ An introduction to machine reasoning in networks, available at: https://www.ericsson.com/en/ blog/2019/11/machine-reasoning-networks-introduction ❭ Supercharging customer experience through AI and automation: The inside view, available at: https://www.ericsson.com/assets/local/managed-services/ai-automation-report-screen-aw.pdf References 1. Ericsson, Employing AI techniques to enhance returns on 5G network investments, 2019, available at: https://www.ericsson.com/49b63f/assets/local/networks/offerings/machine-learning-and-ai-aw-screen.pdf 2. Cornell University, submitted to IEEE Globecom 2019, ArXiv preprint arXiv:1904.02572, 5G Handover using Reinforcement Learning, Yajnanarayana, V; Rydén, H; Hévizi, L; Jauhari, A; Cirkic, M, available at: https://arxiv.org/abs/1904.02572 3. In Proceedings of the 2019 Workshop on Network Meets AI & ML (NetAI'19) Pages 44-49, Contextual Multi-Armed Bandits for Link Adaptation in Cellular Networks, 2019, Saxena, V; Jaldén, J; Gonzalez, J E; Bengtsson, M; Tullberg, H; Stoica, I, available at: https://www.researchgate.net/publication/335183811_ Contextual_Multi-Armed_Bandits_for_Link_Adaptation_in_Cellular_Networks 4. Cornell University, ArXiv preprint arXiv:1805.05138, Rogue Drone Detection: A Machine Learning Approach, 2018, Rydén, H; Redhwan, S B; Lin, X, available at: https://arxiv.org/abs/1805.05138 5. Ericsson Technology Review, Privacy-aware machine learning with low network footprint, October 21, 2019, Vandikas, K; Ickin, S; Dixit, G; Buisman, M; Åkeson, J, available at: https://www.ericsson.com/en/ reports-and-papers/ericsson-technology-review/articles/privacy-aware-machine-learning 6. IEEE, In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Predicting strongest cell on secondary carrier using primary carrier data, Ryden, H; Berglund, J; Isaksson, M; Cöster, R; Gunnarsson, F, available at: https://ieeexplore.ieee.org/document/8369000
46 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 ✱ AI AND RAN PERFORMANCE 12 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 theauthors Francesco Davide Calabrese ◆ joined Ericsson in 2017 as a concepts researcher. In his current role he works on concepts that redefine wireless communications through AI. Prior to joining Ericsson, he worked as a researcher at Nokia and Huawei. He holds a Ph.D. in wireless communication from Aalborg University in Denmark. Philipp Frank ◆ joined Ericsson in 2014. In his current role, he heads AI development for network design and optimization within Ericsson’s Managed Services business area. He holds a Ph.D. in electrical engineering and information technology from the University of Stuttgart, Germany, and a certificate from the executive program AI – Implications for Business Strategy from the Massachusetts Institute of Technology. Euhanna Ghadimi ◆ joined Ericsson in 2018 where he works with AI concepts for future radio access products in Business Area Networks. Prior to joining Ericsson, he was employed at Huawei as a 5G networks researcher and at Scania, where his work focused on AI solutions for connected vehicles. Ghadimi received a Ph.D. in telecommunications from KTH Royal Institute of Technology in Stockholm, Sweden, in 2015. His research interests are in the areas of optimization theory, machine learning and wireless networks. Ursula Challita ◆ joined Ericsson Research as a researcher in 2018, the same year she received a Ph.D. in machine learning for radio resource management at the University of Edinburgh, UK. She was a visiting research scholar at Virginia Tech in the US from 2016 to 2018. Her research interests include machine learning, optimization theory and wireless cellular networks. Pablo Soldati ◆ joined Ericsson in 2018 as a standardization and concepts researcher for 5G New Radio and AI. He received a Ph.D. in wireless communications from KTH Royal Institute of Technology in Stockholm, Sweden, in 2010. He was a postdoctoral scholar at KTH and a visiting postdoctoral scholar at Stanford University before joining Huawei in 2011, where he served as a principal researcher.
48 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 49 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 2 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 3 For most mobile operators, the introduction of the 5G System (5GS) will be a migration from their existing Evolved Packet System (EPS) deployment to a combined 4G-5G network that provides seamless voice and data services. This migration requires a carefully tailored, holistic strategy that includes all network domains and considers the operator’s specific needs per domain. RALF KELLER, TORBJÖRN CAGENIUS, ANDERS RYDE, DAVID CASTELLANOS Introducing the 5GS to provide mobile broadband (MBB) in an existing 4G EPS network has significant impacts across all network domains – from the RAN, to packet core, user data and policies, and services – as well as backend systems. ■ TheEPSisprimarilyusedtodayforavarietyof MBBusecases.Insomecases,EPSdeployments havealreadybeenupgradedforearlysupportof5G bynon-standalone(NSA)NewRadio(NR).Many such4GandNSANRoperatorshavealready decidedtointroduce–orareconsidering introducing–the5GSasstandardizedbythe3GPP. The5GSintroducessupportforNRstandalone (SA)[1]andisspecifiedtosupportexistingMBB usecasesaswellasnewandimprovedones. Ericssonbelievesthatoperatorscanaddress increasingtrafficdemandsandquicklyintroduce innovativenewservicesbyusingNSANR andSANR[2]. DuringthemigrationperiodwhenNRcoverage isbeingbuiltout,servicesrequiringwide-area coveragearebestsupportedthroughinterworking betweenthe5GCore(5GC)andtheexisting EvolvedPacketCore(EPC).Overtime,agrowing numberofnewusecaseswillutilizetheestablished 5GSMBBscaleandwide-areanetworkbuild-out. TheinterworkingwiththeEPCplaces dependenciesonthebackendbusinesssupport systems(BSS)systemintegration,sinceuserdata andpoliciesneedtosupporttwonetworks(theEPC and5GC).Thenewdevicesmustsupport5GS-related capabilities,whileatthesametimedevicesthatonly supporttheEPS–includinginboundroaming devices–willexistforalongperiodandwillrequire correspondingnetworksupport.Thislong-term needisastrongargumentinsupportoftheconcept ofadual-modecorenetworksolutionthatincludes bothEPCand5GCfunctionality. Akeybenefitofadual-modecorenetwork solutionisthecommonoperationalmodelforthe EPCand5GC,whichsimplifiesthemanagementof theoverallsystem.Italsoincludesmoregranular life-cyclemanagementoftheindividualsoftware modules(alsocalledmicroservices)basedoncloud- nativedeploymentandoperationalprinciples[3]. Thecommonoperationalmodelcanbeusedfor dynamicandflexiblescalingofindividualmicro- servicesbasedoncapacityneeds,suchasrebalancing theEPCversus5GCresourceusagewhenthedevice fleetevolvesovertimefrom4Gto5G. 5GSarchitectureforinterworkingwithEPS Introducingthe5GStoanetworkrequiresa comprehensivestrategythatconsidersallnetwork domains,coveragestrategy,spectrumassetsand devices,aswellaswhichservicestoofferwhere. Amongotherthings,the5GC,thepacketcoreinthe 5GS,introducesnewnetworkfunctionsand interfacesinternallyandtowardoperationssupport systemsandBSS,includingchargingsystems.Italso hasnewinterfacesandprotocolstowardthenext- generationRAN(NG-RAN)anddevices,which meansthattheRANmigration,includingspectrum assetsanddevicestrategies,needstobecoordinated withthe5GCintroduction. Additionally,anyplantointroducethe5GC mustconsideritsnewservice-basedarchitecture, whichincludesanetworkrepositoryfunctionfor serviceregistrationanddiscovery,aswellasnew capabilitieslikenetworkslicingsupportand networkexposure. OperatorswithbothNRandLTEaccessareable touse5GCcapabilitiesfortightinterworkingtothe EPS,alsoknownasEPC-5GCtightinterworkingin thefirstreleaseof5Gspecificationsinthe3GPP[4]. 5Gmigration strategy FROM EPS TO 5G SYSTEM Terms and abbreviations 5GC – 5G Core | 5GS – 5G System | AMF – Access and Mobility Management Function | BSS – Business Support Systems | CA – Carrier Aggregation | CAS – Customer Administration System | CP – Control Plane | CUPS – Control Plane User Plane Separation | EPC – Evolved Packet Core | EPS – Evolved Packet System | E-UTRAN – Evolved Universal Terrestrial Radio Access Network | FDD – Frequency Division Duplex | gNB – NR Node B | HSS – Home Subscriber Server | HTTP – Hypertext Transfer Protocol | IMS – IP Multimedia Subsystem | MBB – Mobile Broadband | MME – Mobility Management Entity | NAS –Non-AccessStratum|NG-RAN –Next-GenerationRAN |NR–NewRadio|NSA–Non-Standalone| PCF – Policy Control Function | PCRF – Policy Control and Charging Rules Function | PDN – Packet Data Network | PDU – Protocol Data Unit | PGW – Packet Data Network Gateway | REST – Representational State Transfer | SA – Standalone | SGW – Serving Gateway | SMF – Session Management Function | SMSoIP – SMS-over-IP | SMSoNAS – SMS-over-NAS | SPR – Subscription Profile Repository | TDD – Time Division Duplex | UDM – Unified Data Management | UDR – Unified Data Repository / User Data Repository | UE – User Equipment | UP – User Plane | UPF – User Plane Function
50 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 51 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 4 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 5 AMF,butnotinbothsimultaneously).TheN26 referencepointisusedforbothidlemodeand connectedmodemobility;thedeviceinitiatesidle modemobility(possiblytriggeredbytheRAN), whileconnectedmodeisinitiatedbytheRAN,and thedeviceisinformedwhenhandoverpreparation hasbeencompleted.Furthermore,tightinterworking alsoincludeshowtomapprotocoldataunit(PDU) sessionsinthe5GStopacketdatanetwork(PDN) connectionsintheEPSandviceversa. Theinterworkingarchitectureensuresthatthe new5GC-capabledevicesarealwaysconnectedto theUPFinthe5GCindependentlyiftheyare connectedthough4Gor5Gaccess,whichenables IPaddresspreservationwhendevicesmove betweenaccesses.Thus,servicecharacteristicsare maintained,sinceanycolocatedvalue-added servicesconnectedtotheUPFarethesame, andthepolicycontrolfunction(PCF)applies sessionpoliciesforthedevicewhenconnecting over4Gor5Gaccess. Policyandsubscriptionmanagementneedtobe providedinaconsistentwayforadeviceusing NRorLTEaccess.Theinterworkingarchitecture providesseveral5GCcapabilitiesalsooverLTE/ EPC,includingsupportfornetworkslicing. OperatorscanmigratefromanexistingEPC toadual-modeEPCand5GCnetworksolutionby migratingthepacketgateway,thepolicycontrol andthesubscriptionanddatamanagement,andby introducingnewfunctionality. Overcomingdomain-specific migrationchallenges AsolidEPSto5GSmigrationstrategywillconsider andaddressthechallengesindevicesandallfour networkdomains:RAN,packetcore,userdataand policies,andservices. TheRANdomain Inmanymarkets,thenewNRspectrumisfirst availableinmidandhighbands.Dependingonthe sitegrid,introducingtheNG-RANwithNRSA onlyonthesebandsmayleadtospottyNRcoverage thatisonlysuitableforlocalareaservices.When deployingNRSAforMBB,itispreferabletoensure continuousNRcoveragewithinthetargetedservice area(acity,forexample)toavoidfrequentmobility betweenNRandLTE.Alternatively,intersystem mobilitycouldbelimitedbyconservativemobility thresholds. Achievinghighercapacityandcontinuous coveragerequiresacombinationofNRonmidand highbandsforcapacityandNRFDDonsufficiently lowbandsforcoverage[2].TheNRFDDspectrum onlowbandcanbeeithernew,re-farmedoran existingLTEbandthatissharedbetweenNRand LTEusingdynamicspectrumsharing.NRbands canbecombinedusingcarrieraggregation(CA)or, insomecases,dualconnectivity. NRCAwillbevitalinenablingserviceproviders toservethegrowingnumberof5Gdevicesinthe networkwhilemaintainingoverallnetwork performanceanduserexperience.Thisisdoneby activatingdownlinkCA(FDD+TDD)intheareas withlow-bandandmid-bandNR.Thisnotonly boostsmid-bandNRcoverage,andconsequently capacitygain,butalsoprovidesafurthercoverage boostbyenablingsomeoftheNRsignalingtobe movedtolowerbands.Ericssonhasshownthatthis canprovideupto3-7dBextragaininlinkbudget onthedownlink[8]. NRSAwithCAreducescomplexityintheRAN anddevicescomparedwithdualconnectivityasin NRNSA.Thedevicedoesnotneedtotransmiton twouplinksatthesametime.TheuseofNRSA alsoreducesthetimefromadevicebeingininactive modetofullNRcapacity,enabledbyallcontrol signalingbeingcarriedoverNRinsteadofbeing dependentonLTEandthesetupofdual connectivity.Thebenefittotheconsumerisfaster accesstothefullpotentialofthecombinedNR capacitywhen,forexample,downloadingafile orstartingupavideo. Figure1showsthe5GSarchitectureforEPC-5GC tightinterworking.ToenableIPaddress preservationwhenconnectingoverandchanging between4Gand5Gaccess,the5GCarchitecture includesacommonuserplane(UP)anchorpoint realizedbythesessionmanagementfunctionplus packetdatanetworkgatewaycontrolplanefunction (SMF+PGW-C)andtheuserplanefunctionplus PGWuserplanefunction(UPF+PGW-U). Tosupportseamlessservicecontinuityand network-controlledhandover,theMobility ManagementEntity(MME)andthenewaccess andmobilitymanagementfunction(AMF)interact directlythroughtheN26referencepoint,which supportsdevicesinsingle-registrationmode(the deviceiseitherregisteredintheMMEorinthe Figure 1 EPC-5GC tight interworking architecture Services HSS UDM PCF Internet and data services = signaling = user plane (or combined) = legacy 4G components = new 5GS components AMFMME SGW E-UTRAN NG-RAN IMS SMF+ PGW-C UPF+ PGW-U User data and policies Packet core RAN Devices POLICYAND SUBSCRIPTIONMANAGEMENT NEED TO BE PROVIDED IN A CONSISTENT WAY
52 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 53 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 6 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 7 Tosupportexistingandforthcomingservices likevoiceandemergencywhenmigratingMBB tothe5GS,theNRSAneedstosupportcapabilities andcoveragedemandslikeintersystemhandover, positioningandQoS.Thiscanbeastepwisemigration. Theuserdataandpoliciesdomain TheEPC-5GCtightinterworkingarchitecture showninFigure1assumescommonsubscription managementsupportregardlessoftheaccess technologyusedbyagivenuser.Althougha combinedHSS/UDM(HomeSubscriberServer/ UnifiedDataManagement)functionisdepicted, firstdeploymentssupportsubscriptionmanagement fortheEPCand5GCbyinterworkingbetweena separateHSSandUDMthroughanHTTP/REST (HypertextTransferProtocol/Representational StateTransfer)interface. ExistingHSSfunctionalitymustbeevolvedto enableinterworkingwithUDMandtosupport tightinterworkingbetweentheEPCand5GC. TheevolutionincludesanupgradeoftheHSS functionalityofferedtoEPCservingnodeswith, amongotherthings,subscriptionparametersthat enableuseraccesstothe5GCtoensureIPsession continuityandsingleregistrationacrossthe EPCand5GC. Oncethe5GCforMBBisintroduced,sessions areanchoredintheSMF+PGW-Cfunction. TheuseofSMF+PGW-Callowsthepolicycontrol andchargingrulesfunction(PCRF)usedforpolicy controlintheEPCtobereplacedbyanew dual-modepolicymanagementsystemthat supports5G-enableddevicesregardlessofthe accesstechnologycurrentlyused. HSS/UDMandPCF/PCRFbusinesslogic arealsohighlydependentonhowsubscription dataandpolicysubscriptiondataismanaged (thatis,provisioned,storedandaccessed). TheEPCallowsthesupportofadata-layered architecture,wheretheHSSandPCRFmakeuse ofanexternaldatabasetomanagesubscriptiondata. ThesedatabasesareknownastheUserData Repository(4G-UDR)forHSSsubscriptiondata andtheSubscriptionProfileRepository(SPR) forPCRFpolicysubscriptiondata.The5GC generalizestheconceptintoaUnifiedData Repository(5G-UDR).The5G-UDRstores subscription,policysubscription,application andexposuredata. Duringtheintroductionofthe5GCitisbeneficial todeploynewdual-modesubscription,dataand policymanagementsystemsthatsupportthe EPC-5GCtightinterworkingarchitectureand proceduresasdepictedinFigure2. Thedual-modedatamanagementsystemenables bothsubscriptiondatacentralizationandsingle pointofprovisioninginnew5G-UDRinstances for5G-enabledsubscribers.Thisrequires subscriptiondatamigrationfromthelegacy 4G-UDR/SPRtothe5G-UDR.Thismigration processcanbeassistedbyautomaticmigration toolsand/orbyauto-provisioning/activation mechanismsenabledbynotificationstothe BSS/CAS(customeradministrationsystem), suchasthedetectionofa4G-onlyuserusinga 5G-capabledeviceand/orbeingactiveinthe5GC. Asafirstmigrationstep,theHSSfunctionality inthelegacysubscriptionmanagementsystemmay stillbeusedfor5G-enableduserswhentheyconnect throughtheEPC.TheexistingHSSinstancesmay reachsubscriptiondatafor5G-enabledusersfrom the5G-UDRthroughthelocal4G-UDR/SPRusing proprietaryinterfaces,forexample.ThePCFinthe dual-modepolicymanagementsystemsupports 5G-enableduserseveniftheyconnectthrough theEPC. Alternatively,5G-enabledusersmaybeserved byHSSfunctionalityinthedual-modesubscription managementsystemwhentheyconnectthrough theEPC.Thedual-modesubscription,dataand policymanagementsystemsfor5G-enabled subscriptionscancoexistwiththeexisting subscription,dataandpolicymanagementsystems forlegacy4Gsubscriptions(existingHSSand PCRFinstancesand4G-UDR/SPR)withthe supportofasignalingroutingfunction. Ineithercase,thegoalisthatalltypesof subscriptions(evenlegacy4G-onlysubscriptions) aremanagedonthenewdual-modesystemfor subscription,dataandpolicymanagement,asshown totherightinFigure2.Thedesignofthissolution followstheprinciplesofacommonoperational modelbasedoncloud-nativeimplementationand offersitsbenefitstomanagebothnew5G-enabled subscribersregardlessoftheaccesstechnology usedandlegacy4Guserswhoconnectonly throughtheEPC. Thepacketcoredomain Accordingtoourdefinition,thepacketcoredomain includesfunctionalitytohandleaccessandmobility management(MME,AMF),sessionmanagement, theserving-gateway-controlplanefunction (SGW-C),PGW-C,SMFandUPfunctionality (SGW-U/PGW-U,UPF). Theinitialintroductionofthe5GCforwide-area servicesallowsamigrationofRANandcore independentlyofeachother,similartothetransition from3Gto4G.Priortothestandardizationrelease of5G,the3GPPalsostandardizedaseparationof thegatewayfunctionsintheEPCintocontrolplane (CP)andUP,knownasCUPS.Thisseparation enablesnewopportunitiesforUPdistribution andedgebreakoutoftrafficalreadyintheEPC. SeparatePDNconnectionscanusedifferent SGW-U/PGW-Usincentralandlocaldeployments, forexample.ThefunctionalseparationoftheCP andUPintheEPCCUPSiscarriedoverinthe5GC architecturecorrespondingtotheSMFandUPF. TheavailabilityofbothCUPSandEPC-5GC tightinterworkingenablesmultiplepossible migrationpathsfromtheEPCtothe5GC. OneoptionistofirstintroduceCUPSintotheEPS, whichenablestheoperatortousetheCPandUP separationbeforemigratingtothe5GS.Thiscan bebeneficialtohandleincreasedtrafficdemand whenintroducingNRNSAandpreparefor asmoothmigrationtothe5GCbasedona UPimplementationthatsupportsboththe EPCand5GC. AnotheroptionistointroduceCUPSatthe sametimeasthe5GCbycolocatingSMF+PGW-C withSGW-CfunctionalityandUPF+PGW-U withSGW-Ufunctionality.Thisallowsthenew high-capacity5GdevicestobeservedbyaCP andUPsplit-gatewayarchitecturewhenconnecting overeither4Gor5Gaccess,asshowninthemiddleFigure 2 Migration to dual-mode subscription, data and policy management systems BSS/CAS Legacy subs, data and policy management Legacy subs, data and policy management Dual-mode subs, data and policy management Dual-mode subs, data and policy management HSS PCRF HSS PCRF HSS/UDM PCF HSS/UDM PCRF/PCF 4G-UDR /SPR 4G-UDR /SPR 5G-UDR 5G-UDR BSS/CAS BSS/CAS THE DESIGN OF THIS SOLUTION FOLLOWS THE PRINCIPLES OF A COMMON OPERATIONAL MODEL BASED ON CLOUD-NATIVE IMPLEMENTATION
54 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 55 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 8 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 9 partof Figure3.Anadditionalbenefitofthis colocationisthepossibilityformoreflexible distributionoftheUPindifferentlocations, forexampletoaddresslow-latencyservicesby placingtheUPclosertotheRAN.The5GC tightinterworkingcanalsointerworkwitha legacySGWasshowninFigure1. ThemiddlepartofFigure3alsoshowsthatitis possibletocontinuetousetheexistingSGWand PGWfunctionalityintheEPCforlegacydevices with4G-onlysubscriptionswhenintroducing the5GC,whichminimizestheimpactonexisting servicesandsubscribers.However,theMME needstosupportthegatewayselection(SGW-C& SMF+PGW-C)fordeviceswith5GCsubscription and5GC-NAS(non-accessstratum)capability. Thelatteriscommunicatedaspartofthetracking areaupdate/attachprocedureintheEPS. TheMMEcanalsoutilizeadditionalmethods toassistinthegatewayselection,suchasdedicated AccessPointNamesorDomainNameSystem lookupenhancements.TheMMEmayalsoneedto supportrestrictions,suchasiftheoperatorwantsto preventmobilitytothe5GSforcertainsubscribers. ThenextmigrationstepistocombinethefullEPC and5GCfunctionalityintoadual-modepacketcore, serving5G-enabledsubscribersregardlessofthe accesstechnologyusedandlegacy4Gusersthat connectonlythroughtheEPC,asshownontheright sideofFigure3.Inthisexample,thenew5GC devicesareservedbytheSMF+PGW-C,whilethe legacy4G-onlydevicesarestillservedbyaseparate PGWinstance.Otherdeploymentmodelsare alsoconceivable. Thegoalofthemigrationisthereforeasolution thatfollowstheprinciplesofacommonoperational modelbasedoncloud-nativeimplementationand offersitsbenefitstobothnew5G-enabled subscribersregardlessoftheaccesstechnology usedandtolegacy4Gusers,whoconnectonly throughtheEPC. Whenintroducingthe5GS,itisalsoimportant toconsiderfutureservicesbeyondMBB.Onewayto future-proofthenetworkarchitectureisthrough supportfornetworkslicing.Inthiscontext,all functionsrequiredtosupportMBBandneeded foraccessandmobilitymanagement,session managementandtheUP(SMF+PGW-Cand UPF+PGW-U)arepartofanMBBnetworkslice. Hence,asinglenetworkslicecanbeusedbothfor internetaccessandforIMSvoiceandotherIMS services.Thisarrangementcanbemaintained whenthedevicestartsontheEPCandmoves tothe5GCorviceversa. Theservicesdomain Regardlessofaccesstechnology,supportforIMS voice,emergencyservicesandSMSisexpectedto workseamlesslyanywheresubscriberscanconnect tothenetwork.AphonewillnotselectNRSAaccess unlessitdetectsIMSvoiceservice.Voiceand emergencyservicesaresupportedbyspecific capabilitiesintheRANandPacketCorethat mustbeprovidedtothecombinedEPSand5GS aftermigration.TheIMSneedstobeupdated tosupportNRSA. EPSfallbackcanbeusedasafirstvoicemigration stepwhenintroducingthe5GSindeploymentswhere NRcoveragehasunderlyingLTE/EPCcoverage. EPSfallbackmeansthatthedeviceismovedtoEPS atcallestablishment.Itistypicallyusedpriortothe deploymentofallthevoicecapabilitiesinNRor beforetheRANisdimensionedandtunedforvoice. ThesubsequentvoicemigrationfromEPS fallbacktovoiceoverNRisachievedbyallowing voicecallstobeestablishedonNRconnectedtothe 5GCinsteadofperformingEPSfallbackatcall establishment.Thismigrationstepcanbedoneonce allrequiredvoice-over-NRcapabilitiesareinplace inthedeviceandthenetwork,andRANis dimensionedandtunedforvoice.However,devices introducedbeforethisstepwillremaininthefield whenvoiceoverNRisintroduced,whichiswhythe networkmustsupportvoiceoverNRincluding EPSfallback. Withrespecttoemergencyservices,the3GPP hasspecifieddifferentmethodsforemergencycalls inthe5GS.Forexample,usingaservicerequestto performEPSfallbackofemergencycallsisapossible firstmigrationstep.Thebenefitofthisapproachis thatitonlyimpactstheAMFandgNB,anditavoids regulatoryrequirementsonthe5GSrelatedto emergencycalls.Migratingtoemergency-call- over-NRrequiresallregulatoryrequirements relatedtoemergencycallstobeaddressed. Operatorsmustdecidewhethertostartwith aservicerequestforemergencyortodeploy emergency-call-over-NRdirectly. SMScontinuestobeanimportantserviceinthe 5GS.TherearetwobasicmethodstotransportSMS inthecombined5GSandEPS,namelySMS-over-IP (SMSoIP)usinganIMSSIP(SessionInitiation Protocol)messageandSMS-over-NAS (SMSoNAS)usingNASsignaling.Thelatteruses 5GNASwhenthedeviceisinthe5GSand4GNAS whenintheEPS.OperatorsthatuseSMSoNASfor devicesintheEPSaremigratingtosupport SMSoNASinthe5GS.Operatorsthatarealready usingSMSoIPintheEPSmaycontinuetosupport SMSoIPoverthe5GS. BeyondMBB,5Galsoincludesamultitudeofnew andenhancedcapabilitiesaddressingmanydifferent businesssegments.Itisdifficulttopredictwhich ofthesecapabilitieswillberequiredintheearly introductionofthe5GSandtheanswermayvaryfor differentoperators.Examplesoffrequentlymentioned opportunitiesforwide-areaservicesareautomotive [5],smartgridsforutilities,andpublicsafety. Alloftheseusecaseswillutilizetheestablished 5GSMBBscaleandwide-areanetworkbuild-out, specificallyifsomekeyarchitecturalconceptsare plannedforwhenintroducingthe5GS.Theseinclude technologieslikenetworkslicing,edgecomputing andnetworkexposure[6],forwhichtherequired functionalityisalreadybuilt-infromthestart. These5GStechnologiesarealsokeyenablers forlocalnetworkdeploymentsathospitals, harbors,airportsandmanufacturingfacilities[7]. Conclusion Introducingthe5GSystemforwide-areaservices inanexistingEvolvedPacketSystemnetworkhas significantimpactsacrossallnetworkdomains, includingtheRAN,packetcore,userdataand policies,andservices,aswellasaffectingdevicesand backendsystems.Nonetheless,acombined4G-5G networkisanecessarystepformostoperatorswith existingEPSdeployments. Figure 3 Migration from the EPS to the 5GS utilizing EPC-5GC tight interworking Existing EPC PGW SGW MME E-UTRAN Existing EPC PGW SMF + S/PGW-C UPF+ S/PGW-U SGW MME New 5GC with interworking Dual-mode packet core PGW SMF + S/PGW-C UPF+ S/PGW-U SGW MME E-UTRAN NG-RAN E-UTRAN NG-RAN AMF AMF ...5GS TECHNOLOGIES ARE ALSO KEY ENABLERS FOR LOCAL NETWORK DEPLOYMENTS...
56 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 57 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 10 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 11 Further reading ❭ 5G is here, available at: https://www.ericsson.com/en/5g ❭ 5G Voice, available at: https://www.ericsson.com/en/digital-services/trending/5g-voice-evolution-where-to-start ❭ 5G Access, available at: https://www.ericsson.com/en/networks/offerings/5g ❭ 5G Core, available at: https://www.ericsson.com/en/digital-services/offerings/core-network/5g-core References 1. Ericsson Technology Review, 5G New Radio RAN and transport choices that minimize TCO, November 7, 2019, Eriksson A.C; Forsman, M; Ronkainen, H; Willars, P; Östberg, C, available at: https://www.ericsson. com/en/reports-and-papers/ericsson-technology-review/articles/5g-nr-ran-and-transport-choices-that- minimize-tco 2. Ericsson Technology Review, Simplifying the 5G ecosystem by reducing architecture options, November 30, 2018, Cagenius, T; Ryde, A; Vikberg, J; Willars, P, available at: https://www.ericsson.com/en/reports- and-papers/ericsson-technology-review/articles/simplifying-the-5g-ecosystem-by-reducing-architecture- options 3. Ericsson Technology Review, Cloud-native application design in the telecom domain, June 5, 2019, Persson, H.S; Kassaei, H, available at: https://www.ericsson.com/en/reports-and-papers/ericsson- technology-review/articles/cloud-native-application-design-in-the-telecom-domain 4. 3GPP TS 23.501, 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; System Architecture for the 5G System (5GS), available at: https://www.3gpp.org/ DynaReport/23501.htm 5. Ericsson Technology Review, Driving transformation in the automotive and road transport ecosystem with 5G, September 13, 2019, Lohmar, T; Zaidi, A; Olofsson, H; Boberg, C, available at: https://www. ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/transforming-transportation-with-5g 6. Ericsson Technology Review, Service exposure: a critical capability in a 5G world, May 7, 2019, Friman, J; Ek, M; Chen, P; Manocha, J; Soares, J, available at: https://www.ericsson.com/en/reports-and-papers/ ericsson-technology-review/articles/service-exposure-a-critical-capability-in-a-5g-world 7. Ericsson Technology Review, August 27, 2019, 5G-TSN integration meets networking requirements for industrial automation, Farkas, J; Varga, B; Miklós, G; Sachs, J, available at: https://www.ericsson.com/en/ reports-and-papers/ericsson-technology-review/articles/5g-tsn-integration-for-industrial-automation 8. Ericsson, 2019, Sharing for the best performance, available at: https://www.ericsson.com/en/networks/ offerings/5g/sharing-spectrum-with-ericsson-spectrum-sharing/download-form theauthors Ralf Keller ◆ is an expert in core network migration at Ericsson who joined the companyin1996.Hiscurrent focus is on packet core architecture and technology. His work includes both technology studies and contributions to product strategies for mobile communication, including communication services in 5G, migration to the 5GS, and interworking and coexistence with legacy networks. He is also active in the GSMA, where he works on the profiling of 5G and 5G roaming. Keller holds a Ph.D. in computer science from the University of Mannheim in Germany. Torbjörn Cagenius ◆ is a senior expert in network architecture at Business Area Digital Services. He joined Ericsson in 1990 and has worked in a variety of technology areas such as fiber-to-the-home, main-remote RBS, fixed- mobile convergence, IPTV, network architecture evolution, software-defined networking and Network Functions Virtualization. In his current role, he focuses on 5G and associated network architecture evolution. Cagenius holds an M.Sc. in electrical engineering from KTH Royal Institute of Technology in Stockholm, Sweden. Anders Ryde ◆ is a senior expert in network and service architecture at Business Area Digital Services. He joined Ericsson in 1982 and has worked in a variety of technology areas in network and service architecture development for multimedia-enabled telecommunication, targeting both enterprise and residential users. This includes the evolution of mobile telephony to IMS and VoLTE. In his current role, he focuses on bringing voice and other communication services into 5G, general 5G evolution and associated network architecture evolution. Ryde holds an M.Sc. in electrical engineering from KTH Royal Institute of Technology in Stockholm. David Castellanos ◆ is a senior specialist in subscription handling for MBB at Product Development Unit User Data Management & Policy. He joined Ericsson in 1996 and has worked on identity and subscription management solutions for different access generations (2G/3G/4G) and domains (IMS and identity federation). In his current role, he is focused on identity and subscription management in 5G. Castellanos holds two bachelor’s degrees in telecom engineering from Universidad Politécnica de Madrid in Spain. Theauthorswould liketothankVictor FerraroEsperanza, PerWillars,Göran Hall,JoseMiguel DopicoandMagnus Hallenstålfortheir contributionsto thisarticle. Successfulmanagementofthistransition requiresaholisticstrategythatconsidersallnetwork domains,aswellascoveragestrategy,spectrum assetsandwhichservicestoofferwhere.Thereare severalsupportedmigrationpathsperdomaintoa full5GS,andthetransitioncanbeadaptedto addresseachoperator’sspecificneedsperdomain. Inthelongerterm,theintroductionof5GS supportingmobilebroadbandasinitialwide-area serviceisasolidfoundationtointroduceadditional servicesandusecases,meetingthefullexpectation onafuture-proof5GS.
58 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 59 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 2 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 3 The enhancements in the 3GPP releases 16 and 17 of 5G New Radio include both extensions to existing features as well as features that address new verticals and deployment scenarios. Operation in unlicensed spectrum, intelligent transportation systems, Industrial Internet of Things, and non-terrestrial networks are just a few of the highlights. JANNE PEISA, PATRIK PERSSON, STEFAN PARKVALL, ERIK DAHLMAN, ASBJØRN GRØVLEN, CHRISTIAN HOYMANN, DIRK GERSTENBERGER According to the latest Ericsson Mobility Report, global traffic levels hit 38 exabytes per month at the end of 2019, with a projected fourfold increase to 160 exabytes per month expected by 2025 [1]. Fortunately, the 5G system is designed to handle this massive increase in data traffic in a way that ensures superior performance with minimal impact on the net costs for consumers. ■ Theevolutionof5GNewRadio(NR)has progressedswiftlysincethe3GPPstandardizedthe firstNRrelease(release15)inmid-2018.Notonlyis release16nearlyfinalizedbutthescopeofrelease 17hasalsorecentlybeenapproved.Makingwise decisionsinthemonthsandyearsaheadwill requirethatmobilenetworkoperatorsandother industrystakeholdershaveasolidunderstanding ofbothreleases. NRdevelopmentstartedinrelease15[2][3]with theambitiontofulfillthe5Grequirementssetbythe ITU(InternationalTelecommunicationUnion)in IMT-2020(InternationalMobileTelecommunications- 2020).Theoveralldesignconsistsofseveralkey components.Theextensiontomuchhighercarrier frequenciesisanimportantoneduetothecontinuing demandformoretrafficandhigherconsumerdata ratesandtheassociatedneedformorespectrum andwidertransmissionbandwidths.Theultra-lean designofNRenhancesnetworkenergyperformance andreducesinterference,whileinterworkingand LTEcoexistencewillmakeitpossibletoutilize existingcellularnetworks.Theforwardcompatibility ofNRdesignwillensurethatitispreparedforfuture evolution.Lowlatencyisalsocriticaltoimprove performanceandenablenewusecases.Extensive usageofbeamformingandamassivenumberof antennaelementsfordatatransmissionandfor control-planeproceduresarealsonotable componentsofNRdesign. Figure1showsthetimeplanfortheevolution ofNRoverthenextfewyears.Release16,thefirst stepintheNRevolution,containsseveralsignificant extensionsandenhancements.Someoftheseare extensions/improvementstoexistingfeatures, whileothersareentirelynewfeaturesthataddress newdeploymentscenariosand/ornewverticals. 5GNRrelease16–enhancements toexistingfeatures Themostnotableenhancementstoexistingfeatures inrelease16areintheareasofmultiple-input, multiple-output(MIMO)andbeamforming enhancements,dynamicspectrumsharing(DSS), dualconnectivity(DC)andcarrieraggregation (CA),anduserequipment(UE)powersaving. Multiple-input,multiple-output andbeamformingenhancements Release16introducesenhancedbeamhandling andchannel-stateinformation(CSI)feedback, aswellassupportfortransmissiontoasingleUE frommultipletransmissionpoints(multi-TRP)and full-powertransmissionfrommultipleUEantennas intheuplink(UL).Theseenhancementsincrease throughput,reduceoverhead,and/orprovide additionalrobustness[4].Additionalmobility enhancementsenablereducedhandoverdelays, inparticularwhenappliedtobeam-management mechanismsusedfordeploymentsinmillimeter (mm)wavebands[5]. Dynamicspectrumsharing DSSprovidesacost-effectiveandefficientsolution forenablingasmoothtransitionfrom4Gto5Gby allowingLTEandNRtosharethesamecarrier. Inrelease16,thenumberofrate-matchingpatterns availableinNRhasbeenincreasedtoallow spectrumsharingwhenCAisusedforLTE. Dualconnectivityandcarrieraggregation Release16reduceslatencyforsetupandactivation ofCA/DC,therebyleadingtoimprovedsystem capacityandtheabilitytoachievehigherdatarates. Unlikerelease15,wheremeasurementconfiguration andreportingdoesnottakeplaceuntiltheUEenters thefullyconnectedstate,inrelease16theconnection canberesumedafterperiodsofinactivitywithoutthe needforextensivesignalingforconfigurationand reporting[6].Additionally,release16introduces aperiodictriggeringofCSIreferencesignal transmissionsincaseoftheaggregationof carrierswithdifferentnumerology. Userequipmentpowersaving ToreduceUEpowerconsumption,release16 includesawake-upsignalalongwithenhancements tocontrolsignalingandschedulingmechanisms[7]. Figure 1 NR evolution time plan Release 14 2016 Release 15 NR NR evolution Release 16 Release 17 Release 18 Release 19 2017 2018 2019 2020 2021 2022 2023 5Gevolution:3GPP RELEASES 16 & 17 OVERVIEW
60 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 61 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 4 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 5 throughthecellscreatedbyanIABnode,thereby enablingmulti-hopwirelessbackhauling. ThelowerpartofthefigurehighlightsthatanIAB nodeincludesaconventionalDUpartthatcreates cellstowhichUEsandotherIABnodescanconnect. TheIABnodealsoincludesamobile-termination (MT) partprovidingconnectivityfortheIABnode to(theDUof)thedonornode. NewRadioinunlicensedspectrum Spectrumavailabilityisessentialtowireless communication,andthelargeamountofspectrum availableinunlicensedbandsisattractivefor increasingdataratesandcapacityfor3GPPsystems. Toexploitthisspectrumresource,release16enables NRoperationinunlicensedspectrum,targetingthe 5GHzand6GHzunlicensedbands.Itsupportsboth standaloneoperation,wherenolicensedspectrum isnecessary,andlicensed-assistedoperation,where acarrierinlicensedspectrumaidstheconnection setup.Thisgreatlyaddstodeploymentflexibility comparedwithLTE,whereonlylicensed-assisted operationissupported. Operationinunlicensedspectrumisdependent onseveralkeyprinciplesincludingultra-lean transmissionanduseoftheflexibleNRframestructure. Bothofthesewereincludedinrelease15. Channelaccessmechanismsbasedonlisten- before-talk(LBT)areprobablythemostobvious areaofenhancementinrelease16.NRlargely reuses thesameLBTmechanismasdefined forWi-FiandLTEinunlicensedspectrum. Interestingly,itwasdemonstratedduring standardizationthatreplacingoneWi-Finetwork withanNRnetworkcanleadtoimproved performancefortheremainingWi-Finetworks[9] aswellasfortheNRnetworkitself. IndustrialIoTandultra-reliable low-latencycommunication TheIIOTisamajorverticalfocusareaforNR release16.TowidenthesetofpotentialIIoTuse casesandsupportincreaseddemandfornewuse casessuchasfactoryautomation,electricalpower distributionandthetransportindustry,release16 includeslatencyandreliabilityenhancementsthat buildonthealreadyverylowair-interfacelatency andhighreliability[10]providedbyrelease15. Supportfortime-sensitivenetworking(TSN), whereveryaccuratetimesynchronizationis essential,isalsointroduced.Figure3illustrates TSNintegrationin5GNR. 5GNRrelease16–newverticals anddeploymentscenarios Themostnotablenewverticalsanddeployment scenariosaddressedinrelease16areintheareasof: ❭ Integrated access and backhaul (IAB) ❭ NR in unlicensed spectrum ❭ Features related to Industrial Internet of Things (IIoT) and ultra-reliable low latency communication (URLLC) ❭ Intelligent transportation systems (ITS) and vehicle-to-anything (V2X) communications ❭ Positioning. Integratedaccessandbackhauling IABprovidesanalternativetofiberbackhaulby extendingNRtosupportwirelessbackhaul[8]. Asaresult,itispossibletouseNRforawirelesslink fromcentrallocationstodistributedcellsitesand betweencellsites.Thiscansimplifythedeployment ofsmallcells,forexample,andbeusefulfortemporary deploymentsforspecialeventsoremergency situations.IABcanbeusedinanyfrequencyband inwhichNRcanoperate.However,itisanticipated thatmm-wavespectrumwillbethemostrelevant spectrumforthebackhaullink.Furthermore, theaccesslinkmayeitheroperateinthesame frequencybandasthebackhaullink(knownas inbandoperation)orbyusingaseparatefrequency band(out-of-bandoperation). Architecture-wise,IABisbasedontheCU/DU splitintroducedinrelease15.TheCU/DUsplit impliesthatthebasestationissplitintotwoparts– acentralizedunit(CU)andoneormoredistributed units(DUs)–wheretheCUandDU(s)maybe physicallyseparateddependingonthedeployment. TheCUincludestheRRC(radioresourcecontrol) andPDC(packetdataconvergence)protocols,while theDUincludestheRLC(radiolinkcontrol)and MAC(multipleaccesscontrol)protocolsalongwith thephysicallayer.TheCUandDUareconnected throughthestandardizedF1interface. Figure2illustratesthebasicstructureofa networkutilizingIAB.TheIABnodecreatescells ofitsownandappearsasanormalbasestationto UEsconnectingtoit.ConnectingtheIABnodeto thenetworkusesthesameinitial-accessmechanism asaterminal.Onceconnected,theIABnodereceives thenecessaryconfigurationfromthedonornode. AdditionalIABnodescanconnecttothenetwork Figure 2 High-level architecture of IAB CU DU MT DU MT DU F1 Donor node IAB node Backhaul based on IAB Access link Donor node IAB node IAB node Conventional backhaul Access link Access link Backhaul based on IAB IAB node F1 Figure 3 Overview of the TSN integration Ethernet TSN domain Ethernet TSN domain5G domain: supporting Ethernet/TSN Time reference IIoT device UE 5G Core RAN Ethernet bridge Programmable logic controller TSN control
62 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 63 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 6 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 7 AlthoughmanyoftheURLLC-related improvementsaresmallinthemselves,taken togethertheysignificantlyenhanceNRinthearea ofURLLC[11]. Theinter-UEdownlink(DL)preemptionthatis alreadysupportedinrelease15isextendedin release16toincludetheUL,suchthataUE’s previouslyscheduledlower-priorityUL transmissioncanbepreempted(thatis,cancelled) byanotherUE’shigher-priorityULtransmission. Release16alsosupportsstandardizedhandling ofintra-EUULresourceconflicts. Toreducelatency,release16supportsmore frequentcontrol-channelmonitoring.Furthermore, forbothULconfiguredgrantandDLsemi-persistent scheduling,multipleconfigurationscanbeactive simultaneouslytosupportmultipleservices.These enhancementsareespeciallyusefulincombination withTSNtraffic,wherethetrafficpatternisknown tothebasestation. Intelligenttransportationsystems andvehicle-to-anythingcommunications ITS,whichprovidearangeoftransportand traffic-managementservices,areanothermajor verticalfocusareainrelease16.Amongother benefits,ITSsolutionsimprovetrafficsafetyaswell asreducingtrafficcongestion,fuelconsumption andenvironmentalimpacts.TofacilitateITS, communicationisrequirednotonlybetween vehiclesandthefixedinfrastructurebutalso betweenvehicles.Currently,25usecasesfor advancedV2Xcommunicationshavebeendefined, includingvehicleplatooningandcooperative communicationusingextendedsensors[12]. Inrelease15,communicationwithfixed infrastructureisprovidedbytheaccess-link interfacebetweenthebasestationandtheUE. Release16addstheoptionoftheNRsidelink(PC5), whichcanoperateinin-coverage,out-of-coverage andpartial-coveragescenarios,utilizingallNR frequencybands.Itsupportsunicast,groupcastand broadcastcommunication,andhybridautomatic repeatrequest(hybrid-ARQ)retransmissionscan beusedforscenariosthatrequiremorerobust communication.Groupscanbeeitherconfigured orformed,andthegroupmemberscommunicate usinggroupcasttransmissions.Atruckplatoon, forexample,couldbeconfiguredusingdedicated hybrid-ARQsignalingbetweenthereceivers andtransmitter,orformedinadynamicmanner basedonthedistancebetweenthetransmitter andreceiver(s). Positioning Formanyyears,UEpositioninghasbeen accomplishedwithGlobalNavigationSatellite Systemsassistedbycellularnetworks.Thisapproach providesaccuratepositioningbutistypicallylimited tooutdoorareaswithsatellitevisibility.Thereis currentlyarangeofapplicationsthatrequires accuratepositioningnotonlyoutdoorsbutalso indoors.Architecture-wise,NRpositioningisbased ontheuseofalocationserver,similartoLTE.The locationservercollectsanddistributesinformation relatedtopositioning(UEcapabilities,assistance data,measurements,positionestimatesandsoon) totheotherentitiesinvolvedinthepositioning procedures.Arangeofpositioningmethods,both DL-basedandUL-based,areusedseparatelyorin combinationtomeettheaccuracyrequirementsfor differentscenarios. DL-basedpositioningissupportedbyproviding anewreferencesignalcalledthepositioning referencesignal(PRS).ComparedwithLTE, thePRShasamoreregularstructureandamuch largerbandwidth,whichallowsforamoreprecise correlationandtimeofarrival(ToA)estimation. TheUEcanthenreporttheToAdifferencefor PRSsreceivedfrommultipledistinctbasestations, andthelocationservercanusethereportsto determinethepositionoftheUE. UL-basedpositioningisbasedonrelease15 soundingreferencesignals(SRSs)withrelease16 extensions.BasedonthereceivedSRSs,thebase stationscanmeasureandreport(tothelocation server)thearrivaltime,thereceivedpowerand theangleofarrivalfromwhichtheposition oftheUEcanbeestimated. ThetimedifferencebetweenDLreceptionand ULtransmissioncanalsobereportedandusedin round-triptime(RTT)basedpositioningschemes, wherethedistancebetweenabasestationand aUEcanbedeterminedbasedontheestimated RTT.BycombiningseveralsuchRTT measurements,involvingdifferentbasestations, thepositioncanbedetermined. 5GNRrelease17 Theworkitemsapprovedbythe3GPPinDecember 2019willleadtotheintroductionofnewfeaturesfor thethreemainusecasefamilies:enhancedmobile broadband(eMBB),URLLC and massivemachine- typecommunications(mMTC).Thepurposeisto supporttheexpectedgrowthinmobile-datatraffic, aswellascustomizingNRforautomotive,logistics, publicsafety,mediaandmanufacturingusecases. Theenhancementstoexistingfeaturesintroducedin release17willbeforfunctionalityalreadydeployed inliveNRnetworksorrelatetospecificnew requirementsthatareemerginginthemarket. ThetablepresentedinTable1summarizesthe scopeoftheenhancementstoexistingNRfeatures inrelease17,whilethetablein Table2 summarizes thescopeofthenewfeatures. Terms and abbreviations CA – Carrier Aggregation | CSI – Channel-State Information | CU – Centralized Unit | DC – Dual Connectivity | DL – Downlink | DSS – Dynamic Spectrum Sharing | DU – Distributed Unit | eMBB – Enhanced Mobile Broadband | FR1, FR2 – Frequency Range 1, 2 | hyrbrid-ARQ – Hybrid Automatic Repeat Request | IAB – Integrated Access and Backhaul/Backhauling | IIoT – Industrial Internet of Things | IoT – Internet of Things | ITS – Intelligent Transportation Systems | LBT – Listen-Before-Talk | LTE-M – LTE Machine-Type Communications | MIMO – Multiple-Input, Multiple-Output | mMTC – Massive Machine-Type Communications | MT – Mobile Termination | MTC – Machine-Type Communications | Multi-TRP – Multiple Transmission Points | NR – New Radio | PC5 – Direct Mode Interface | PRS – Positioning Reference Signal | RTT – Round-Trip Time | SON – Self-Organizing Networks | SRS – Sounding Reference Signal | ToA – Time of Arrival | TSN – Time-Sensitive Networking | UE – User Equipment | UL – Uplink | URLLC – Ultra-Reliable Low-Latency Communication | V2X – Vehicle-to-Anything | XR – Anything Reality CURRENTLY, 25 USE CASES FOR ADVANCED V2X COMMUNICATIONS HAVE BEEN DEFINED THE ENHANCEMENTS… RELATE TO SPECIFIC NEW REQUIREMENTS THAT ARE EMERGING IN THE MARKET
64 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 65 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 8 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 9 eMBB feature IAB • Additionof(limited)supportfornetworktopologychanges • Improvedduplexingofaccessandbackhaullinks (simultaneousoperation onchildandparentlink,forexample) • Routingenhancements MIMO • Improvementsbasedonexperiencefromcommercialnetworksfocusingon multi-beamoperationmainlyforfrequencyrange2(FR2),supportformulti-TRP deployment,SRSs,andCSImeasurementandreporting DSS • Cross-carrierschedulingenhancements • Otherschedulingenhancements Coverage • Enhancedwide-areacoverageforbothFR1andFR2(tobestudied) • Focusonmobilebroadbandandvoiceservicesusecases,withtheexception ofthelow-powerwideareausecase Multi-radio dual connectivity • Moreefficientactivation/deactivationmechanismofsecondarycells • Conditionalprimary-secondarycellchange/addition UE power saving • Improvedmechanismsintheareaofdiscontinuousreceptionandblinddecoding ofcontrolchannels Data collection • Simplifieddeploymentandenhancementstosupportself-organizingnetworks (SON)withimproveddata-collectionmechanismsforSONandminimization ofdrivetests QoE management and optimizations for diverse services • GenericframeworkfortriggeringandconfiguringQoEmeasurementcollection andreportingforvarious5Gusecases URLLC feature IIoT and URLLC support • ImprovedsupportforfactoryautomationandURLLC,includingphysicallayer feedbackenhancementsandenhancementsforsupportoftimesynchronization • IdentificationofenhancementsforURLLC/IIoToperationincontrolled environmentsonunlicensedbands Positioning • Higheraccuracy(horizontalandvertical)andlowerlatency,especiallyfor IIoTusecases Sidelink • FocusonV2X,publicsafetyandcommercialusecases • Resourceallocationenhancement • Sidelinkdiscontinuousreception RAN slicing (also relevant for the mMTC use case) • MechanismstoenableUEfastaccesstothecellsupportingtheintendedslice • Mechanismstosupportservicecontinuityforintra-radio-accesstechnology handoverserviceinterruption mMTC feature Small data transmissions in inactive state • Reducedoverheadfromconnectionestablishment • Usecases:keep-alivemessages,wearablesandvarioussensors Table 1 Summary of release 17 enhancements to existing features eMBB feature Supporting NR from 52.6GHz to 71GHz •ExtendedNRfrequencyrangetoallowexploitationofmorespectrum, includingthe60GHzunlicensedband •DefinitionofnewOFDM(orthogonalfrequency-divisionmultiplexing) numerologyandchannelaccessmechanismtocomplywiththeregulatory requirementsapplicabletounlicensedspectrum Multicast and broadcast services • PrimarilytargetedatV2X,publicsafety,IPmulticast,softwaredelivery andInternetofThings(IoT)applications Support for multi-SIM devices • Pagingcollisionavoidance • NetworknotificationwhenaUEswitchesnetworks Support for non- terrestrial networks • Supportforsatellites(especiallyLowEarthorbitandgeostationarysatellites) andhigh-altitudeplatformsasanadditionalmeanstoprovidecoverage inruralareas Multi-radio dual connectivity • L2versusL3relaying(studyandcompare) • Scenariosincludesingle-hop,UE-to-UEandUE-to-networkrelaying Sidelink relaying • Improvedmechanismsintheareaofdiscontinuousreceptionandblind decodingofcontrolchannels Data collection • SimplifieddeploymentandenhancementstosupportSONwithimproveddata- collectionmechanismsforSONandminimizationofdrivetests URLLC feature Anything reality (XR) evaluations • Evaluateneedsintermsofsimultaneouslyprovidingveryhighdatarates andlowlatencyinaresource-efficientmanner • Intendedtosupportvariousformsofaugmentedrealityandvirtualreality, collectivelyreferredtoasXR mMTC feature Support of reduced- capability NR devices • Targetedatmid-tierapplicationssuchasmachine-typecommunicationsfor industrialsensors,videosurveillance,andwearableswithdataratesbetween NarrowbandIoT/LTE-Mdataratesand“full”NRdatarates • Addressesissuesincludingcomplexityreduction,UEpowersavingandbattery lifetimeenhancement Table 2 Summary of new functionality added in release 17
66 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 67 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 10 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 11 Further reading ❭ Leading the way to 5G through standardization, available at: https://www.ericsson.com/en/blog/2019/5/lte- nr-interworking-in-5G ❭ A new standard for Dynamic Spectrum Sharing, available at: https://www.ericsson.com/en/blog/2019/6/ dynamic-spectrum-sharing-standardization ❭ Standardizing a new paradigm in base station architecture, available at: https://www.ericsson.com/en/ blog/2019/9/standardizing-a-new-paradigm-in-base-station-architecture ❭ Drones and networks: mobility support, available at: https://www.ericsson.com/en/blog/2019/1/drones-and- networks-mobility-support ❭ How to identify uncertified drones with machine learning, available at: https://www.ericsson.com/en/ blog/2019/5/how-to-identify-uncertified-drones-machine-learning ❭ An overview of remote interference management for 5G, available at: www.ericsson.com/en/blog/2019/9/ overview-of-remote-interference-management References 1. Ericsson Mobility Report, November 2019, available at: https://www.ericsson.com/en/mobility-report/ reports/november-2019 2. Academic Press, Oxford, UK, 5G NR: The Next Generation Wireless Access Technology, 2018, Dahlman, E; Parkvall, S; Sköld, J 3. IEEE Wireless Communications, pp. 124-130, Ultra-Reliable and Low-Latency Communications in 5G Downlink: Physical Layer Aspects, June 2018, Ji, H; Park, S; Yeo, J; Kim, Y; Lee, J; Shim, B, available at: https://ieeexplore.ieee.org/document/8403963 4. 3GPP RP-182863, Enhancements on MIMO for NR, available at: www.3gpp.org 5. 3GPP RP-190489, NR mobility enhancements, available at: www.3gpp.org 6. 3GPP RP-191600, LTE-NR & NR-NR Dual Connectivity and NR Carrier Aggregation enhancements, available at: www.3gpp.org 7. 3GPP TR 38.840, Study on User Equipment (UE) power saving in NR, available at: www.3gpp.org 8. 3GPP TR 38.874, NR; Study on Integrated Access and Backhaul, available at: www.3gpp.org 9. 3GPP TR 38.889, Study on NR-based access to unlicensed spectrum, available at: www.3gpp.org 10. 3GPP TR 38.824, Study on physical layer enhancements for NR ultra-reliable and low latency case (URLLC) , available at: www.3gpp.org 11. 3GPP TR 38.825, Study on NR industrial Internet of Things (IoT) , available at: www.3gpp.org 12.3GPP TR 38.885, Study on NR Vehicle-to-Everything (V2X) , available at: www.3gpp.org Conclusion Theenhancementsinthe3GPP’sreleases16and 17willplayacriticalroleinexpandingboththe availabilityandtheapplicabilityof5GNewRadio toawiderangeofnewapplicationsandusecases inbothindustryandpublicservices.Inorderto makethedetailsofthesetworeleasesmoreeasily digestible,wehaveidentifiedwhatweconsider tobethemostsignificantenhancementsand groupedthemintotwocategories:enhancements toexistingfeaturesandfeaturesthataddress newverticalsanddeploymentscenarios. FromEricsson’spointofview,theoverall ambitionoftheNRevolutionfromause-case perspectivemustbetoensurethat5GNRcovers allrelevantusecasestofulfillthevisionofubiquitous connectivity–thatis,theabilitytoconnectanything anywhereatanytime.Fromafeaturesperspective, webelievethattheevolutionofNRfunctionality mustbedrivenbythegoalofincreasingefficiency andeffectivenesswhenandwhereitiscommercially justified. Lookingahead,itiscriticalthattheindustry workstogethertoensurethatNRiseasytodeploy andoperate,andthatitcontinuestoprovide superiorperformancecomparedwithcompeting technologies.WemustalsoensurethatNRprovides ahighdegreeofenergyefficiencyonboththenetwork anddevicesides,andthatitretainsitsabilityto coexistsmoothlywithLTE. AtEricsson,weareconvincedthatthebestway forwardisforNRtocontinuetosupportalluse casesfromoneplatform,withafocusonforward compatibility,sufficientconfigurabilityandmaximal simplicity.Wemustalsoworktoavoidunnecessary updatesinthenetworkhardwareandensurethat functionalityisspecifiedinacommonwaythat benefitsmultipleusecases.
68 ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 69 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 12 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 13 theauthors Asbjørn Grøvlen ◆ is a principal researcher in physical layer standardization who joined Ericsson in 2014. He currently works as Ericsson’s technical coordinator for 3GPP RAN WG1 and has been involved in the standardization of wireless-access technologies from 3G to 4G LTE and 5G NR. His contribution to NR (5G) has been on initial access and mobility. Grøvlen holds an M.Sc. in electrical engineering from the Norwegian University of Science and Technology in Trondheim. Christian Hoymann ◆ joined Ericsson Research in 2007 and currently leads a research group at Ericsson Eurolab in Aachen, Germany. His team focuses on standardization of 4G and 5G radio networks (Wi-Fi, LTE and NR). In addition, he heads up Ericsson’s 3GPP RAN standardization delegation as the company’s technical coordinator for 3GPP RAN. Hoymann holds a Ph.D. in electrical engineering from RWTH Aachen University, Germany. Dirk Gerstenberger ◆ joined Ericsson in 1997 after earning a Dipl-Ing. in electrical engineering from Paderborn University in Germany. He is currently a manager at the Standards & Technology department within Business Area Networks at Ericsson, working with the evolution of radio-access standards and radio-network deployments. Gerstenberger led the radio-access standardization as head of Ericsson’s RAN1 delegation and chairman of 3GPP RAN1 during standardization of 3G and 4G, and he was also engaged in industry initiatives leading to the standardization of 5G. He received the Ericsson Inventor of the Year award in 2008 and is named as the inventor in more than 100 patents. theauthors Janne Peisa ◆ has worked at Ericsson in the research and development of 3G, 4G and 5G systems since 1998. He is currently responsible for coordinating Ericsson’s research on 5G evolution and beyond 5G activities. Previously, he coordinated Ericsson’s RAN standardization activities in the 3GPP and led Ericsson Research’s 5G program. In 2001, he received the Ericsson Inventor of the Year award. Peisa has authored several publications and patents and holds a Ph.D. in theoretical physics from the University of Helsinki, Finland. Patrik Persson ◆ joined Ericsson Research in 2007 and currently serves as a principal researcher. Since 2014 he has been responsible for the Ericsson back-office work in the 3GPP RAN standardization of 4G and 5G. Prior to that, he worked extensively in the areas of antennas and propagation as well as proprietary development of LTE. Persson holds a Ph.D. in electrical engineering from KTH Royal Institute of Technology in Stockholm, Sweden. Stefan Parkvall ◆ is a senior expert working with future radio access. He joined Ericsson in 1999 and played a key role in the development of HSPA, LTE and NR radio access. Parkvall has also been deeply involved in 3GPP standardization for many years. He is an IEEE (Institute of Electrical and Electronics Engineers) fellow and has coauthored several popular books, including 4G: LTE/ LTE-Advanced for Mobile Broadband, and 5G NR: The Next Generation Wireless Access Technology. He has more than 1,500 patents in the area of mobile communication and holds a Ph.D. in electrical engineering from KTH Royal Institute of Technology. Erik Dahlman ◆ joined Ericsson in 1993 and is currently a senior expert in radio-access technologies within Ericsson Research. He has been involved in the development of wireless-access technologies from early 3G to 4G LTE to 5G NR. He is currently focusing on the evolution of 5G as well as technologies applicable beyond 5G wireless access. He is the coauthor of the books 3G Evolution: HSPA and LTE for Mobile Broadband, 4G: LTE and LTE-Advanced for Mobile Broadband, 4G: LTE- Advanced Pro and the Road to 5G, and, most recently, 5G NR: The Next Generation Wireless Access Technology. Dahlman holds a Ph.D. in telecommunications from KTH Royal Institute of Technology.
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Ericsson Technology Review: issue 1, 2020

  • 1.
    ERICSSON TECHNOLOGY C H AR T I N G T H E F U T U R E O F I N N O V A T I O N | V O L U M E 1 0 1 I 2 0 2 0 – 0 1 5GNEWRADIO EVOLUTION PRIVACY-AWARE MACHINE LEARNING NEXT-GENERATION EDGE-CLOUD ECOSYSTEM
  • 3.
    CONTENTS ✱ #01 2020✱ ERICSSON TECHNOLOGY REVIEW 5 08 PRIVACY-AWARE MACHINE LEARNING WITH LOW NETWORK FOOTPRINT Federated learning makes it possible to train machine learning models without transferring potentially sensitive user data from devices or local deployments to a central server. As such, it addresses security and privacy concerns at the same time that it improves functionality and performance. 16 5G NEW RADIO RAN AND TRANSPORT CHOICES THAT MINIMIZE TCO By deploying self-built transport in the RAN area instead of using leased lines, mobile network operators gain access to the full range of 5G New Radio RAN architecture options and minimize their total cost of ownership (TCO). 26 CREATING THE NEXT-GENERATION EDGE-CLOUD ECOSYSTEM Edge computing has great potential to help communication service providers improve content delivery, enable extreme low-latency use cases and meet stringent legal requirements on data security and privacy. 36 ENHANCING RAN PERFORMANCE WITH ARTIFICIAL INTELLIGENCE Artificial intelligence has a key role to play in helping operators achieve a high degree of automation, increase network performance and shorten time to market for new features. Our research shows that graph-based frameworks for both network design and network optimization can generate considerable benefits. 48 5G MIGRATION STRATEGY: FROM EPS TO 5G SYSTEM The necessary migration from existing Evolved Packet System (EPS) deployments to combined 4G-5G networks that provide seamless voice and data services requires a carefully tailored, holistic strategy that includes all network domains and considers each operator’s specific needs per domain. 58 5G NEW RADIO EVOLUTION The enhancements in the 3GPP releases 16 and 17 of 5G New Radio include both extensions to existing features as well as features that address new verticals and deployment scenarios. Operation in unlicensed spectrum, intelligent transportation systems, Industrial Internet of Things, and non-terrestrial networks are just a few of the highlights. 16 Training (global) Training Inference Data lake (global) Pipelines Data Model distribution Aggregated weights Ericsson Customer Local deployment 1 Training Inference Local deployment 2 Training Inference Local deployment 3 Local storage Local storage Local storage 08 Configuration data Data processing Diagnostics Network Optimization Performance data Cell trace data Extract - transform - load Identification and classification Accessibility and load issues Mobility issues Coverage issues Interference issues Root-cause analytics and insights Accessibility and load Mobility Coverage Interference Recommendations and actions Accessibility and load Mobility Coverage Interference 36 48 CU DU MT DU MT DU F1 Donor node IAB node Backhaul based on IAB Access link Donor node IAB node IAB Conventional backhaul Access link Backhaul based on IAB IAB node F1 58 Application execution enviro Third-party edge application e.g. image recognition, rendering Devices 5G radio access Edge data Distributed cloud infrastruc Connectivity infrastructure 26
  • 4.
    EDITORIAL ✱ #01 2020✱ ERICSSON TECHNOLOGY REVIEW 7 ✱ EDITORIAL ERICSSON TECHNOLOGY REVIEW ✱ #01 2020 Ericsson Technology Review brings you insights into some of the key emerging innovations that are shaping the future of ICT. Our aim is to encourage an open discussion about the potential, practicalities, and benefits of a wide range of technical developments, and provide insight into what the future has to offer. a d d r e s s Ericsson SE -164 83 Stockholm, Sweden Phone: +46 8 719 00 00 p u b l i s h i n g All material and articles are published on the Ericsson Technology Review website: www.ericsson.com/ericsson-technology-review p u b l i s h e r Erik Ekudden e d i t o r s Tanis Bestland, lead editor (Nordic Morning) tanis.bestland@nordicmorning.com e d i t o r i a l b o a r d Håkan Andersson, Anders Rosengren, Mats Norin, Magnus Buhrgard, Gunnar Thrysin, Håkan Olofsson, Dan Fahrman, Robert Skog, Patrik Roseen, Jonas Högberg, John Fornehed, Kjell Gustafsson, Jan Hägglund, Per Willars and Sara Kullman a r t d i r e c t o r Liselotte Stjernberg (Nordic Morning) p r o j e c t m a n a g e r Susanna O’Grady (Nordic Morning) l ay o u t Liselotte Stjernberg (Nordic Morning) i l l u s t r at i o n s Jenny Andersén (Nordic Morning) s u b e d i t o r s Ian Nicholson (Nordic Morning) Paul Eade (Nordic Morning) i s s n : 0 0 1 4 - 0 17 1 Volume: 101, 2020 AUTOMATIONANDTIGHTINTEGRATION… ARECRITICALTOACHIEVINGCOST-EFFICIENT DEPLOYMENTS ERIK EKUDDEN SENIOR VICE PRESIDENT, CHIEF TECHNOLOGY OFFICER AND HEAD OF GROUP FUNCTION TECHNOLOGY ■ mobile data traffic volumes are expected to increase by a factor of four by 2025, and 45 percent of that traffic will be carried by 5G networks. To deliver on customer expectations in this rapidly changing environment, communication service providers (CSPs) must overcome challenges in three key areas: building sufficient capacity, resolving operational inefficiencies through automation and artificial intelligence (AI), and improving service differentiation. Fortunately, the contents of this issue of ETR magazine provide insights about how to tackle all three. For many operators, the introduction of the 5G System (5GS) to provide wide-area services in existing Evolved Packet System (EPS) deployments isacriticalsteptowardcreatingafull-service,future- proof 5GS in the longer term. Our article on the topic provides an overview of all the aspects that operators need to consider when putting together a robust EPS-to-5GS migration strategy and offers guidance on how to adapt the transition to address a CSP’s specific needs per domain. To cope with the large increase in required bit rate per site and achieve a cost-efficient rollout of 5G New Radio (NR), CSPs also need a good understanding of the different RAN architecture and transport network alternatives available to them. In this issue, we present all the available options and explain why automation and tight integration between the RAN and the transport network are critical to achieving cost- efficient deployments. The surge in data volume that will come from the massive number of devices enabled by 5G has made edge computing more important than ever before. Beyond its abilities to reduce ADDRESSING 5G CHALLENGES TOGETHER network traffic and improve user experience, edge computing will also play a critical role in enabling use cases for ultra-reliable low-latency communication in industrial manufacturing and a variety of other sectors. Our article on the topic explores how to deliver distributed edge computing solutions that can host different kinds of platforms and applications and provide a high level of flexibility for application developers. The integration of AI into current and future generations of cellular access will be critical to achieving Ericsson’s vision of creating a cellular network that constantly adapts itself both to customer requirements and to the static and dynamic characteristics of different scenarios. The AI article in this issue explains how AI can be applied most effectively in three RAN performance improvement domains: network design, network optimization and RAN algorithms. This issue of the magazine also features an article about federated learning (FL) – a smarter, more resource-efficient way for CSPs to ensure consistent QoE. The article demonstrates that it is possible to migrate from a conventional machine learning model to an FL model and significantly reduce the amount of information that is exchanged between different parts of the network, thereby enhancing privacy without negatively impacting accuracy. Ericsson is deeply committed to helping CSPs and other stakeholders understand and plan for the many new 5G NR opportunities that are on the horizon. The significant enhancements to 5G NR in 3GPP releases 16 and 17 are certain to play a critical role in expanding both the availability and the applicability of 5G NR in both industry and public services. Our article on this topic analyzes the most notable new developments in these coming releases, and shares our insights about the future beyond release 17. We hope you enjoy this issue of ETR magazine and we’d be delighted if you shared it with your colleagues and business partners. You can find both PDF and HTML versions of all the articles at: www.ericsson.com/ericsson-technology-review
  • 5.
    8 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 9 ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 2 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 3 Federated learning makes it possible to train machine learning models without transferring potentially sensitive user data from devices or local deployments to a central server. As such, it addresses privacy concerns at the same time that it improves functionality. Depending on the complexity of the neural network, it can also dramatically reduce the amount of data needed while training a model. KONSTANTINOS VANDIKAS, SELIM ICKIN, GAURAV DIXIT, MICHAEL BUISMAN, JONAS ÅKESON Reliance on artificial intelligence (AI) and automation solutions is growing rapidly in the telecom industry as network complexity continues to expand. The machine learning (ML) models that many mobile network operators (MNOs) use to predict and solve issues before they affect user QoE are just one example. ■Animportantaspectofthe5Gevolutionisthe transformationofengineerednetworksinto continuouslearningnetworksinwhichself- adapting,scalableandintelligentagentscanwork independentlytocontinuouslyimprovequalityand performance.Theseemerging“zero-touch networks”are,andwillcontinuetobe,heavily dependentonMLmodels. Thereal-worldperformanceofanyMLmodel dependsontherelevanceofthedatausedtotrainit. ConventionalMLmodelsdependonthemass transferofdatafromthedevicesordeploymentsites toacentralservertocreatealarge,centralized dataset.Evenincaseswherethecomputationis decentralized,thetrainingofconventionalML modelsstillrequireslarge,centralizeddatasetsand missesoutonusingcomputationalresourcesthat maybeavailableclosertowheredataisgenerated. WhileconventionalMLdeliversahighlevelof accuracy,itcanbeproblematicfromadatasecurity perspective,duetolegalrestrictionsand/orprivacy concerns.Further,thetransferofsomuchdata requiressignificantnetworkresources,whichmeans thatlackofbandwidthanddatatransfercostscanbe anissueinsomesituations.Evenincaseswhereall therequireddataisavailable,relianceona centralizeddatasetformaintenanceandretraining purposescanbecostlyandtimeconsuming. Forbothprivacyandefficiencyreasons,Ericsson believesthatthezero-touchnetworksofthefuture mustbeabletolearnwithoutneedingtotransfer voluminousamountsofdata,performcentralized computationand/orriskexposingsensitive information.Federatedlearning(FL),withitsability todoMLinadecentralizedmanner,isapromising approach. TobetterunderstandthepotentialofFLina telecomenvironment,wehavetesteditinanumber ofusecases,migratingthemodelsfrom conventional,centralizedMLtoFL,usingthe accuracyoftheoriginalmodelasabaseline.Our researchindicatesthattheusageofasimpleneural networkyieldsasignificantreductioninnetwork utilization,duetothesharpdropintheamountof datathatneedstobeshared. Aspartofourwork,wehavealsoidentifiedthe propertiesnecessarytocreateanFLframeworkthat canachievethehighscalabilityandfaulttolerance requiredtosustainseveralFLtasksinparallel. Anotherimportantaspectofourworkinthisarea hasbeenfiguringouthowtotransferanMLmodel thataddressesaspecificandcommonproblem, pretrainedwithinanFLmechanismonexisting networknodestonewlyjoinednetworknodes,so thattheytoocanbenefitfromwhathasbeenlearned previously. Theconceptoffederatedlearning ThecoreconceptbehindFListotrainacentralized modelondecentralizeddatathatneverleavesthe localdatacenterthatgeneratedit.Ratherthan transferring“thedatatothecomputation,”FL transfers“thecomputationtothedata.”[1] Initssimplestform,anFLframeworkmakesuse ofneuralnetworks,trainedlocallyascloseas possibletowherethedataisgenerated/collected. Suchinitialmodelsaredistributedtoseveraldata sourcesandtrainedinparallel.Oncetrained,the weightsofallneuronsoftheneuralnetworkare transportedtoacentraldatacenter,wherefederated averagingtakesplaceandanewmodelisproduced andcommunicatedbacktoalltheremoteneural networksthatcontributedtoitscreation. WITH LOW NETWORK FOOTPRINT Privacy-aware machinelearning Terms and abbreviations AI – Artificial Intelligence | AUC – Area Under the Curve | FL – Federated Learning | ML – Machine Learning | MNO – Mobile Network Operator | ROC – Receiver Operating Characteristic
  • 6.
    10 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 11 ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 4 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 5 Figure2illustratesthebasicsystemdesign. Afederationistreatedasataskrun-to-completion, enablingasingleresourcedefinitionofall parametersofthefederationthatislaterdeployedto differentcloud-nativeenvironments.Theresource definitionforthetaskdealsbothwithvariantand invariantpartsofthefederation. Thevariantpartshandlethecharacteristicsofthe FLmodelanditshyperparameters.Theinvariant partshandlethespecificsofcommoncomponents thatcanbereusedbydifferentFLtasks.Invariant partsincludeamessagequeue,themasteroftheFL taskandtheworkerstobedeployedandfederatedin differentdatacenters. Workers(processesrunninginlocaldeployments) aretightlycoupledwiththeunderlyingMLplatform thatisusedtotrainthemodel,whichisimmutable duringthefederation.InourFLexperiments,we selectedTensorFlowtotraintheneuralnetwork, whichisdesignedtobeinterchangeablewithother MLplatformssuchasPyTorch.Communication betweenthemasterandtheworkersisprotected usingTransportLayerSecurityencryptionwith one-timegeneratedpublic/privatekeysthatare discardedassoonasanFLtaskiscompleted. Invariantcomponentscanbereusedbydifferent FLtasks.FLtaskscanrunsequentiallyorinparallel dependingontheavailabilityofresources.Master andworkerprocessesareimplementedasstateless components.Thisdesignchoiceleadstoamore robustframework,sinceitallowsforanFLtaskto failwithoutaffectingotherongoingFLtasks. Faulttolerance Toreducethecomplexityofthecodebaseforboth themasteroftheFLtaskandtheworkersandto keepourimplementationstateless,wechosea messagebustobethesinglepointoffailureinthe designofourFLframework.Thisdesignchoiceis furthermotivatedbytheresearchintocreating highlyscalableandfault-tolerantmessagebusesby combiningleader-electiontechniquesand/orby relyingonpersistentstoragetomaintainthestateof themessagequeueincaseofafailure[4]. Themessageexchangebetweenthemasterofthe FLtaskandtheworkersisimplementedintheform ofassignedtaskssuchas“computenewweights”and “averageweights.”Eachtaskispushedintothe messagequeueandhasadirectrecipient.The recipientmustacknowledgethatithasreceivedthe task.Iftheacknowledgementisnotmade,thetask remainsinthequeue.Incaseofafailure,messages remaininthemessagequeuewhileKubernetes restartsthefailedprocess.Oncetheprocessreaches arunningstateagain,themessagequeue retransmitsanyunacknowledgedtasks. Techniquessuchassecureaggregation[2]and differentialprivacy[3]canbeappliedtofurther ensuretheprivacyandanonymityofthedataorigin. FLcanbeusedtotestandtrainnotonlyon smartphonesandtablets,butonalltypesofdevices. Thismakesitpossibleforself-drivingcarstotrainon aggregatedreal-worlddriverbehavior,forexample, andhospitalstoimprovediagnosticswithout breachingtheprivacyoftheirpatients. Figure1illustratesthebasicarchitectureofanFL lifecycle.Thelightbluedashedlinesindicatethat onlytheaggregatedweightsaresenttotheglobal datalake,asopposedtothelocaldataitself,asisthe caseinconventionalMLmodels.Asaresult,FL makesitpossibletoachievebetterutilizationof resources,minimizedatatransferandpreservethe privacyofthosewhoseinformationisbeing exchanged. ThemainchallengewithanFLapproachisthat thetransitionfromtrainingaconventionalML modelusingacentralizeddatasettoseveralsmaller federatedonesmayintroduceabiasthatimpactsthe accuracyoriginallyachievedbyusingacentralized dataset.Theriskforthisisgreatestinlessreliable andmoreephemeralfederationsthatspanoverto mobiledevices. Itisreasonabletoexpectdatacentersusedby MNOstobesignificantlymorereliablethandevices intermsofdatastorage,computationalresources andgeneralavailability.However,itisimportantto ensurehighfaulttolerance,ascorresponding processesmaystillfailduetolackofresources, softwarebugsorotherissues. Federatedlearningframeworkdesign OurFLframeworkdesignconceptiscloud-native, builtonafederationofKubernetes-baseddata centerslocatedindifferentpartsoftheworld. Weassumerestrictedaccesstoallowforthe executionofcertainprocessesthatarevitaltoFL. Figure 2 Basic design of an FL platform Message bus FL master FL worker 1 FL worker 2 FL worker 3 FL worker N... Figure 1 Overview of federated learning Training (global) Training Inference Data lake (global) Pipelines Data Model distribution Aggregated weights Ericsson Customer Local deployment 1 Training Inference Local deployment 2 Training Inference Local deployment 3 Local storage Local storage Local storage
  • 7.
    12 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 13 ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 6 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 7 Preventivemaintenanceusecase HardwarefaultpredictionisatypicalMLusecase foranMNO.Inthiscase,theaimistopredict whethertherewillbeahardwarefaultataradiounit withinthenextsevendaysbasedondatagenerated intheeight-weekintervalprecedingtheprediction time.TheinputstotheMLmodelconsistofmore than500featuresthatareaggregationsofmultiple performancemanagementcounters,fault managementdatasuchasalarms,weatherdataand thedate/timesincethehardwarehasbeenactivein thefield. Threetrainingscenarios Weperformedtheexperimentsinthreescenarios– centralizedML,isolatedMLandFL. CentralizedMListhebenchmarkscenario.The datasetsfromallfourworkernodesaretransferred toonemasternode,andmodeltrainingisperformed there.Thetrainedmodelisthentransferredand deployedbacktothefourworkernodesforinference. Inthisscenario,allworkernodesuseexactlythe samepretrainedMLmodel. IntheisolatedMLscenario,nodataistransferred fromtheworkernodestoamasternode.Instead, eachworkernodetrainsonitsowndatasetand operatesindependentlyfromtheothers. IntheFLscenario,theworkernodestrainontheir individualdatasetsandsharethelearnedweights fromtheneuralnetworkmodelviathemessage queue.Thesaturationofthemodelaccuraciesis achievedafter15roundsoftheweight-sharingand weight-averagingprocedure.Inthisway,theworker nodescanlearnfromeachotherwithouttransferring theirdatasets. Thepropertiesofeachtrainingscenarioare summarizedinFigure3,TableA. Accuracyresults TableBinFigure3presentstheresultsintheformof medianROCAUC(receiveroperatingcharacteristic areaunderthecurve)scoresobtainedthroughmore than100independentexperiments.Thescores achievedintheFLscenarioaresimilartothose achievedinthecentralizedandisolatedones,while thevarianceoftheFLscoresissignificantlylower comparedwiththeothertwoscenarios. TheresultsinTableBshowthatitisworker1 (south)thatbenefitsfromFL.Theyalsosuggestthat anisolatedMLapproachcanberecommendedin caseswheretheindividualdatasetshaveenough datafortraining.Theonlydrawbackisthatbecause theisolatednodesneverreceiveanyinformation fromothernodes,theywillbemoreconservativein theirresponsetochangesinthedata,withtheriskof potentiallyhigherblindspotsintheindividual datasets. Theimpactofaddingnewworkers Tofacilitatetheaddingofnewworkersatalatertime, informationaboutthecurrentroundmustbe maintainedinthemessageexchangebetweenthe masterandtheworkers.WhenanFLtaskstarts,all workersregistertoroundID0,whichtriggersthe mastertoinitializetherandomweightsand broadcastthesamedistributiontoallworkers.All workerstraininparallelandcontributetothesame traininground.Astheroundsincrease,thefederated model’smaturityincreasesuntilasaturationpointis reached. IfthecurrentroundIDisgreaterthan0,themaster isawarethattheprocessofaveragingofweights hastakenplaceatleastonce,whichmeansthatthe modelisnotatarandominitialstate.Whenanew workerjoinstheFLtask,itsendsitsroundIDas0. Figure 3 Tables relating to the hardware fault prediction use case Centralized Isolated Federated Centralized median (std) Isolated median (std) Federated median (std) Downlink consumption Uplink consumption NoPrivacy preserved Use of overall data Data transfer cost Weight transfer cost Yes Yes 0.91 (0.15)Worker 1 (region 1) 0.89 (0.12) 0.95 (0.05) 0.92 (0.8)Worker 2 (region 2) 0.93 (0.08) 0.93 (0.03) 0.95 (0.16)Worker 3 (region 3) 0.95 (0.13) 0.97 (0.07) 0.97 (0.13)Worker 4 (region 4) 0.97 (0.11) 0.96 (0.05) 0.93 (0.13)Overall 0.93 (0.11) 0.95 (0.05) Federated (MB)Centralized (MB) Table D – Network footprint Table C – Network footprint formulas for each training scenario Table B – ROC AUC scores of workers throughout three scenarios Table A – Summary of scenario definitions FL message size (MB) Rounds Rounds Master 0 0 Worker ID 0 0 Master N * R * Model₀ N * R * Model₀ Worker ID R * Model₀ R * Model₀ i: worker ID N: number of workers R: number of rounds needed until accuracy convergence Model₀: Size of ML model ni : size of dataset in worker ID Worker ID Model₀ ni Master Centralized ML Isolated ML FL ∑ N * Model₀ N i=0 ni Yes No Yes High None None None None Low Workers 19.22,000 0.26 15 4
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    14 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 ✱ PRIVACY-AWARE MACHINE LEARNING 10 ERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 Konstantinos Vandikas ◆ is a principal researcher at Ericsson Research whose work focuses on the intersection between distributed systems and AI. His background is in distributed systems and service-oriented architectures. He has been with Ericsson Research since 2007, actively evolving research concepts from inception to commercialization. Vandikas has 23 granted patents and over 60 patent applications. He has authored or coauthored more than 20 scientific publications and has participated in technical committees at several conferences in the areas of cloud computing, the Internet of Things and AI. He holds a Ph.D. in computer science from RWTH Aachen University, Germany. Selim Ickin ◆ joined Ericsson Research in 2014 and is currently a senior researcher in the AI department in Sweden. His work has been mostly around building ML prototypes in diverse domains such as to improve network-based video streaming performance, to reduce subscriber churn rate for a video service provider and to reduce network operation cost. He holds a B.Sc. in electrical and electronics engineering from Bilkent University in Ankara, Turkey, as well as an M.Sc. and a Ph.D. in computing from Blekinge Institute of Technology in Sweden. He has authored or coauthored more than 20 publications since 2010. He also has patents in the area of ML within the scope of radio network applications. Gaurav Dixit ◆ joined Ericsson in 2012. He currently heads the Automation and AI Development function for Business Area Managed Services. In earlier roles he was a member of the team that set up the cloud product business within Ericsson. He holds an MBA from the Indian Institute of Management in Lucknow, India, and the Università Bocconi in Milan, Italy, as well as a B.Tech. in electronics and communication engineering from the National Institute of Technology in Jalandhar, India. Michael Buisman ◆ is a strategic systems director at Business Area Managed Services whose work focuses on ML and AI. He joined Ericsson in 2007 and has more than 20 years of experience of delivering new innovations in the telecom industry that drive the transition to a digital world. For the past two years, Buisman and his team have been developing a managed services ML/AI solution that is now being deployed to several customers globally. Buisman holds a BA from the University of Portsmouth in the UK and an MBA from St. Joseph’s University in Philadelphia in the US. Jonas Åkeson ◆ joined Ericsson in 2005. In his current role, he drives the implementation of AI and automation in the three areas that integrate Ericsson’s Managed Services business. He holds an M.Sc. in engineering from Linköping Institute of Technology, Sweden, and a higher education diploma in business economics from Stockholm University, Sweden. theauthors
  • 9.
    16 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 17 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 2 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 3 By deploying self-built transport in the RAN area instead of using leased lines, mobile network operators gain access to the full range of 5G New Radio RAN architecture options and minimize their total cost of ownership (TCO). ANN-CHRISTINE ERIKSSON, MATS FORSMAN, HENRIK RONKAINEN, PER WILLARS, CHRISTER ÖSTBERG The 5G evolution is well underway – leading mobile network operators (MNOs) in several regions of the world have already launched the first commercial 5G NR networks, and large-scale deployments are expected in the years ahead. The use of self-built transport in denser areas with a suitable RAN architecture will play a key role in ensuring cost-efficiency. ■Acost-efficient5GNRdeploymentrequires MNOstotakeseveralfactorsintoconsideration. Mostobviously,theyneedtomakesurethatthe5G NRdeploymentcomplementstheirexisting4GLTE networkandmakesuseofbothcurrent4GLTEand new5GNRspectrumassets.Beyondthat,itisvital toconsiderthevariousRANarchitectureoptions availableandthewaysinwhichthetransport networkneedstoevolvetosupportthem,alongwith thelargeincreaseinuserdataratespersite. Whileurbanareaswithhighuserdensitywillbe thefirstpriorityfor5GNRdeployments,suburban andruralareaswillnotbefarbehind.Thesethree areatypeshavedifferentpreconditionssuchas availabletransportsolutions,inter-sitedistance (ISD),trafficdemandandspectrumneedsthatmust betakenintoconsiderationatanearlystageinthe deploymentprocess. Predicted5Gtraffic 5Gisprojectedtoreach40percentpopulation coverageand1.9billionsubscriptionsby2024[1], correspondingto20percentofallmobile subscriptions.Thosefiguresindicatethatitwillbe thefastestglobalrolloutsofar.Thetotalmobiledata trafficgeneratedbysmartphonesiscurrentlyabout 90percentandisestimatedtoreach95percentby theendof2024.Withthecontinuedgrowthof smartphoneusage,totalworldwidemobiledata trafficispredictedtoreachabout130exabytesper month–fourtimeshigherthanthecorresponding figurefor2019–and35percentofthistrafficwillbe carriedby5GNRnetworks. Thegrowingdatademandsformobilebroadband cangenerallybemetwithlimitedsitedensification [2].Therearebenefitstodeploying5GNRmid- bands(3-6GHz)atexisting4Gsites,resultingina significantperformanceboostandmaximalreuseof siteinfrastructureinvestments.Bymeansofmassive MIMO(multiple-input,multiple-output) techniques,suchasbeamformingandmulti-user MIMO,higherdownlinkcapacitycanbeachieved alongwithimproveddownlinkdatarates–both outdoorsandindoors. Deepindoorcoverageismaintainedthrough interworkingwithLTEand/orNRonlowbands usingdualconnectivityorcarrieraggregation. Furtherspeedandcapacityincreasescanbe attainedbydeploying5GNRathighbands (26-40GHz),alsoknownasmmWave.Ifadditional spectrumdoesnotsatisfythetrafficdemand (dueto,forexample,theintroductionoffixed wirelessaccess)densificationwithsolutions suchasstreetsitesmayberequired. Increasinguserdataratesperantennasite Theintroductionofnewspectrumfor5GNRwill increasethecarrierbandwidthsfromthe5MHz, 10MHzand20MHzusedforLTEto50MHzand 100MHzforthemidbands(3-6GHz)and 400/800MHzforthehighbands(24-40GHz), allowingforgigabit-per-seconddataratesperuser equipment(UE).Inurbanareas,thetotalamount ofspectrumwillgrowfromafewtensorhundreds ofmegahertztoseveralhundredorthousand megahertzperantennasite. Simultaneously,trafficdemandspersubscriber willincreaseexponentially.Allinall,thisimpliesthat thebitratedemandsinthebackhaulandfronthaul transportnetworkwillincreasesignificantly(per antennasite,forexample).Thebitratedemandwill bemultiplegigabitspersecond,comparedwiththe fewhundredmegabitspersecondincurrentmobile networks. Thespectrumincreaseperantennasitewillbe lessinsuburbanareas,whileinruralareasrefarming ofcurrentspectrumorspectrumsharingbetween LTEandNRwillbemorecommon.RANtransport networkswillneedtoevolvetoaddresstheincrease inaccumulateduserdatarates,particularlyinurban areas,andinmanysuburbanonesaswell. Transportnetworkoptions EvolvingthetransportnetworkinthelocalRAN areaisanimportantfirststepwhendeploying5G ontopofLTE. Inmostcases,themobilebackhaultransportfor DistributedRAN(DRAN)–thearchitecture traditionallyusedtobuildmobilenetworks–has beenarentedpacket-forwardingservice,Ethernet orIPbased,typicallycalledaleasedlineand providedbytraditionalfixednetworkoperators. Anotheroptioniswhitefiber,anopticalwavelength serviceofferedbymanytraditionalfixednetwork operators. Insteadofleasingatransportservice,some mobileoperatorsdeployself-builttransport solutionsusingmicrowavelinks,whichusually enablesshortinstallationleadtime.Integrated AccessandBackhaul(IAB)isanotheroptionfor self-builttransportin5G.WithIAB,themobile spectrumisalsousedforbackhaul,whichis especiallyrelevantforhigh-frequencybandswhere thebandwidthmaybehundredsofmegahertz. Alternatively,itispossibleforamobileoperatorto deployaself-builttransportsolutionontopof physicalfiber(knownasdarkfiber)thatisavailable forrentfromfixednetworkoperators,ormore recentlyfrompurefibernetworkoperatorsand municipalnetworks.Themobileoperatorthen buildsandownsthetransportequipmentina RANarea,definedasthelocalurbanareainacity andthesuburbanareasclosetocities. Urbanareastendtohavemultiplefibernetwork operatorsthatdeployfibertoeverystreet,which meansthatdarkfiberisreadilyavailableforrent. Whiledarkfiberislesscommoninsuburbanareas, CHOICES THAT MINIMIZE TCO 5GNewRadio RAN&transport TRAFFICDEMANDSPER SUBSCRIBERWILLINCREASE EXPONENTIALLY
  • 10.
    18 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 19 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 4 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 5 infrastructureinvestments.Thebackhaul–thatis, thetransportbetweentheRANandthecore network(CN)–usesanS1/NGinterface[3]. DRANiswellsuitedforuseinallareas(urban, suburbanandrural)andcanusealargevarietyof transportsolutions.DRANreuseslegacy infrastructureinvestments,suchasexistingsites andoperationsandmaintenancestructure,andis ofparticularvalueinareaswherethepopulation densityislowandtheusersarescattered. Theutilizationofstatisticalvariationsintraffic forthedimensioningofself-builtpackettransport intheRANareatransportnetworkisanother benefitofDRAN. Wheredensificationisneededforcoverageor capacity,DRANstreetsitesfitwelltogetherwiththe existingDRANmacrosites.SpecificDRANunits tailoredforstreetsites,denotedasRBUinFigure1, havebenefitssuchasintegratedbasebandfunctions, simpleinstallationandreducedstreetsitespace. CentralizedRAN CentralizedRAN(CRAN)ischaracterizedby centralizedbasebandformultiplepiecesofradio equipment.WithaCRANdeployment,the basebandunitslocatedinacentralsiteandtheradio equipmentlocatedattheantennasitesare interconnectedwithatransportnetwork denominatedfronthaul,eitherCommonPublic RadioInterface(CPRI)orevolvedCPRI(eCPRI)[4]. InareaswithsmallISDsandaccesstodarkfiber (urbanandinsomecasesdensesuburbanareas), centralizingandpoolingthebasebandunitstoan aggregationsitecanbeagoodoption.Theuseof CRANcanleadtoreducedcostsforsitespaceand energyconsumptionattheantennasites,aswellas easierinstallation,operationandmaintenance. CRANprovidesefficientcoordination(via interbandcarrieraggregationandCoMP– coordinatedmultipoint–forexample)between physicallyseparatedantennasites.Italsoenables dimensioningofabasebandpooltohandlemoreand largerantennasitesduetostatisticalvariationsof trafficpersite,whichalsomakesbasebandresource expansioneasierwhentrafficgrowsintheCRAN area.Resilienceandenergyefficiencyareother benefits,asthebasebandpoolservesmanyantenna sites.Thestatisticalvariationoftrafficpersitemay alsobeutilizedinRANareatransportnetwork dimensioning. InenvironmentswhereCRANisdeployed,adark fibertransportsolutionisrequiredforthefronthaul. Theconnectedradiositesalsoneedtobewithinthe latencylimitrequiredbythebasebandunits.Theuse ofdarkfiberisagoodfitwiththenewwideNR frequencybandsandtheexpansionofthefronthaul duetotheuseofadvancedantennasystems[5]. WhendeployingCRAN,itismostbeneficialto connectsitesinthesameareatothesamebaseband pool.Incaseswhereitisdifficulttodeployadark fibertransportsolution,eitheraDRANorahigh- layersplitvirtualizedRAN(HLS-VRAN) architecturemaybedeployedforthosesites, coexistingwithotherCRAN-connectednodes. Toachievethebenefitsofstatisticalmultiplexing oftrafficto/fromtheradioequipmentinthe transportnetworkandinthebasebandpool,itis necessarytouseanEthernet-basedfronthaulsuch aseCPRI[4].Theradioequipmentattheantenna sitesmayeitherhavesupportforeCPRIorinclude aconverterfromCPRItoeCPRI.Itisalsopossible tomixeCPRIandCPRIradioequipment,usingan opticalfronthaultransportsolution,butwithout transportmultiplexinggains. CRANrequiressuitablesites(suchascentral officesites)tocolocatethebasebandunits.Thesize anddensityofthesecentralofficesitesdependson eachsituation,butatypicalcasecouldbecentral officesiteswithanISDoflessthan1kmuptoafew kilometersinanarea. Higher-layersplitappliedasavirtualizedRAN deployment ForbothDRANandCRAN,itispossibletoadda VRANbyimplementinganHLSwherethegNB itsavailabilityissteadilyincreasing.Inruralareas, thereisoftenonlyonefiberoperator,andfiberis onlydeployedtospecificsitessuchasbusinesses andschools.Inthesecases,darkfiberisusuallynot providedasaservice. Ontopofdarkfiber,mobileoperatorscandeploy anoptical(passiveoractive)orapacket-forwarding solution.Thepassiveopticalsolutionusescolored smallform-factorpluggabletransceivers(SFPs)in theendpointsandopticalfiltersinbetweenforadd/ droptosubtendedsites/equipmentalongthefiber path.AnactiveopticalsystemusesgraySFPsinthe endpointsandactiveopticalswitchingequipmentto generatewavelengthsandperformopticalswitching onthesites/equipmentonthefiber.Thepacket- forwardingsolutioncanbeanEthernetorIP solutionwithpacket-forwardingcapabilities onallsites/equipmentalongthefiberpath. RANarchitectureoptions Figure1illustratesDRANalongwiththeother RANarchitectureoptionsavailableforusein5G NR.Theoptionthatismostappropriatefora particulardeploymentwilllargelydependonthe typeofdeploymentarea(urban,suburbanorrural) andtheavailabilityofdarkfiber. Inalloptions,outdoorsitedeploymentscanbe eithermacrosites(typicallymountedonrooftopsor antennamastscoveringalargerarea)orstreetsites (typicallymountedonpoles,wallsorstrands coveringsmallerareasorspots). TheflexibilityoflocatingRANfunctionalityin differentlocationsin5GNRRANarchitectureand theabilitytosupportmoreradiositesincreasesthe needfornetworkautomation,makingitnecessaryto simplifytheinstallation,deploymentandoperation ofboththeRANandtransportpieces.Forexample, theautomationcapabilitiesusedtosimplify installationintheRANmustalsobeintroducedinto transporttoimprovetheinteractionbetweenthetwo. DistributedRAN DRANwithunitaryeNodeBbasestationshasbeen thedominantarchitecturefor4GLTE.DRANwill alsobeacommonlyusedarchitecturein5GNR deployments,withthebenefitofreusingthelegacy Figure 1 RAN architecture deployment options CU DU gNB Antenna/ hub site CU DU RBU Macro site Street siteHLS-RBU HLS-gNB Backhaul Fronthaul CPRI/eCPRI Fronthaul CPRI/eCPRI Backhaul S1/NG Backhaul F1Backhaul S1/NG Backhaul S1/NG Core network Centralized RAN Distributed RAN Distributed RAN + Virtualized RAN (HLS) Centralized RAN + Virtualized RAN (HLS) Small/ street site CU DU Central office site DU Central office site DU Small/ street site CU Data center DU HLS-gNB Antenna/ hub site DU HLS-RBU Small/ street site Backhaul F1 THEUSEOFDARKFIBERIS AGOODFITWITHTHENEWWIDE NRFREQUENCYBANDS...
  • 11.
    20 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 21 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 6 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 7 isdividedintoacentralunit(CU)anddistributed units(DUs).ThisisknownasHLS-VRAN. TheDUsandtheCUareseparatedbytheF1 interface,carriedonabackhaultransportnetwork. ThesearedenotedHLS-gNBformacroand HLS-RBUforstreetsitesinFigure1. Whenacloudinfrastructurealreadyexistsin thenetwork,theHLS-VRANdeploymentmaybe beneficialfromanoperationalandmanagement pointofview.ForaDRANdeployment,adding HLS-VRANcouldresultindualconnectivitygains ifitisexpectedthatitwillbecommonforUEstobe connectedtodifferentbasebandsites. Inareaswhereastreetsitedeploymentisneeded asacoverageorcapacitycomplementtothemacro sitedeployment,astreetHLS-VRANdeployment fitswellwithmacroHLS-VRAN.SpecificHLS- VRANunitstailoredforstreetsites,denotedas HLS-RBUinFigure1,havethesamebenefits astheRBU. 5GNewRadiototalcostofownership Amobileoperator’sTCOfor5GNRintroductionin aRANareaincludesbothcapitalexpenses(one- timecosts)andoperatingexpenses(recurringcosts). Typicalcapitalexpensesincluderadio/RANand transportequipment,siteconstruction,installation costsandsiteacquisition.Typicaloperating expensesincludecostsforaleasedline,darkfiber rental,spectrumforwirelesstransport,siterental, energyconsumption,operationandmaintenance costsandvendorsupport.SincetheRANareatype anddeploymentsolutionalternativesaffecttheTCO, itisusefultocomparetheTCOofthedeployment solutionalternativesindifferentRANareas. BasedonEricssoncustomerpriceinformation andinternalanalysis,Figure2presentstherelative operatorTCOcoveringallcapitalexpensesand operatingexpensesforanurbanlocalRANareaina high-costmarket.Differentregionsandcustomers havevariationsincoststructure.Localdeviations canbesignificant,leadingtoreduceddifferencesbut withthesamerelationintherelativecoststructures. Thelargestcostcomponentsaretransportrentcost, siterental,energyconsumptionandradio/RAN equipment.Thegraphindicatesthatusingself-built transportinthelocalRANareaisamuchmorecost- efficientapproachthanusingaleasedlinetoevery site,bothinDRANandCRANarchitectures.The costdifferenceisespeciallylargeinhigh-costmarkets. Thereasonforthisisthattheintroductionof5G NRsignificantlyincreasestheradiobandwidth comparedwithpreviousgenerations,whichresults inincreasedtransportbitratedemands.While typicaltransportbandwidthtoaradiositeranged from10sto100sofMbpsin2G-4G,itistypicallyup tomultiplegigabitspersecondin5G.Inthelower rangeofthebandwidthscale,thetraditionalleased linecosthasbeenmanageable.Butatsiteswherethe requiredtransportbitratereachesgigabits-per- secondrates,therelativecostfortheleasedline increasesdramatically,accountingforasmuchas 70-80percentoftheRANareaTCO. Thesecondlargestcostinthe“DRANwithleased linetoeverysite”example(andthelargestinthe othertwoexamples)issiterental.Somescenarios willrequiredensificationwithnewsites,whichcould beamixofbothmacrositesandsmallersitetypes (streetsites).However,networkdensificationislikely tofacechallengesduetothehighcostofsiterental andlimitedsiteavailability. Thereare,however,ongoingdiscussionsin severalregionsaboutregulatingthehighsiterental feeforantennasites,whichwouldsignificantly increasetheopportunitytodensifywithnewsites. Thecleartrendoftowercompaniestakingoverthe operationofphysicalsitesandofferingsitesharing mayalsodecreasesiterentcost. RANequipmentandenergyrankasthethirdand fourthlargestcostsinallthreeexamples.Thesecost componentsaredependentonthedeployedRAN architecture.Duetodifferentpricesindifferent marketsandareas,DRANismorecost-efficientin somecases,whileCRANisinothers.Thisexplains whythechoicemaydifferbetweenMNOs. Leasedlineversusdarkfiber Leasedlineisahighvaluetypeofserviceandthefee increaseswiththerequiredbitrate,makingitabig challengefor5GRAN,astheneededtransport bitratesaremuchhigherthaninprevious generations.Whitefiberhasbasicallythesamecost challengesasleasedlines,becauseitisaservicewith aServiceLevelAgreement. Figure 2 Relative operator TCO for 5G NR introduction in an urban local RAN area DRAN leased line to every site DRAN self-built transport in local RAN area Leased line cost Dark fiber rent Site rent RAN equipment Energy All other TCO costs CRAN self-built transport in local RAN area Terms and abbreviations CN – Core Network | CO – Central Office | CPRI – Common Public Radio Interface | CRAN – Centralized RAN | CU – Central Unit | DRAN – Distributed RAN | DU – Distributed Unit | eCPRI – Evolved CPRI | F1 – Interface CU – DU | gNB – GNodeB | HLS – Higher-Layer Split | IAB – Integrated Access and Backhaul | ISD – Inter-Site Distance | LoS – Line-of-Sight | MNO - Mobile Network Operator | NG – Interface gNB - CN | NR – New Radio | RBU – Radio Base Unit | S1–InterfaceeNB-CN| SFP – Small Form-factor Pluggable Transceiver | TCO – Total Cost of Ownership | UE – User Equipment | VRAN – Virtualized RAN USINGSELF-BUILT TRANSPORTINTHELOCAL RANAREAISAMUCHMORE COST-EFFICIENTAPPROACH
  • 12.
    22 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 23 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 8 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 9 Darkfiberrentalalsohasaratherhighcost structure,butthetransportfeeisindependentof bitratesandinsteadbasedonthefiberdistance. DarkfibersolutionsthereforefitwellinRANareas withshortdistancesandarepreferablydeployed,so thatthesamefibercanbeshared,tosomeextent,by multiplesites.Figure3illustratesthedifference betweenatraditionalleased-lineapproachandself- builttransportbasedondarkfiber.Figure4shows whichofthesetwotransportsolutionsismostcost- efficientdependingondataratetositeandsitedistance. Aself-builttransportnetworkbasedondarkfiber maybedeployedwithdifferentfiberandradiosite structuressuchasstar,subtendorringtopology.The mostcost-efficienttopologyissubtending,where multiplesitessharefiber.Ifnetworkresiliencyis required,aringtopologyissuitableattheexpenseof greaterfiberlength.Apurestartopologygives maximumresiliencebuthasthegreatestfiberlength andisthereforethemostexpensivechoice. Figure4illustratesthetypicalfiberlengthpersite, wheretheshortestlengthsappearinurbanareas usingthesubtendingtopology,andthelongest distancesinsuburbanareasusingthestartopology. Figure4alsoshowsthetypicaluserdataratesfor5G. Darkfiberismorecost-efficientthanleasedlinesin denserareaswherethefiberlengthpersiteislow, andthedataratesarehigh.Ifthefiberlength becomeslonger,orthedataratesaresmaller,leased linesaremorecost-efficient. Forthedifferenttechnologyoptionsontopofdark fiber,thepassiveopticalsolutionisthemostcost- efficientself-builtopticalsolution.Thisassumesthat thenumberofsitesandequipmentsubtendedonthe fiberiswithinthescalingofwavelengthsinthe system. Thealternativeself-builtpacket-basedsolution hastheadvantagesofstatisticalmultiplexing throughoutthenetworkandcanbeanL2Ethernet switchedand/orL3IProutedsolution.Itassumes thatallradioequipmentsupportsapacket- forwardinginterface. Alternatively,whendarkfiberisnotavailableor toocostly,wirelesstransportsuchasIABor microwavelinksmaybeused.Theserequireline- of-sight(LoS)ornear-LoS. Conclusion Ouranalysisindicatesthatduetothelargeincrease inrequiredbitratepersitefor5GNR,theuseof traditionalleasedlinesastransporttoeveryradio/ antennasiteintheRANwillbeassociatedwitha highcostindenserareas.Self-builttransportinthe RANareaisasignificantlymorecost-efficient alternativeformobileoperators.Darkfiberis oneself-builttransportalternative;microwave linksisanother. Sincedarkfibercostscaleswithdistancerather thanbandwidth,andthetrendwith5Gistoward shortersite-to-sitedistancesandhigherbitrates, darkfiberwillbesignificantlymorecost-efficient thanleasedlinesinmanyscenarios.Further,the largenumberoffiberprovidershasboosted availabilityandcompetition,resultinginadecrease infiberrentalcostinmosturbanareas,aswellasin somesuburbanones.BeyondtheRANareawhere thelocaltrafficisaggregatedandself-builttransport isterminated,traditionalleasedlineservicestothe mobilecorecontinuetobeareasonablesolution. DistributedRAN(DRAN),whichworkswell overbothfiberandwirelesstransportsolutions, willcontinuetobethedominantdeployment architectureinmostsituations.CentralizedRAN (CRAN)isaninterestingdeploymentarchitecture forregionsorhigh-trafficareaswheredarkfiber transportisavailable.CRANoffersoperational Figure 4 Relative costs for leased lines and dark fiber Dark fiber most cost-efficient Date rate to site (Gbps) 10 5 10 5 1 Typical fiber length per urban/suburban site Typical 5G user data rates Equal TCO Fiber length (km) Leased line most cost-efficient Figure 3 Traditional leased-line approach versus self-built transport in local RAN area Leased-line sites to CN Self-built to aggregation site, leased lines to CN Local RAN area A few hundred meters -> a few kilometers CN CN Agg/ CO
  • 13.
    24 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 25 ✱ 5G NR RAN & TRANSPORT OPTIONS 5G NR RAN & TRANSPORT OPTIONS ✱ 10 NOVEMBER 7, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 7, 2019 11 Ann-Christine Eriksson ◆ is a senior specialist in RAN and service layer interaction at Business Area Networks. She joined Ericsson in 1988 and has worked with research and development within RAN of the 2G, 3G, 4G and 5G mobile network generations. Her focus areas include QoS, radio resource handling and RAN architecture. In her current role, she focuses on evaluating different 5G RAN architecture deployment options with the goal of optimizing RAN efficiency, performance and cost. Eriksson holds an M.Sc. in physical engineering from KTH Royal Institute of Technology in Stockholm, Sweden. Mats Forsman ◆ joined Ericsson in 1999 to work with intelligent networks. Since then he has worked within the areas of IP, broadband and optical networks. Today, his focus is on new concepts for transport within mobile networks at Business Area Networks; one such concept area is 5G RAN transport and automation. Forsman holds an M.Sc. in mathematics and natural science from Umeå University, Sweden. Henrik Ronkainen ◆ joined Ericsson in 1989 to work with software development in telecom control systems but soon followed the journey of mobile systems evolution as a software and system architect for the 2G and 3G RAN systems. With the introduction of HSDPA, he worked as a system architect for 3G and 4G UE modems but rejoined Business Area Networks in late 2014, focusing on analysis and solutions for the architecture, deployment and functionality targeted for the 5G RAN. Ronkainen holds a B.Sc. in electrical engineering from the Faculty of Engineering at Lund University, Sweden. Per Willars ◆ is an expert in network architecture and radio network functionality at Business Area Networks. He joined Ericsson in 1991 and has worked intensively with RAN issues ever since. This includes leading the definition of 3G RAN, before and within the 3GPP, and more lately indoor solutions. He has also worked with service layer research and explored new business models. In his current role, he analyzes the requirements for 5G RAN (architecture and functionality) with the aim of simplifying 5G. Willars holds an M.Sc. in electrical engineering from KTH Royal Institute of Technology. Christer Östberg ◆ is an expert in the physical layer of radio access at Business Area Networks. He joined Ericsson in 1997 with a 10-year background in developing 2G prototypes and playing an instrumental role during the preassessment of 3G. At Ericsson, Östberg began with algorithm development and continued as a system architect, responsible for modem parts of 3G and 4G UE platforms. He joined Business Area Networks in 2014, focusing on analysis and solutions for the architecture, deployment and functionality targeted for the 5G RAN. Östberg holds an M.Sc. in electrical engineering from the Faculty of Engineering at Lund University. theauthors benefitsbypoolingallbasebandtoacentralsite, whichresultsinpotentialcostsavingsinsiterental andenergy,andmaximizestheopportunityfor inter-sitecoordinationfeatures.Incaseswherea networkhasanexistingcloudinfrastructure,the operatormaybenefitfromaddingahigh-layersplit virtualizedRANdeploymenttoaDRANorCRAN architecture. Becausetheflexibilityofthe5GNRarchitecture enablesmuchgreaterdistributionofequipmentand sitesthaneverbefore,itisnecessarytosimplifythe installation,deploymentandoperationofboththe RANanditstransport.Ahighdegreeofautomation andtightintegrationbetweenthetwowillbecritical toachievingcost-efficientdeployments. Further reading ❭ Learn more about building 5G networks at: https://www.ericsson.com/en/5g/5g-networks References 1. Ericsson Mobility Report, June 2019, available at: https://www.ericsson.com/en/mobility-report/reports/ june-2019 2. Ericsson Technology Review, The advantages of combining 5G NR with LTE, November 5, 2018, Kronestedt, F, et al., available at: https://www.ericsson.com/en/ericsson-technology-review/archive/2018/ the-advantages-of-combining-5g-nr-with-lte 3. 3GPP, TS Group RAN; NR; Overall Description; Stage 2, available at: https://portal.3gpp.org/ desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3191 4. CPRI Common Public Radio Interface, available at: http://cpri.info/index.html 5. Ericsson white paper, Advanced antenna systems for 5G networks, available at: https://www.ericsson. com/en/white-papers/advanced-antenna-systems-for-5g-networks ...AUTOMATIONANDTIGHT INTEGRATIONWILLBECRITICAL TOACHIEVINGCOST-EFFICIENT DEPLOYMENTS
  • 14.
    26 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 27 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 2 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 3 Edge computing has great potential to help communication service providers improve content delivery, enable extreme low-latency use cases and meet stringent legal requirements on data security and privacy. To succeed, they need to deliver solutions that can host different kinds of platforms and provide a high level of flexibility for application developers. PÉTER SUSKOVICS, BENEDEK KOVÁCS, STEPHEN TERRILL, PETER WÖRNDLE As well-established, trusted partners that already provide device connectivity, mobility support, privacy, security and reliability, the telecommunications industry and communication service providers (CSPs) more broadly have a competitive advantage in edge computing. This advantage is compounded by their ability to reach out globally to all edge sites with relative ease. ■Themainbenefitofedgecomputingistheability tomoveworkloadsfromdevicesintothecloud, whereresourcesarelessexpensiveanditiseasierto benefitfromeconomiesofscale.Atthesametime, itispossibletooptimizelatencyandreliabilityand achievesignificantsavingsinnetworkcommunication resourcesbylocatingcertainapplicationcomponents attheedge,closetothedevices.Toefficientlymeet applicationandserviceneedsforlowlatency, reliabilityandisolation,edgecloudsaretypically locatedattheboundarybetweenaccessnetworksor on-premisesforlocaldeployments. Sinceitsinventionadecadeago,edgecomputing hasmainlybeenusedtoimproveconsumerQoEby reducingnetworklatencyandpotentialcongestion points tospeedupcontentdelivery.Italsolowers operatorcostsbyreducingpeeringtraffic. Now,asa resultofthesurgeindatavolumethatwillcomefrom themassivenumberofdevicesenabledbyNew Radio,therolloutof5Ghasmadeedgecomputing moreimportantthaneverbefore. Beyonditsabilitiestoreducepeeringtrafficand improveuserexperienceinareassuchasvideo, augmentedreality,virtualreality,mixedrealityand gaming,edgecomputingalsoplaysakeyrolein enablingultra-reliablelow-latencycommunication usecasesinindustrialmanufacturing.Italsohelps operatorsmeetstringentlegalrequirementsondata securityandprivacythataremakingitincreasingly problematictostoredatainaglobalcloud. Edge-computingapplicationswillhavediffering requirementsdependingonwhichdriverhas motivatedthem,andtheywillbebuiltaround differentecosystemsthatutilizeplatformsthatmay beecosystem-specific.Forexample,theplatforms andapplicationprogramminginterfaces(APIs)for smartmanufacturingaredifferentfromthose requiredforgamingandotherconsumer-segment- relatedusecases,whichcanbebasedonweb-scale platformsandAPIs.Arobustedge-computing solutionmustbeabletohostplatformsofdifferent kindsandprovideahighlevelofflexibilityfor applicationdevelopers. Keyfactorsshapingtheedge-cloudecosystem Ontopofbeingabletomeettherequirementsof emerging5Gusecases,thereareotherimportant factorstoconsiderwhendesigninganedge- computingsolution,namely: ❭ Application design trends, life-cycle management and platform capabilities ❭ Expectations on management and orchestration ❭ Edge-computing industry status. Applicationdesigntrends,life-cycle managementandplatformcapabilities Cloud-nativedesignprincipleshavebecomea commondesignpatternformodernapplications– bothfortelecomworkloads[1]aswellasother services.Themodular,microservice-based architectureofcloudnativeapplicationsenables significantefficiencygainsandinnovationpotential whenpairedwithanexecutionenvironmentanda managementsystemdesignedtohandlecloud- nativeapplications. Reuseofgenericmicroservicedesignsacross differentapplicationsandenhancedplatform servicesallowsdeveloperstofocusoncoreaspects oftheservicewithregardtoqualityandinnovation. Next-generation edge-cloud ecosystem CREATING THE Edge computing Edge computing is a form of cloud computing that pushes the data processing power (compute) out to the edge devices rather than centralizing compute and storage in a single data center. This reduces latency and network contention between the equipment and the user, which increases responsiveness. Efficiency may also improve because only the results of the data processing need to be transported over networks, which consumes far less network bandwidth than traditional cloud computing architectures. The Internet of Things – which uses edge sensors to collect data from geographically dispersed areas – is the most common use case for edge computing. Hyperscale cloud providers are extending their ecosystem toward the edge, and as part of the Industry 4.0 transformation enterprises are establishing use-case-specific development environments for their edge. The Cloud Native Computing Foundation [2] is gaining traction across all these development ecosystems, enabling portability of applications to private and public clouds.
  • 15.
    28 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 29 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 4 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 5 differentdomainsofmanagementandorchestration –rangingfromhardwaretovirtualization infrastructuretoradioandcorenetwork applications,togetherwithedge-application platformorchestration–mustallworktogether inanoptimalmanner. Edge-computingindustrystatus Edgecomputingisdependentonfunctionalities inmultipledomains.Forexample,thefirststepin applicationdeploymentistoensurethatruntime isavailableintheappropriateplace,whichputs requirementsontheorchestrationlayerandplacement capabilities,aswellasonbusinessinterfaces. Oncetheruntimeisdeployed,anchoringand connectivityarerequiredtoconfigurethenecessary localbreakoutpointsandsteerthetraffictowhere theedgeruntimerequiresit.Mostofthese functionalitiesarenotspecifictoedgecomputing andhaveeitherbeenaddressedbyindustry standardizationoropensource.Figure1presents themostrelevantstandardizationandopen-source forumsforthird-partyedgeapplications. Onthenetworkingside,the3GPPhasbeen addressingedge-computingrequirementssince release14,bothfromtheconnectivityperspective aswellasfromaserviceandexposureperspective. Addressingedgecomputingunderthe3GPPisthe onlyguaranteetosecurefullcompatibilitywith existingtelecommunicationnetworkdeployments andtheirfutureevolution[3]. Intheimplementationdomain,ETSI(theEuropean TelecommunicationsStandardsInstitute)Network FunctionsVirtualization(NFV)[4]definesthe infrastructure,orchestrationandmanagement, whileTMForumleadsthewayforthedigital transformationofCSPs. WhenitcomestoruntimeandAPIs,the fragmentationoftheusecasesisstandingintheway ofthevisionofoneruntimeandonetypeofAPI. Somedeveloperswilluseawidelyadoptedruntime likeKubernetes,especiallyitsversionscertifiedby theCNCF,orembraceweb-scaleplatforms, whilesomeverticalswillprobablydevelop,orset requirementson,theirownplatformand/orAPIs. The5G-ACIA(5GAllianceforConnected IndustriesandAutomation)consortium[5] isonesuchexample.Acomparableinitiativeinthe automotivesectoristheAECC(AutomotiveEdge ComputingConsortium)[6]. Byutilizingstandardcomponentsand telecommunicationinfrastructurethatisalready Theincreasedamountofindividualsoftware modulesandthedemandtomanagethem efficientlyimpliestheuseofcontainertechnology topackageandexecutethosesoftwaremodules. Kuberneteshasbecometheplatformofchoicefor container-based,cloud-nativeapplicationsinboth thetelecomindustryaswellasforgeneral-purpose services.Northboundmanagementsystemsfor telecomedgeworkloadsaswellasnon-telecomedge workloadsdelegatesomelife-cyclemanagement functionalitytoKubernetes,thusreducing complexityinthosemanagementsystems. TheCloudNativeComputingFoundation (CNCF)ecosystemhasbecomeafocalpointfor developersaimingtobuildmodern,scalablecloud- nativeapplicationsandinfrastructure.Embracinga certifiedKubernetesplatformisthebestwayto becomecompatiblewiththeCNCFecosystemand therebyutilizethespeedofinnovationandvariety ofapplicationsbeingdeveloped. Expectationsonmanagement andorchestration Theprimaryroleofmanagementandorchestration istoassureandoptimizetheapplicationplatform, 3GPP-definedconnectivity,cloudinfrastructureand transport,aswellasensuringtheoptimalplacement oftheedgeapplication. Putinthesimplestterms,edgecomputingisan optimizationchallengeatscalethatconsistsof severaldifferentaspects.Thefirstissupporting theconsumerexperiencebyplacingappropriate functionality–suchaslatency-sensitiveapplications –attheedge.Thesecondaspectisensuringthatthe usersareconnectedtotheseapplications.Thethird aspectisreducingthestressontransportresources andcontributingtonetworkefficiencybyplacing certaintypesofcachingfunctionsattheedge. Whileitmayseemidealfromaperformance perspectivetoplaceallapplicationsattheedge, edgeresourcesarelimitedandprioritizationsmust bemade.Fromanoptimizationperspective,itisvital toplaceonlytheapplicationsthatwillprovidethe mostbenefitattheedge.Determiningthebest locationforthemanagementfunctionality–thatis, theanalyticsfunctionalitythatcanreducetraffic backhaulatthecostoflocalprocessing–isacritical aspectoftheoptimizationprocess.Insomecases, localdeploymentofthemanagementfunctionality maybenecessarytomeetservicecontinuity expectations. Arelatedconsiderationisthelife-cycle managementoftheedgeapplicationsandtheedge applicationplatform,whichmustbeefficiently onboardedfromacentrallocation,distributed andinstantiatedtothecorrectlocations.The responsibilitiesforthiscandifferdepending ontheagreementbetweentheedgeapplication platformproviderandtheCSP.Whendeploying theedgeapplicationsandtheedgeapplication platform,appropriateconnectivitytoboththeradio andthebroadernetworkmustbeestablished. Anedge-computingsolutionmustbeableto managemanydistributededgesitesthateachhave theirownneedsbasedonlocalusagepatterns. Themassivescalethatarisesfromthispresentsa multidimensionalchallenge.Toovercomeit,the Figure 1 Relevant standardization and open-source forums Third-party edge application Application runtime environment (CNCF) Management and orchestration (TM Forum and ETSI) Connectivity infrastructure (3GPP) Distributed cloud infrastructure (ETSI) Terms and abbreviations API – Application Programming Interface | CNCF – Cloud Native Computing Foundation | CSP – Communication Service Provider | DNS – Domain Name System | IoT – Internet of Things | NFV – Network Functions Virtualization | ONAP – Open Networking Automation Platform | UPF –User Plane Function | VNF – Virtual Network Function | WAN –Wide Area Network IT IS VITAL TO PLACE ONLY THE APPLICATIONS THAT WILL PROVIDE THE MOST BENEFIT AT THE EDGE
  • 16.
    30 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 31 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 6 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 7 inplace,aCSPwillbepreparedtohostanytypeof third-partyapplicationorapplicationplatform. Ourhigh-levelsolutionproposal Basedonourunderstandingofthekeyfactors shapingtheedge-cloudecosystem,wehavedefined threemainprinciplesthatunderpinourapproachto edgecomputing: ❭ Reuse industrialized and proven capabilities whenever possible. ❭ Ensure backward compatibility. ❭ Capitalize on existing ecosystems. Thefirstprincipleisareminderthatmanyofthe functionalitiesneededtoenableedgecomputingare notspecifictoedgecomputing.Theyhavebeenused andimprovedovertime,andtheyshouldbereused whereappropriate.Further,thefirstprinciple discouragestheadoptionofhighlyspecialized solutionsearlyintheprocess,inlightofthecurrent marketfragmentationandtheuncertaintiesabout thewinningusecasesinthissegment. Thesecondprinciplehighlightstheimportance ofensuringthatitispossibletodeployexisting applicationsthatwouldbenefitfromedge deploymentwithoutrequiringarewriteonboth thedeviceandbackendsides. Thethirdprinciplepushesustomakethe transferofapplicationsfromacentralcloud totheedgeastransparentlyaspossibletothe developers.Thismeansthereshouldbenochanges tothelife-cyclemanagementoftheapplications,and existingplatforms(alongwithanyspecializedones) shouldcontinuetobeusedforapplication managementandtoprovidetheservicesthe developersneed. Withtheseprinciplestoguideus,weproposea solutionwiththecapabilitiestoonboardedge applicationsandedgeapplicationplatformsintoa CSPenvironment,whichcanbedistributedtothe edgedatacenter,centraldatacenterorpubliccloud. Figure2depictsthehigh-levelarchitecture. Thedark-blueboxesrepresentthemaincomponents ofoursolutionandthepurpleonesindicatethird- partyapplications. Wedesignedthissolutiontomeetfourkey criteria: 1. The solution must be able to host different kinds of platforms for different application types. 2. To harmonize with existing developer communities, the execution environment must be CNCF certified (when it is provided by the CSP). 3. To address scaling and mobility issues, the orchestration and management solution of the runtime environment must be aligned with similar functionalities of the network. 4. The solution must both be compatible with 4G and 5G standards and avoid introducing a new layer of complexity (only simple and necessary APIs should be provided). Thesolutionisbasedonthedistributedcloud infrastructureforvirtualnetworkfunctions(VNFs) andtheETSINFVorchestrationandmanagement functionalities.Thesameorchestrationand managementfunctionsareusedfortheconnectivity infrastructure,distributedcloudinfrastructure, wideareanetwork(transport)orchestrationandthe orchestrationandmanagementoftheapplication executionenvironment.Thisalsoensuresthatthere isauserplanefunction(UPF)availableclosetothe applicationruntimeattherightscalinglevelthatthe sessionmanagementfunctioncanselect. Toenabletransparentconnectivitybetweenthe edgeapplicationandthedevice,theconnectivity infrastructureinoursolutionis3GPPcompatible. Asaresult,noedge-solution-specificenhancements areneededinthedevice. Theexposurefunctionalityprovidesthemain APIstothethird-partydevelopers,ofwhichthere aretwomaintypes.ThefirstsetofAPIsisforthe businessrelationwiththeoperator,toenablethe onboardingandmanagementoftheruntime environmentitselfandtoconfigureandmonitorthe connectivitythroughaggregatedAPIsbuiltontop ofthe3GPP’sservicecapabilityexposurefunction, networkexposurefunctionandoperationssupport systems/businesssupportsystemsAPIs. TheothersetofAPIscanbeexposedto third-partydevelopersforthedeploymentand managementoftheapplicationsthemselves.We proposethat,forthistypeofAPI,aCNCF-certified Kubernetesdistributionshouldbeofferedinaway similartohowitisprovidedonweb-scaleclouds today.Thisapproachharmonizeswiththetrends andprovidesdeveloperswithgreaterflexibility. Runtimeenvironment Toprovideabroadbaselinefortheadoption ofapplicationsattheedge,oursolutionprovides customizableKubernetesdistributioninaddition totheabilitytoonboardarbitrarythird-party runtimeenvironments. OneofthemainbenefitsofKubernetesinmany differentusecasesisitsmodularity.Theplugins availableinitsruntimeenvironmentallowahigh degreeofcustomizationtofitaspecifictypeof workload.Weknow,however,thatindustrial applicationsoftenrelyondedicatedruntime environmentsthatprovidetailor-made characteristics,whichmeansthattheedge willgenerallyconsistofseveraldifferentruntime environments.Asaresult,webelievethatefficient managementofamultitudeofdifferentruntime environmentsisoneofthemostimportant capabilitiesoftheedge-computingsolution. Networkingandconnectivityaspects Networkingrequirementsinedgedeployments aremainlyaboutfacilitatingconnectivitybetween attacheddevicesandcentralservices(traditional networking),attacheddevicesandedgeapplications, andedgeapplicationsandcentralservices. Thedemandsonconnectivitytypicallyvary betweendifferenttypesofedgeapplications–both withregardtothetypeofconnectivityaswellasthe requiredcharacteristics.Theexecutionenvironment, infrastructure,UPFsandmanagementsystemsmust providetherequiredconnectivityservicesflexibly andefficiently. Kubernetesprovidesavarietyofcontainer networkinterfacestomanageconnectivityboth betweenmicroservicesandtoexternalendpoints. Figure 2 High-level architecture of an edge-computing solution for a typical application Application execution environment Third-party edge application e.g. image recognition, rendering Managing the edge application Internet / IntranetWAN Devices 5G radio access Edge data center Operator data center Public cloud or private cloud Consuming connectivity and cloud servicesDistributed cloud infrastructure Management and orchestration Exposure of services Third-party application management functionality Third-party central application e.g. AI training Connectivity infrastructure
  • 17.
    32 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 33 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 8 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 9 Furtherconnectivityfeaturesfornorth-southtraffic areenabledbyingressandegresscontrollers. Theunderlyinginfrastructureisexpectedto providebasiclayer-2andlayer-3connectivityto supporttheKubernetesnetworkinglayer.This significantlyreducesthemanagementcomplexityfor theunderlyinginfrastructureandbypassestheneed tointegratetheKuberneteslayerintoanylowerlayer infrastructuremanagementsystem. Thereareseveraltechnologiesin4Gand5Gto providelocalbreakoutfunctionality.Thepacketcore VNFs(suchastheUPF)canprovidelocalbreakout capabilitiesforthetraffictoberoutedtothe applicationsintheedgelocations.Distributed AnchorPointisagenericsolutionavailabletoday thatsuccessfullyaddressesmanyusecasesand requiresnofurtherstandardization. Lookingfurtherahead,SessionBreakoutisa DomainNameSystem(DNS)-basedsolutionfor dynamicbreakoutthatstillneedsindustry alignment.Itisexpectedtosolveissuesinmany usecases(includingenterprisebreakout). SessionBreakoutcanprovideoptimaltraffic-routing accordingtoaServiceLevelAgreement,forexample. 3GPPstandardizationwillbeneededtoaddress DNS/IPandexposureuse. MultipleSessionsisatargetsolutionthatrequires furtherindustryalignmentandsupportindevices (iOSandAndroid,forexample).Basedonservice- peeringprinciples,itwouldmapapplicationsto specificsessionsontheuserequipmentside, therebymeetingtheneedsofallusecasesalong withoperators’expectationsfornetworkslicing. Networkmanagement,orchestration andassuranceaspects Distributedcloudandedgecapabilitiesrequire thesupportofseverallayersinthenetwork:the transportlayer,thevirtualizationinfrastructure layer,theaccessandcoreconnectivitylayerandthe edgeapplicationlayer.Figure3showshowthese fourlayersfittogetherinthecontextofconsumer devices(ontheleft)anddistributedsites(atthe bottom).Theedge-applicationplatform (theruntimeenvironment)sitsbetweenthe thirdandfourthlayers,supportedbynetwork management,orchestrationandassurance. Severalorchestrationandmanagementaspects mustbeconsidered,particularlywithrespectto edgeapplications(thepurpleboxesinFigure3), theedge-applicationplatform,VNFs(shownasdark grayboxesinFigure3),virtualizationinfrastructure andthedistributionofmanagementfunctionality. Therearetwogeneralapproachestohandling edgeapplications.Thefirstistotreatthemlikean operator’sVNF.Third-partyedgeapplicationsthat willbeexecutedontheedge-applicationplatform requireadifferentapproach.Theseapplications willbecentrallyonboarded,thendistributed, andlife-cyclemanagedbytheedgeplatform. Wheninstantiating,theCSP’sorchestrationand managementwillcreatetheconnectivitytothe consumerdeviceovertheradionetworkaswellas theconnectivitytotheinternet.Theapplication managementandoverallassurancecanbe performedbytheedge-applicationprovider orbytheCSP. Theedge-applicationplatform(s)canbe onboardedandmanagedbytheCSPlikeaVNF, inthesamewaythatanyVNFisonboardedand operatedonanyvirtualinfrastructure.TheCSPwill exposecapabilitiestoinstantiatetheedgeapplication platforminstanceswhenandwhererequired. VNFs(includingcloud-nativeVNFs)are onboarded,designedandlife-cyclemanagedina similarwaytocentraldatacenterdeployments, withtwoadditionalconsiderations:transport betweensitesandappropriatedistributionof VNFstotheedgetooptimizeuserexperience. Thesecapabilitiesalreadyexistinproductsbased onETSIManagementandOrchestration(MANO) [7]and/ortheOpenNetworkAutomationPlatform (ONAP)[8]. Finally,inadistributedenvironment,itcanbe usefultodistributecertainmanagement functionalitiessuchasanalyticsandartificial intelligencefunctionsthatcanperformlocalanalysis anddeliverprocessedinsights,ratherthan backhaulingunnecessarydatatoacentralserver. Inthefuture,orchestrationandconfiguration capabilitiesmayalsobeabletoperformlocalhealing actionstosupporteitherefficientedgeoperationsor edge-servicecontinuity,evenwhencommunication totheedgesitehasbeenlost. Conclusion Edgecomputingwillplayavitalroleinenabling awiderangeof5Gusecasesandhelpingservice providersmeetstringentlegalrequirementsondata securityandprivacy.Beyonditsabilitiestoreduce peeringtrafficandimproveuserexperienceinareas suchasvideo,augmentedreality,virtualreality, mixedrealityandgaming,edge-computingisalso neededtoenableultra-reliablelow-latency communicationusecasesinindustrial manufacturingandavarietyofothersectors.To meetthesediverseneeds,communicationservice providersmustbeabletodeliveredge-computing solutionsthatcanhostdifferentkindsofplatforms andprovideahighlevelofflexibilityforapplication developers. Itisourviewthatsuccessfuldevelopmentofan edge-computingsolutionrequiresasolid understandingoftheusecases,associated deploymentoptionsandapplication-developer communities.Itisofcriticalimportancethatthe solutionisabletoonboardthird-partyapplications and/orapplicationenvironments,utilizingmethods definedbyoperationssupportsystems standardizationbodiessuchasTMForum. Ratherthanbuildinganewapplicationecosystem andplatform,westronglyrecommendreusing industrializedandprovencapabilities,utilizing themomentumcreatedwithCNCF,andensuring backwardcompatibility.Figure 3 Edge deployment and orchestration Edge application platform Edge application layer Virtualization infrastructure layer Access and core connectivity layer Transport layer Distributed sites Business support systems Edge application management Network management, orchestration & assurance IN THE FUTURE, ORCHESTRATION AND CONFIGURATIONCAPABILITIES MAYALSOBEABLETOPERFORM LOCAL HEALING ACTIONS
  • 18.
    34 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 35 ✱ NEXT-GEN EDGE-CLOUD ECOSYSTEM NEXT-GEN EDGE-CLOUD ECOSYSTEM ✱ 10 FEBRUARY 18, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 18, 2020 11 Further reading ❭ Going beyond edge computing, available at: https://www.ericsson.com/en/digital-services/trending/ distributed-cloud ❭ Cloud native applications, available at: https://www.ericsson.com/en/digital-services/trending/cloud-native ❭ How to orchestrate your journey to Cloud Native, available at: https://www.ericsson.com/en/blog/2019/5/ how-to-orchestrate-your-journey-to-cloud-native ❭ Is cloud native design really needed in telecom?, available at: https://www.ericsson.com/en/blog/2019/1/ are-cloud-native-design-really-needed-in-telecom References 1. Ericsson Technology Review, Cloud-native application design in the telecom domain, June 5, 2019, Saavedra Persson, H; Kassaei, H, available at: https://www.ericsson.com/en/reports-and-papers/ericsson- technology-review/articles/cloud-native-application-design-in-the-telecom-domain 2. Cloud Native Computing Foundation (CNCF), available at: https://www.cncf.io 3. 3GPP, 3GPP SA6 accelerates work on new verticals!, June 7, 2019, Chitturi, S, available at: https://www.3gpp.org/news-events/2045-sa6_verticals 4. ETSI, Network Functions Virtualisation (NFV), available at: https://www.etsi.org/technologies/nfv 5. 5G Alliance for Connected Industries and Automation (5G ACIA), available at: https://www.5g-acia.org/ 6. Automotive Edge Computing Consortium (AECC), available at: https://aecc.org/ 7. ETSI, Open Source MANO, available at: https://www.etsi.org/technologies/nfv/open-source-mano 8. Open Network Automation Platform, available at: https://www.onap.org/ theauthors Péter Suskovics ◆ joined Ericsson in 2007 as a software developer and participated in several productdevelopmentgroups through contributor and leader roles. The main technology areas were IP, operations and maintenance, NFV, performance management, 5G and the Internet of Things (IoT). As a strong proponent of open source, Suskovics now works as a system architect in the field of cloud, 5G and the IoT in Business Area Digital Services with a major focus on technology and innovation projects. He holds an M.Sc. in information engineering (2008) and completed his Ph.D.innetworkoptimization (2011) at the Budapest University of Technology and Economics, Hungary. Benedek Kovács ◆ joined Ericsson in 2005 as a software developer and tester, and later worked as a system engineer. He was the innovation manager of the Budapest R&D site 2011-13, where his primary role was to establish an innovative organizational culture and launch internal start-ups based on worthy ideas. Kovács went on to serve as the characteristics, performance management and reliability specialist in the development of the 4G VoLTE solution. Today he works on 5G networks and distributed cloud, as well as coordinating global engineering projects. Kovács holds an M.Sc. in information engineering and a Ph.D. in mathematics fromtheBudapestUniversity of Technology and Economics. Stephen Terrill ◆ is a senior expert in automation and management, with more than 20 years of experience working with telecommunications architecture, implementation and industry engagement. His work has included both architecture definition and posts within standardization organizations such as ETSI, the 3GPP, ITU-T (ITU Telecommunication Standardization Sector) and IETF (Internet Engineering Task Force). In recent years, his work has focused on the automation and evolution of operations support systems, and he has been engaged in open source on ONAP’s Technical Steering Committee and as ONAP architecture chair. Terrill holds an M.Sc., a B.E. (Hons.) and a B.Sc. from the University of Melbourne, Australia. Peter Wörndle ◆ is a technology expert in the area of NFV with responsibility for NFV technology evolution, technology strategy and architecture, as well as cloud-native and edge technologies. Since joining Ericsson in 2007, he has held different positions in R&D and IT, working mainly with cloud and virtualization in R&D, IT operations and standardization. Wörndle holds an M.Eng. in electrical engineering and communication from RWTH Technical University in Aachen, Germany, and currently serves as the vice-chair of the ETSI NFV Technical Steering Committee.
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    36 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 37 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 2 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 3 Artificial intelligence (AI) has a key role to play in helping operators achieve a high degree of automation, increase network performance and shorten time to market for new features. Our research demonstrates that graph-based frameworks for both network design and network optimization can generate considerable benefits for operators. Even greater benefits can be achieved in the longer term through a comprehensive AI-based RAN redesign. FRANCESCO DAVIDE CALABRESE, PHILIPP FRANK, EUHANNA GHADIMI, URSULA CHALLITA, PABLO SOLDATI Advanced 5G use cases and services in areas such as ultra-reliable low latency communications, massive machine-type communications and enhanced mobile broadband place heavy demands on RANs in terms of performance, latency, reliability and efficiency. ■Thewidevarietyofnetworkrequirements,paired withagrowingnumberofcontrolparametersof modernRANs,hasgivenrisetoanoverlycomplex systemforwhichvendorsarefindingitincreasingly difficulttowritemaintenance,operationand fast-controlsoftware.Thereisaclearneedtoboth simplifythemanagementandprovisioningofthe differentservicesandimprovetheperformance oftheservicesoffered. Thetechnicalobjectivesofsimplificationand performanceimprovementcanberoughlymapped tothebusinessobjectivesofreducingoperatingand capitalexpensesrespectively,whichtranslateinto reducedcost-per-byteforcommunicationservice providersandincreasedQoSforconsumers. EmbracingAItechniquesforthedesignof cellularsystemshasthepotentialtoaddressmany challengesinthecontextofbothsimplificationand performanceimprovement[1],makingitpossibleto achievenewobjectivesthatarebeyondthereachof classicaloptimizationandrule-basedapproaches. Intermsofsimplification,AIhasalreadyshown thecapabilitytosignificantlyimprovefunctionalities suchasanomalydetection,predictivemaintenance andthereductionofsiteinterventionsthrough automatedsiteinspectionswithdrones. PerformanceimprovementintheRANisagreater challenge,asitrequiresthereplacementofclassic rule-basednetworkfunctionalitieswiththeir AI-basedcounterparts.Additionalrequirements includeflexibleandprogrammabledatapipelines fordatacollectionandstorage;frameworksforthe creation(training),execution(inference)and updatingofthemodels;theadoptionofgraphical processingunitsfortraining;andthedesign ofnewchipsetsforinference. ThreedomainsforRANperformance improvement ImprovingRANperformanceinvolvesupdatingthe RAN’scontrolparametersacrosstime,frequency andspacetoadapttheRANoperationtobothstatic networkcharacteristics,suchasthe3Dgeometry ofthesurroundingsanddynamicnetworkchanges inchannel,usersandtrafficdistributions.Akey prerequisitetosuccessfullyapplyAIinthiscontext isadeepunderstandingofthenatureandroleof differentclassesofparametersaffectingnetwork performance,aswellasthecomplexityofand potentialtoimproveeachclass. EnhancingRAN performance EMBRACING AI TECHNIQUES FOR THE DESIGN OF CELLULAR SYSTEMS HAS THE POTENTIAL TO ADDRESS MANY CHALLENGES Artificial intelligence Artificial intelligence (AI) has experienced an extraordinary renaissance in recent years. The abundance of data and computational capacity that are available today have finally made decades-old techniques like deep learning practically feasible. Substantial investments from both the public and private sectors have fueled the growth of an ecosystem comprised of libraries, platforms, publications and so on that has propelled the field forward and facilitated access to AI techniques for practitioners in various areas. While the theoretical advances of the AI discipline often occur in domains such as image processing and games, the strengths exhibited by the resulting AI systems – such as the ability to optimize across multiple variables and identify patterns over complex time series – have attracted attention in many industries. In finance, manufacturing and logistics, for example, such capabilities show great potential to improve performance, reduce costs and speed up time to market. withAI
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    38 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 39 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 4 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 5 RANalgorithmsdomain TheRANalgorithmsdomainfocusesonoptimizing theL3toL1controlparametersthatdirectlyaffect thesignaltransmittedto/fromtheuser.Examples includehandoverandconnectivitydecisionsandthe allocationtousersofresourcessuchasmodulation andcodingscheme,resourceblocks,powerand beams.TheL3toL1algorithmsadaptthese parametersonafasttimescale,forindividual networkentities(cellsandUEs,forexample),tothe rapidlychangingenvironmentconditionsinterms ofchannel,traffic,userdistributionandsoon. OurpathtowardAI-basedRANoptimization AnaturalfirststeptowardawideintegrationofAI inRANproductsforperformanceenhancement istheadoptionofAI-basedsolutionsinthenetwork designandoptimizationdomains.Optimizingthe RANbytuningthenetworkhyperparametersis saferandeasierthanredesigningtheRANalgorithms withAI-basedsolutions,asitconsistsofanouter controlloopthatdoesnotmodifytheRANalgorithm designitselfbutonlytunesitsbehavior. Figure2demonstrateshowdifferentnetwork hyperparametervaluesresultindifferentbehaviors fortheunderlyingRANalgorithm,whichare representedbydifferentshapes.However,the performanceimprovementachievablebyAI-based networkoptimizationremainslimitedbythe underlyingdesignoftheRANalgorithmsandthe frequencyatwhichnetworkhyperparametersc anbeadapted,whichaffectstheextenttowhich thesystemcanbecontrolled. AtEricsson,ourlong-termgoalistocreatean all-encompassingAI-basedframeworkthatspans thefullhierarchyofcontrol–thatis,notonly networkdesignandoptimizationbutalso, importantly,AI-basedRANalgorithms. ExamplesofAIapplicationsintoday’snetworks Basedonourlong-standingresearchinthearea ofhowAIcanbeusedtoimproveRANperformance, EricssonhasdevelopedpowerfulAI-based frameworksfornetworkdesignandnetwork optimization,aswellasseveralotherAI-based solutionsforspecificusecases. Figure1illustratesthemaindomainsfor performanceimprovementthatwehaveidentifiedat Ericsson:networkdesign,networkoptimizationand RANalgorithms.Thedomainsarecharacterized basedonthetypeofparametersinvolved,thetype andnumberofnetworkentitiesandthefrequency atwhichupdatestypicallytakeplace. Networkdesigndomain Thenetworkdesigndomainfocusesonimproving theparametersthatdefinenetworkdeployment– suchasthenumberandlocationofnewcells,the associationsofcellstobaseband(BB)units,the selectionofBBunitstoformanelasticRAN (E-RAN)configuration,andsoon.Networkdesign traditionallyreliesonplanningtoolsandthedomain knowledgeofengineersandisperformedrather infrequently,suchaswhennewcellsareadded toanexistingnetwork. Networkoptimizationdomain Thenetworkoptimizationdomainfocusesontuning networkhyperparameters.Whiletheterm hyperparameterhasbeenstronglyassociatedwith machinelearninginrecentyears,itgenerallyrefers toanyparameterusedtocontrolthebehaviorofan underlyingalgorithm.Thehyperparameters ofthealgorithmaretunedtoproduce,forthesame measuredinput,adifferentoutputthatismore appropriateforthegivenscenario. Whilenetworkhyperparametersencompass boththecorenetworkandtheRAN,ourfocus hereisonRANhyperparameterssuchasstatic/ semi-staticconfigurationparametersforcellsand userequipmentaswellasthehyperparameters ofRANalgorithms. Networkhyperparametersareoptimizedto slowlyadapttheRANalgorithmstodifferent networkscenariosandconditionsandbringthe performanceofacertainareaofthenetwork (aparticularclusterofcells,forexample)intoa steadystatewhereinspecifickeyperformance indicators(KPIs)areimproved.Examplesinclude hyperparametersforself-organizingnetworks algorithmsandL3algorithms(mobility,load balancingandsoon)forcoordinationalgorithms (suchascoordinatedmulti-point(CoMP), multi-connectivity,carrieraggregation(CA)and supplementaryuplink),aswellasforL1/L2 algorithms(uplinkpowercontrol,linkadaptation, schedulingandthelike). Figure 1 Main performance improvement domains Domain Parameter type Network entities Update frequency Network design Deployment parameters Basebands, cells, RAN configurations, and so on Monthly/ weekly Network optimization Network hyperparameters Cell clusters/ individual cells Weekly/ daily/hourly RAN algorithms L3 to L1 transmission parameters Cells and user equipment Seconds/ milliseconds Figure 2 Impact of different hyperparameter values on the behavior of the underlying algorithms AI-based network optimization Network hyperparameters L3 to L1 transmission parameters Measurements and reports Rule-based RAN algorithms
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    40 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 41 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 6 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 7 Networkdesignframework Inboth4Gand5G,ourcentralizedRAN(C-RAN) andE-RANinterconnectBBunitstoallowoptimal coordinationacrosstheentirenetworkina centralized,distributedorhybridnetwork architecture.ToensurethatC-RANandE-RAN performanceisinlinewithcustomerexpectations, athoroughnetwork(re)designisrequired.Inthis regard,AItechniquesbasedonadvancednetwork graphmethodologiesareappliedtounderstand andcharacterizethecomplexradionetworkandits underlyingstructures,suchastherelationsbetween cellsandBBunits.Thisapproachleadstoanoptimal designthatmaximizesconsumerthroughput throughoptimizedCoMPandCAtechniques,and thedesignisalsofuture-proofintermsofcapacity andtechnologyexpansions.Thedesigncanbesplit intotwomainsteps. Inthefirststep,withC-RAN,BBoperationis shiftedfromsitelocationtoacentralizedBBhub. TheC-RANdesignthereforefocusesonthe reconfigurationoftheexistingdistributedRAN architecturetoacentralizedarchitecture,where cellsaregroupedinacentralizedhub.Thisisdone insuchawayastocreatetheoptimalcoordination amongcellsbelongingtothesameBBunit, resultinginhigherspectrumefficiencyand improvedconsumerexperience. C-RANconfigurationdesignisahighlycomplex taskanddifficulttosolveusingatraditionalnetwork designapproach.Thisisbecausefindinganoptimal cellgroupingthatmaximizesnetworkperformance amongalargenumberofpossiblecellgrouping combinationsrequiresnumerousaspectstobe considered,suchas: ❭ Intra and inter-frequency cell coverage overlap and neighbor signal strength ❭ Signal quality and diversity to improve coordination techniques ❭ Distance between cells ❭ Frequency band distribution per BB unit ❭ BB capacity design ❭ Future cells/sites deployment. UsinganAI-basednetworkgraphanalysis, naturalandhiddenstructureswithincellrelations (alsoknownascommunities)canbediscovered. Basedonthevariousnetworkindicatorslisted above,thestrengthofeachcellrelationshipcanbe measuredbyaweightfactor.Thehighertheweight factor,themorelikelyitisthatthesecellsshouldbe groupedtogetherintothesameBBunit. Inthesecondstep,E-RANenablesflexible coordinationbetweenBBunitsirrespectiveofthe BBdeployment.SimilartotheC-RANdesign,an AI-basednetworkgraphapproachcanalsobe appliedheretoobtainoptimalBBclustersconsisting ofasetofinterconnectedBBunitsforborderless coordinationacrosstheentirenetwork. Figure3showstheperformanceimprovement ina4GnetworkoperatedbyanAsianoperatorfor threeKPIsafteranautomatedE-RANredesign. Thefirstbargraphindicatesthattheconnections inCAmodeusingthreecomponentcarriers(CCs) increasedby30percent.Themiddlebargraph showsthatthedatavolumecarriedbyanysecondary cellincreasedby22percent,whilethebargraphon therightshowsthatdownlinkcellthroughput increasedby4.3percent.However,themost valuablebenefitisthattheE-RANdesignisentirely automatedandperformedinminutesratherthant hemonthsofworkthatwouldberequired byhumanexperts. Networkoptimizationframework Themonitoringandcontrolofnetworkperformance istraditionallyhandledbyateamofengineers supportedbyexpertsystemstargetedatoptimizing particularareasofthenetwork(typicallyacluster ofcells).Assuch,networkperformanceisoften optimizedbyusingamixofmanualandautomated rule-basedinstructionscombinedwithpredetermined thresholdsforeachnetworkperformancemetric. Theserulesandthresholdsarecompletelybased onhumanobservationsandexpertise. However,oursolutionsdemonstratethatitis possibletocreateafullyscalableandautomated closed-loopAI-basedsolutionfornetwork optimizationconsistingofautomatednetworkdata processing,networkissueidentificationand classification,detailedroot-causereasoning andautomatedparameterconfiguration Terms and abbreviations AI – Artificial Intelligence | BB – Baseband | C-RAN – Centralized RAN | CA – Carrier Aggregation | CC – Component Carrier | CoMP – Coordinated Multi-Point | E-RAN – Elastic RAN | KPI –Key Performance Indicator | L1 – Layer 1 | L2 – Layer 2 | L3 – Layer 3 | RL – Reinforcement Learning Figure 3 Performance improvement of three KPIs after an automated E-RAN design CA configuration 100.00 - 43 - 13.4 - 12.9 - 35 - 60.76 - 48.61 - 15.21 - 11.03 - Baseline AI-based Baseline AI-based Baseline AI-based Secondary cell data volume Downlink cell throughput 30% 1 CC 2 CCs 3 CCs E-RAN DESIGN IS ENTIRELY AUTOMATED
  • 22.
    42 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 43 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 8 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 9 recommendations.Figure4illustratesthe operationsflowforEricsson’snetwork optimizationframework. State-of-the-artunsupervisedandsemi- supervisedlearningtechniquescombinedwith expertdomainknowledgeleadtoanefficient annotationofnormalandabnormalperformance patternsthatcanbeutilizedlaterforissue identificationandclassificationusingsupervised learningtechniques.Byintegratingnetwork topologiesandconfigurationswithhundredsof performancemetricsandtheirtwo-dimensional correlationintimeandspace,itispossibleto generateaknowledgegraphthatrevealsthespecific rootcausesthatleadtoanidentifiednetworkissue. Closingtheautomatedloop,networkparameter changesareautomaticallysuggestedtoresolvethe specificrootcauseandfurtherimproveperformance. AI-basedusecases Anon-exhaustivelistofAI-basedusecasesthat Ericssonhasinvestigatedincludeshandover[2],link adaptation[3],transmissionoptimizationinC-RAN, interferencemanagement,roguedronedetection[4] andfederatedlearninginRANforprivacy awareness[5].Twooftheusecasesthatareof particularinterestinthecontextofRAN optimizationarethepredictionofperformance onasecondarycarrierusingprimarycarrierdata[6] andantennatilting. Secondarycarrierprediction Theuseofbothhigh-frequencybandssuchas 28GHzandhighermillimeter-wavebandswill continuetoincreasein5Gradionetworksandin futuregenerations.Alargernumberofbands provideshighercapacitybutresultsinlarger measurementoverhead.Forinstance,initial deploymentsonthe28GHzfrequencybands willprovidespottycoverage.Foruserstobeableto makeuseofpotentiallyspottycoverageonhigher frequencies,theUEsneedtoperforminter-frequency measurements,whichcouldleadtohighmeasurement overhead.WehaveusedAItechniquestopredict coverageonthe28GHzbandbasedonmeasurements attheservingcarrier(forexampleat3.5GHz). Thisapproachdecreasedthemeasurementsona secondarycarrier,thusreducingtheenergy consumptionandthedelayforactivatingfeatures likeCA,inter-frequencyhandoverandloadbalancing. Antennatilting AI-basedantennatiltingdeservesparticular attentionamongnetworkoptimizationusecases,as itpromisestoenhancethecoverageandcapacityof mobilenetworksbyadjustingbasestationantennas’ electricaltiltbasedonthedynamicsofthenetwork environment.Unliketheconventionalantennatilt approachthatfollowsarule-basedpolicy,AI techniquesenableaself-evolvingpolicy,learning fromfeedbackthroughnetworkKPIs.Using reinforcementlearning(RL),anagentistrainedto dynamicallycontroltheelectricaltiltofmultiplebase stationsjointlysoastoimprovethesignalqualityofa cellandreducetheinterferenceonneighboringcells inresponsetochangesintheenvironment,suchas trafficandmobilitypatterns.Thisresultsinan overallimprovementofnetworkperformanceand QoEfortheuserswhilereducingoperationalcosts. Nextsteps EricssoncontinuestoinvestsignificantR&D resourcesintheuseofAIinallthreeRAN performanceimprovementdomains.Weexpect toseenotableadvancementsinthenetworkdesign andnetworkoptimizationdomainsinthenearterm, whileatthesametimeweareincreasinglyshifting ourfocustothecriticallyimportantRAN algorithmsdomain. Networkdesign Inthenetworkdesigndomain,wearecurrently workingtomakeaspectssuchascell-to-BBand BB-to-BBconnectionssoftwaredefined.This developmentwouldenabletheintegrationof automatedAI-basednetworkdesigninaclosedloop, wherethenetworkcontinuouslyreshapesitsgraph dependingonchangingtrafficpatternsorthe additionofnewnodestothenetwork. Networkoptimization Inthenetworkoptimizationdomain,ournear-term goalistoextendtheframeworktooptimizealarger numberofhyperparametersatahigherupdate frequency.Inthemid-term,weaimtointegratethese newcapabilitiesintoourproductsandultimately makethemanativepartofourproductoffering. RANalgorithms AddressingtheoptimizationoftheRANalgorithms domainisvitaltoourlong-termvisionofcreatingan all-encompassingsingleAI-basedcontrollerthat spansthefullhierarchyofcontrol.Thebenefitof suchacontrollerwouldbetheinherentcapabilityto optimizemultipletransmissionparametersacross layerssimultaneously.Thecreationofacontroller Figure 4 Flow of operations for Ericsson’s network optimization framework Configuration data Data processing Diagnostics Network Optimization Performance data Cell trace data Extract - transform - load Identification and classification Accessibility and load issues Mobility issues Coverage issues Interference issues Root-cause analytics and insights Accessibility and load Mobility Coverage Interference Recommendations and actions Accessibility and load Mobility Coverage Interference ERICSSON CONTINUES TO INVEST SIGNIFICANT R&D RESOURCES IN THE USE OF AI
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    44 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 45 ✱ AI AND RAN PERFORMANCE AI AND RAN PERFORMANCE ✱ 10 JANUARY 20, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 11 withtheabilitytolearndirectlythroughexploration ofthestatespacewouldremovetheboundaries imposedbyhuman-designedalgorithms,makingit possibletoidentifybettercombinationsof transmissionparameterswithinalayerand acrosslayers.Moreover,acontrollerwiththe abilitytolearnfromdatawouldinherently betunedtotheenvironmentandbefree ofnetworkhyperparameters,whichwouldlead tosimplificationofthesoftwarestack. Nonetheless,replacingL3toL1RANalgorithms withasingleAI-basedcontrollerpresentsmore challengesthannetworkdesignandoptimization, onseverallevels.Onechallengeisthatfast parameterchangesintroducetheproblemof transients,andthereforerequiretheAIcontroller topredicttheshort-termstateevolutionofthe systemduetochannelandtrafficchanges,for example,aswellastheactionstheAIcontroller itselfsubmitstothesystem. Anotherchallengeistheneedtoredefinethe radioaccessprobleminawaythatenableslearning throughinteractionwiththeRANenvironment. Today’sdivide-and-conquerapproachforproviding radioaccesstoUEsbybreakingdowntheproblem intomanysubproblemsofmanageablecomplexity, anddesigningspecificsolutionsforeach subproblem,isdifficulttoapplywhenusing AI-basedcontrollers.Inotherwords,stayingwithin thecurrentfragmentedRANframeworkwith differentAI-basedcontrollers,eachtryingto optimizeaRANfeaturewhilelearningthrough interactionwiththesameRANenvironment, wouldpreventthesystemfromlearningand jeopardizesystemperformance. Onepossibleapproachtoaddresssuchchallenges wouldbetoadoptRLastheframeworkofchoicefor RANcontrol.RLhasthenecessarycapabilitiesto dealwithtransients,butitremainschallengingto deployitinthecontextofthecurrentfragmented RANframework.Tothisend,oneapproachwould betoredefinetheproblemanddeviseasolutionwith asinglestageofstateestimationandasinglestageof downstreamend-to-endcontrol.Thisdesignchoice wouldenableastateestimationascloseaspossible tothetruesystemstateandacontrollercapable ofjointoptimizationoverseveraltransmission parameters. Additionally,atrueAI-basedredesignofthe systemwouldrequireend-to-endintegrationofthe differentlayersofthecontrolhierarchyinsuchaway thatslower(higher)layersofcontrol(suchasnetwork design)canmakedecisionstoimproveoverall systemperformanceasafunctionofthemodels learnedforthefaster(lower)layersofcontrol(such asRANalgorithms). Conclusion Interestinartificialintelligence(AI)isgrowing rapidlyinthetelecomindustryasoperatorslookfor waystoautomateRANoperations,boostnetwork performanceandshortenthetimetomarketfornew features.Itisimportanttonote,however,thatthe successfuluseofAItooptimizetheperformance ofaradiocommunicationnetworkrequiresadeep understandingbothofthenatureandroleofthe differentclassesofparametersthataffectnetwork performance,aswellasthecomplexityand optimizationpotentialofeachclass. AtEricsson,ourlong-termaimistoredefinethe overallconceptofradioaccesscontrolwiththeintent tocreateacellularnetworkthatconstantlyadapts itselftothestaticanddynamiccharacteristicsofthe scenariosaswellastherequirementsofthe customers.Tohelpgetusthere,wehaveidentified threemainRANperformanceimprovement domainsbasedonthetypeofparametersinvolved, thetypeandnumberofnetworkentitiesandthe frequencyatwhichupdatestypicallytakeplace. Ourworkdemonstratesthatgraph-based frameworksforbothnetworkdesignandnetwork optimizationcangenerateconsiderablebenefitsin termsofimprovedperformance,simplified managementandshortertimetomarket.Looking furtherahead,weexpectthatthecreationofasingle AIcontrollerthatreplacesRANalgorithmswillplay akeyroleinacomprehensiveAI-basedRAN redesignandultimatelymakeitpossibletoachieve performancetargetsthatareunreachableina traditionalrule-baseddesign. Further reading ❭ Ericsson pioneers machine learning network design for SoftBank, available at: https://www.ericsson.com/ en/press-releases/2018/5/ericsson-pioneers-machine-learning-network-design-for-softbank ❭ How will AI enable the switch to 5G?, available at: https://www.ericsson.com/en/networks/offerings/ network-intelligence-and-automation/ai-report ❭ Towards zero-touch networks, available at: https://www.ericsson.com/en/ai-and-automation ❭ Ericsson launches unique AI functionality to boost radio access networks, available at: https://www.ericsson.com/en/news/2019/10/ericsson-ai-to-boost-ran ❭ AI in 5G networks: Highlights from our latest report, available at: https://www.ericsson.com/en/ blog/2019/5/ai-in-5g-networks-report-key-highlights ❭ Automated network operations, available at: https://www.ericsson.com/en/digital-services/offerings/ network-automation ❭ How to connect the dots of future network AI, available at: https://www.ericsson.com/en/blog/2019/7/ connect-the-dots-of-future-network-AI ❭ An introduction to machine reasoning in networks, available at: https://www.ericsson.com/en/ blog/2019/11/machine-reasoning-networks-introduction ❭ Supercharging customer experience through AI and automation: The inside view, available at: https://www.ericsson.com/assets/local/managed-services/ai-automation-report-screen-aw.pdf References 1. Ericsson, Employing AI techniques to enhance returns on 5G network investments, 2019, available at: https://www.ericsson.com/49b63f/assets/local/networks/offerings/machine-learning-and-ai-aw-screen.pdf 2. Cornell University, submitted to IEEE Globecom 2019, ArXiv preprint arXiv:1904.02572, 5G Handover using Reinforcement Learning, Yajnanarayana, V; Rydén, H; Hévizi, L; Jauhari, A; Cirkic, M, available at: https://arxiv.org/abs/1904.02572 3. In Proceedings of the 2019 Workshop on Network Meets AI & ML (NetAI'19) Pages 44-49, Contextual Multi-Armed Bandits for Link Adaptation in Cellular Networks, 2019, Saxena, V; Jaldén, J; Gonzalez, J E; Bengtsson, M; Tullberg, H; Stoica, I, available at: https://www.researchgate.net/publication/335183811_ Contextual_Multi-Armed_Bandits_for_Link_Adaptation_in_Cellular_Networks 4. Cornell University, ArXiv preprint arXiv:1805.05138, Rogue Drone Detection: A Machine Learning Approach, 2018, Rydén, H; Redhwan, S B; Lin, X, available at: https://arxiv.org/abs/1805.05138 5. Ericsson Technology Review, Privacy-aware machine learning with low network footprint, October 21, 2019, Vandikas, K; Ickin, S; Dixit, G; Buisman, M; Åkeson, J, available at: https://www.ericsson.com/en/ reports-and-papers/ericsson-technology-review/articles/privacy-aware-machine-learning 6. IEEE, In Proceedings of the 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Predicting strongest cell on secondary carrier using primary carrier data, Ryden, H; Berglund, J; Isaksson, M; Cöster, R; Gunnarsson, F, available at: https://ieeexplore.ieee.org/document/8369000
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    46 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 ✱ AI AND RAN PERFORMANCE 12 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 20, 2020 theauthors Francesco Davide Calabrese ◆ joined Ericsson in 2017 as a concepts researcher. In his current role he works on concepts that redefine wireless communications through AI. Prior to joining Ericsson, he worked as a researcher at Nokia and Huawei. He holds a Ph.D. in wireless communication from Aalborg University in Denmark. Philipp Frank ◆ joined Ericsson in 2014. In his current role, he heads AI development for network design and optimization within Ericsson’s Managed Services business area. He holds a Ph.D. in electrical engineering and information technology from the University of Stuttgart, Germany, and a certificate from the executive program AI – Implications for Business Strategy from the Massachusetts Institute of Technology. Euhanna Ghadimi ◆ joined Ericsson in 2018 where he works with AI concepts for future radio access products in Business Area Networks. Prior to joining Ericsson, he was employed at Huawei as a 5G networks researcher and at Scania, where his work focused on AI solutions for connected vehicles. Ghadimi received a Ph.D. in telecommunications from KTH Royal Institute of Technology in Stockholm, Sweden, in 2015. His research interests are in the areas of optimization theory, machine learning and wireless networks. Ursula Challita ◆ joined Ericsson Research as a researcher in 2018, the same year she received a Ph.D. in machine learning for radio resource management at the University of Edinburgh, UK. She was a visiting research scholar at Virginia Tech in the US from 2016 to 2018. Her research interests include machine learning, optimization theory and wireless cellular networks. Pablo Soldati ◆ joined Ericsson in 2018 as a standardization and concepts researcher for 5G New Radio and AI. He received a Ph.D. in wireless communications from KTH Royal Institute of Technology in Stockholm, Sweden, in 2010. He was a postdoctoral scholar at KTH and a visiting postdoctoral scholar at Stanford University before joining Huawei in 2011, where he served as a principal researcher.
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    48 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 49 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 2 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 3 For most mobile operators, the introduction of the 5G System (5GS) will be a migration from their existing Evolved Packet System (EPS) deployment to a combined 4G-5G network that provides seamless voice and data services. This migration requires a carefully tailored, holistic strategy that includes all network domains and considers the operator’s specific needs per domain. RALF KELLER, TORBJÖRN CAGENIUS, ANDERS RYDE, DAVID CASTELLANOS Introducing the 5GS to provide mobile broadband (MBB) in an existing 4G EPS network has significant impacts across all network domains – from the RAN, to packet core, user data and policies, and services – as well as backend systems. ■ TheEPSisprimarilyusedtodayforavarietyof MBBusecases.Insomecases,EPSdeployments havealreadybeenupgradedforearlysupportof5G bynon-standalone(NSA)NewRadio(NR).Many such4GandNSANRoperatorshavealready decidedtointroduce–orareconsidering introducing–the5GSasstandardizedbythe3GPP. The5GSintroducessupportforNRstandalone (SA)[1]andisspecifiedtosupportexistingMBB usecasesaswellasnewandimprovedones. Ericssonbelievesthatoperatorscanaddress increasingtrafficdemandsandquicklyintroduce innovativenewservicesbyusingNSANR andSANR[2]. DuringthemigrationperiodwhenNRcoverage isbeingbuiltout,servicesrequiringwide-area coveragearebestsupportedthroughinterworking betweenthe5GCore(5GC)andtheexisting EvolvedPacketCore(EPC).Overtime,agrowing numberofnewusecaseswillutilizetheestablished 5GSMBBscaleandwide-areanetworkbuild-out. TheinterworkingwiththeEPCplaces dependenciesonthebackendbusinesssupport systems(BSS)systemintegration,sinceuserdata andpoliciesneedtosupporttwonetworks(theEPC and5GC).Thenewdevicesmustsupport5GS-related capabilities,whileatthesametimedevicesthatonly supporttheEPS–includinginboundroaming devices–willexistforalongperiodandwillrequire correspondingnetworksupport.Thislong-term needisastrongargumentinsupportoftheconcept ofadual-modecorenetworksolutionthatincludes bothEPCand5GCfunctionality. Akeybenefitofadual-modecorenetwork solutionisthecommonoperationalmodelforthe EPCand5GC,whichsimplifiesthemanagementof theoverallsystem.Italsoincludesmoregranular life-cyclemanagementoftheindividualsoftware modules(alsocalledmicroservices)basedoncloud- nativedeploymentandoperationalprinciples[3]. Thecommonoperationalmodelcanbeusedfor dynamicandflexiblescalingofindividualmicro- servicesbasedoncapacityneeds,suchasrebalancing theEPCversus5GCresourceusagewhenthedevice fleetevolvesovertimefrom4Gto5G. 5GSarchitectureforinterworkingwithEPS Introducingthe5GStoanetworkrequiresa comprehensivestrategythatconsidersallnetwork domains,coveragestrategy,spectrumassetsand devices,aswellaswhichservicestoofferwhere. Amongotherthings,the5GC,thepacketcoreinthe 5GS,introducesnewnetworkfunctionsand interfacesinternallyandtowardoperationssupport systemsandBSS,includingchargingsystems.Italso hasnewinterfacesandprotocolstowardthenext- generationRAN(NG-RAN)anddevices,which meansthattheRANmigration,includingspectrum assetsanddevicestrategies,needstobecoordinated withthe5GCintroduction. Additionally,anyplantointroducethe5GC mustconsideritsnewservice-basedarchitecture, whichincludesanetworkrepositoryfunctionfor serviceregistrationanddiscovery,aswellasnew capabilitieslikenetworkslicingsupportand networkexposure. OperatorswithbothNRandLTEaccessareable touse5GCcapabilitiesfortightinterworkingtothe EPS,alsoknownasEPC-5GCtightinterworkingin thefirstreleaseof5Gspecificationsinthe3GPP[4]. 5Gmigration strategy FROM EPS TO 5G SYSTEM Terms and abbreviations 5GC – 5G Core | 5GS – 5G System | AMF – Access and Mobility Management Function | BSS – Business Support Systems | CA – Carrier Aggregation | CAS – Customer Administration System | CP – Control Plane | CUPS – Control Plane User Plane Separation | EPC – Evolved Packet Core | EPS – Evolved Packet System | E-UTRAN – Evolved Universal Terrestrial Radio Access Network | FDD – Frequency Division Duplex | gNB – NR Node B | HSS – Home Subscriber Server | HTTP – Hypertext Transfer Protocol | IMS – IP Multimedia Subsystem | MBB – Mobile Broadband | MME – Mobility Management Entity | NAS –Non-AccessStratum|NG-RAN –Next-GenerationRAN |NR–NewRadio|NSA–Non-Standalone| PCF – Policy Control Function | PCRF – Policy Control and Charging Rules Function | PDN – Packet Data Network | PDU – Protocol Data Unit | PGW – Packet Data Network Gateway | REST – Representational State Transfer | SA – Standalone | SGW – Serving Gateway | SMF – Session Management Function | SMSoIP – SMS-over-IP | SMSoNAS – SMS-over-NAS | SPR – Subscription Profile Repository | TDD – Time Division Duplex | UDM – Unified Data Management | UDR – Unified Data Repository / User Data Repository | UE – User Equipment | UP – User Plane | UPF – User Plane Function
  • 26.
    50 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 51 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 4 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 5 AMF,butnotinbothsimultaneously).TheN26 referencepointisusedforbothidlemodeand connectedmodemobility;thedeviceinitiatesidle modemobility(possiblytriggeredbytheRAN), whileconnectedmodeisinitiatedbytheRAN,and thedeviceisinformedwhenhandoverpreparation hasbeencompleted.Furthermore,tightinterworking alsoincludeshowtomapprotocoldataunit(PDU) sessionsinthe5GStopacketdatanetwork(PDN) connectionsintheEPSandviceversa. Theinterworkingarchitectureensuresthatthe new5GC-capabledevicesarealwaysconnectedto theUPFinthe5GCindependentlyiftheyare connectedthough4Gor5Gaccess,whichenables IPaddresspreservationwhendevicesmove betweenaccesses.Thus,servicecharacteristicsare maintained,sinceanycolocatedvalue-added servicesconnectedtotheUPFarethesame, andthepolicycontrolfunction(PCF)applies sessionpoliciesforthedevicewhenconnecting over4Gor5Gaccess. Policyandsubscriptionmanagementneedtobe providedinaconsistentwayforadeviceusing NRorLTEaccess.Theinterworkingarchitecture providesseveral5GCcapabilitiesalsooverLTE/ EPC,includingsupportfornetworkslicing. OperatorscanmigratefromanexistingEPC toadual-modeEPCand5GCnetworksolutionby migratingthepacketgateway,thepolicycontrol andthesubscriptionanddatamanagement,andby introducingnewfunctionality. Overcomingdomain-specific migrationchallenges AsolidEPSto5GSmigrationstrategywillconsider andaddressthechallengesindevicesandallfour networkdomains:RAN,packetcore,userdataand policies,andservices. TheRANdomain Inmanymarkets,thenewNRspectrumisfirst availableinmidandhighbands.Dependingonthe sitegrid,introducingtheNG-RANwithNRSA onlyonthesebandsmayleadtospottyNRcoverage thatisonlysuitableforlocalareaservices.When deployingNRSAforMBB,itispreferabletoensure continuousNRcoveragewithinthetargetedservice area(acity,forexample)toavoidfrequentmobility betweenNRandLTE.Alternatively,intersystem mobilitycouldbelimitedbyconservativemobility thresholds. Achievinghighercapacityandcontinuous coveragerequiresacombinationofNRonmidand highbandsforcapacityandNRFDDonsufficiently lowbandsforcoverage[2].TheNRFDDspectrum onlowbandcanbeeithernew,re-farmedoran existingLTEbandthatissharedbetweenNRand LTEusingdynamicspectrumsharing.NRbands canbecombinedusingcarrieraggregation(CA)or, insomecases,dualconnectivity. NRCAwillbevitalinenablingserviceproviders toservethegrowingnumberof5Gdevicesinthe networkwhilemaintainingoverallnetwork performanceanduserexperience.Thisisdoneby activatingdownlinkCA(FDD+TDD)intheareas withlow-bandandmid-bandNR.Thisnotonly boostsmid-bandNRcoverage,andconsequently capacitygain,butalsoprovidesafurthercoverage boostbyenablingsomeoftheNRsignalingtobe movedtolowerbands.Ericssonhasshownthatthis canprovideupto3-7dBextragaininlinkbudget onthedownlink[8]. NRSAwithCAreducescomplexityintheRAN anddevicescomparedwithdualconnectivityasin NRNSA.Thedevicedoesnotneedtotransmiton twouplinksatthesametime.TheuseofNRSA alsoreducesthetimefromadevicebeingininactive modetofullNRcapacity,enabledbyallcontrol signalingbeingcarriedoverNRinsteadofbeing dependentonLTEandthesetupofdual connectivity.Thebenefittotheconsumerisfaster accesstothefullpotentialofthecombinedNR capacitywhen,forexample,downloadingafile orstartingupavideo. Figure1showsthe5GSarchitectureforEPC-5GC tightinterworking.ToenableIPaddress preservationwhenconnectingoverandchanging between4Gand5Gaccess,the5GCarchitecture includesacommonuserplane(UP)anchorpoint realizedbythesessionmanagementfunctionplus packetdatanetworkgatewaycontrolplanefunction (SMF+PGW-C)andtheuserplanefunctionplus PGWuserplanefunction(UPF+PGW-U). Tosupportseamlessservicecontinuityand network-controlledhandover,theMobility ManagementEntity(MME)andthenewaccess andmobilitymanagementfunction(AMF)interact directlythroughtheN26referencepoint,which supportsdevicesinsingle-registrationmode(the deviceiseitherregisteredintheMMEorinthe Figure 1 EPC-5GC tight interworking architecture Services HSS UDM PCF Internet and data services = signaling = user plane (or combined) = legacy 4G components = new 5GS components AMFMME SGW E-UTRAN NG-RAN IMS SMF+ PGW-C UPF+ PGW-U User data and policies Packet core RAN Devices POLICYAND SUBSCRIPTIONMANAGEMENT NEED TO BE PROVIDED IN A CONSISTENT WAY
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    52 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 53 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 6 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 7 Tosupportexistingandforthcomingservices likevoiceandemergencywhenmigratingMBB tothe5GS,theNRSAneedstosupportcapabilities andcoveragedemandslikeintersystemhandover, positioningandQoS.Thiscanbeastepwisemigration. Theuserdataandpoliciesdomain TheEPC-5GCtightinterworkingarchitecture showninFigure1assumescommonsubscription managementsupportregardlessoftheaccess technologyusedbyagivenuser.Althougha combinedHSS/UDM(HomeSubscriberServer/ UnifiedDataManagement)functionisdepicted, firstdeploymentssupportsubscriptionmanagement fortheEPCand5GCbyinterworkingbetweena separateHSSandUDMthroughanHTTP/REST (HypertextTransferProtocol/Representational StateTransfer)interface. ExistingHSSfunctionalitymustbeevolvedto enableinterworkingwithUDMandtosupport tightinterworkingbetweentheEPCand5GC. TheevolutionincludesanupgradeoftheHSS functionalityofferedtoEPCservingnodeswith, amongotherthings,subscriptionparametersthat enableuseraccesstothe5GCtoensureIPsession continuityandsingleregistrationacrossthe EPCand5GC. Oncethe5GCforMBBisintroduced,sessions areanchoredintheSMF+PGW-Cfunction. TheuseofSMF+PGW-Callowsthepolicycontrol andchargingrulesfunction(PCRF)usedforpolicy controlintheEPCtobereplacedbyanew dual-modepolicymanagementsystemthat supports5G-enableddevicesregardlessofthe accesstechnologycurrentlyused. HSS/UDMandPCF/PCRFbusinesslogic arealsohighlydependentonhowsubscription dataandpolicysubscriptiondataismanaged (thatis,provisioned,storedandaccessed). TheEPCallowsthesupportofadata-layered architecture,wheretheHSSandPCRFmakeuse ofanexternaldatabasetomanagesubscriptiondata. ThesedatabasesareknownastheUserData Repository(4G-UDR)forHSSsubscriptiondata andtheSubscriptionProfileRepository(SPR) forPCRFpolicysubscriptiondata.The5GC generalizestheconceptintoaUnifiedData Repository(5G-UDR).The5G-UDRstores subscription,policysubscription,application andexposuredata. Duringtheintroductionofthe5GCitisbeneficial todeploynewdual-modesubscription,dataand policymanagementsystemsthatsupportthe EPC-5GCtightinterworkingarchitectureand proceduresasdepictedinFigure2. Thedual-modedatamanagementsystemenables bothsubscriptiondatacentralizationandsingle pointofprovisioninginnew5G-UDRinstances for5G-enabledsubscribers.Thisrequires subscriptiondatamigrationfromthelegacy 4G-UDR/SPRtothe5G-UDR.Thismigration processcanbeassistedbyautomaticmigration toolsand/orbyauto-provisioning/activation mechanismsenabledbynotificationstothe BSS/CAS(customeradministrationsystem), suchasthedetectionofa4G-onlyuserusinga 5G-capabledeviceand/orbeingactiveinthe5GC. Asafirstmigrationstep,theHSSfunctionality inthelegacysubscriptionmanagementsystemmay stillbeusedfor5G-enableduserswhentheyconnect throughtheEPC.TheexistingHSSinstancesmay reachsubscriptiondatafor5G-enabledusersfrom the5G-UDRthroughthelocal4G-UDR/SPRusing proprietaryinterfaces,forexample.ThePCFinthe dual-modepolicymanagementsystemsupports 5G-enableduserseveniftheyconnectthrough theEPC. Alternatively,5G-enabledusersmaybeserved byHSSfunctionalityinthedual-modesubscription managementsystemwhentheyconnectthrough theEPC.Thedual-modesubscription,dataand policymanagementsystemsfor5G-enabled subscriptionscancoexistwiththeexisting subscription,dataandpolicymanagementsystems forlegacy4Gsubscriptions(existingHSSand PCRFinstancesand4G-UDR/SPR)withthe supportofasignalingroutingfunction. Ineithercase,thegoalisthatalltypesof subscriptions(evenlegacy4G-onlysubscriptions) aremanagedonthenewdual-modesystemfor subscription,dataandpolicymanagement,asshown totherightinFigure2.Thedesignofthissolution followstheprinciplesofacommonoperational modelbasedoncloud-nativeimplementationand offersitsbenefitstomanagebothnew5G-enabled subscribersregardlessoftheaccesstechnology usedandlegacy4Guserswhoconnectonly throughtheEPC. Thepacketcoredomain Accordingtoourdefinition,thepacketcoredomain includesfunctionalitytohandleaccessandmobility management(MME,AMF),sessionmanagement, theserving-gateway-controlplanefunction (SGW-C),PGW-C,SMFandUPfunctionality (SGW-U/PGW-U,UPF). Theinitialintroductionofthe5GCforwide-area servicesallowsamigrationofRANandcore independentlyofeachother,similartothetransition from3Gto4G.Priortothestandardizationrelease of5G,the3GPPalsostandardizedaseparationof thegatewayfunctionsintheEPCintocontrolplane (CP)andUP,knownasCUPS.Thisseparation enablesnewopportunitiesforUPdistribution andedgebreakoutoftrafficalreadyintheEPC. SeparatePDNconnectionscanusedifferent SGW-U/PGW-Usincentralandlocaldeployments, forexample.ThefunctionalseparationoftheCP andUPintheEPCCUPSiscarriedoverinthe5GC architecturecorrespondingtotheSMFandUPF. TheavailabilityofbothCUPSandEPC-5GC tightinterworkingenablesmultiplepossible migrationpathsfromtheEPCtothe5GC. OneoptionistofirstintroduceCUPSintotheEPS, whichenablestheoperatortousetheCPandUP separationbeforemigratingtothe5GS.Thiscan bebeneficialtohandleincreasedtrafficdemand whenintroducingNRNSAandpreparefor asmoothmigrationtothe5GCbasedona UPimplementationthatsupportsboththe EPCand5GC. AnotheroptionistointroduceCUPSatthe sametimeasthe5GCbycolocatingSMF+PGW-C withSGW-CfunctionalityandUPF+PGW-U withSGW-Ufunctionality.Thisallowsthenew high-capacity5GdevicestobeservedbyaCP andUPsplit-gatewayarchitecturewhenconnecting overeither4Gor5Gaccess,asshowninthemiddleFigure 2 Migration to dual-mode subscription, data and policy management systems BSS/CAS Legacy subs, data and policy management Legacy subs, data and policy management Dual-mode subs, data and policy management Dual-mode subs, data and policy management HSS PCRF HSS PCRF HSS/UDM PCF HSS/UDM PCRF/PCF 4G-UDR /SPR 4G-UDR /SPR 5G-UDR 5G-UDR BSS/CAS BSS/CAS THE DESIGN OF THIS SOLUTION FOLLOWS THE PRINCIPLES OF A COMMON OPERATIONAL MODEL BASED ON CLOUD-NATIVE IMPLEMENTATION
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    54 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 55 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 8 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 9 partof Figure3.Anadditionalbenefitofthis colocationisthepossibilityformoreflexible distributionoftheUPindifferentlocations, forexampletoaddresslow-latencyservicesby placingtheUPclosertotheRAN.The5GC tightinterworkingcanalsointerworkwitha legacySGWasshowninFigure1. ThemiddlepartofFigure3alsoshowsthatitis possibletocontinuetousetheexistingSGWand PGWfunctionalityintheEPCforlegacydevices with4G-onlysubscriptionswhenintroducing the5GC,whichminimizestheimpactonexisting servicesandsubscribers.However,theMME needstosupportthegatewayselection(SGW-C& SMF+PGW-C)fordeviceswith5GCsubscription and5GC-NAS(non-accessstratum)capability. Thelatteriscommunicatedaspartofthetracking areaupdate/attachprocedureintheEPS. TheMMEcanalsoutilizeadditionalmethods toassistinthegatewayselection,suchasdedicated AccessPointNamesorDomainNameSystem lookupenhancements.TheMMEmayalsoneedto supportrestrictions,suchasiftheoperatorwantsto preventmobilitytothe5GSforcertainsubscribers. ThenextmigrationstepistocombinethefullEPC and5GCfunctionalityintoadual-modepacketcore, serving5G-enabledsubscribersregardlessofthe accesstechnologyusedandlegacy4Gusersthat connectonlythroughtheEPC,asshownontheright sideofFigure3.Inthisexample,thenew5GC devicesareservedbytheSMF+PGW-C,whilethe legacy4G-onlydevicesarestillservedbyaseparate PGWinstance.Otherdeploymentmodelsare alsoconceivable. Thegoalofthemigrationisthereforeasolution thatfollowstheprinciplesofacommonoperational modelbasedoncloud-nativeimplementationand offersitsbenefitstobothnew5G-enabled subscribersregardlessoftheaccesstechnology usedandtolegacy4Gusers,whoconnectonly throughtheEPC. Whenintroducingthe5GS,itisalsoimportant toconsiderfutureservicesbeyondMBB.Onewayto future-proofthenetworkarchitectureisthrough supportfornetworkslicing.Inthiscontext,all functionsrequiredtosupportMBBandneeded foraccessandmobilitymanagement,session managementandtheUP(SMF+PGW-Cand UPF+PGW-U)arepartofanMBBnetworkslice. Hence,asinglenetworkslicecanbeusedbothfor internetaccessandforIMSvoiceandotherIMS services.Thisarrangementcanbemaintained whenthedevicestartsontheEPCandmoves tothe5GCorviceversa. Theservicesdomain Regardlessofaccesstechnology,supportforIMS voice,emergencyservicesandSMSisexpectedto workseamlesslyanywheresubscriberscanconnect tothenetwork.AphonewillnotselectNRSAaccess unlessitdetectsIMSvoiceservice.Voiceand emergencyservicesaresupportedbyspecific capabilitiesintheRANandPacketCorethat mustbeprovidedtothecombinedEPSand5GS aftermigration.TheIMSneedstobeupdated tosupportNRSA. EPSfallbackcanbeusedasafirstvoicemigration stepwhenintroducingthe5GSindeploymentswhere NRcoveragehasunderlyingLTE/EPCcoverage. EPSfallbackmeansthatthedeviceismovedtoEPS atcallestablishment.Itistypicallyusedpriortothe deploymentofallthevoicecapabilitiesinNRor beforetheRANisdimensionedandtunedforvoice. ThesubsequentvoicemigrationfromEPS fallbacktovoiceoverNRisachievedbyallowing voicecallstobeestablishedonNRconnectedtothe 5GCinsteadofperformingEPSfallbackatcall establishment.Thismigrationstepcanbedoneonce allrequiredvoice-over-NRcapabilitiesareinplace inthedeviceandthenetwork,andRANis dimensionedandtunedforvoice.However,devices introducedbeforethisstepwillremaininthefield whenvoiceoverNRisintroduced,whichiswhythe networkmustsupportvoiceoverNRincluding EPSfallback. Withrespecttoemergencyservices,the3GPP hasspecifieddifferentmethodsforemergencycalls inthe5GS.Forexample,usingaservicerequestto performEPSfallbackofemergencycallsisapossible firstmigrationstep.Thebenefitofthisapproachis thatitonlyimpactstheAMFandgNB,anditavoids regulatoryrequirementsonthe5GSrelatedto emergencycalls.Migratingtoemergency-call- over-NRrequiresallregulatoryrequirements relatedtoemergencycallstobeaddressed. Operatorsmustdecidewhethertostartwith aservicerequestforemergencyortodeploy emergency-call-over-NRdirectly. SMScontinuestobeanimportantserviceinthe 5GS.TherearetwobasicmethodstotransportSMS inthecombined5GSandEPS,namelySMS-over-IP (SMSoIP)usinganIMSSIP(SessionInitiation Protocol)messageandSMS-over-NAS (SMSoNAS)usingNASsignaling.Thelatteruses 5GNASwhenthedeviceisinthe5GSand4GNAS whenintheEPS.OperatorsthatuseSMSoNASfor devicesintheEPSaremigratingtosupport SMSoNASinthe5GS.Operatorsthatarealready usingSMSoIPintheEPSmaycontinuetosupport SMSoIPoverthe5GS. BeyondMBB,5Galsoincludesamultitudeofnew andenhancedcapabilitiesaddressingmanydifferent businesssegments.Itisdifficulttopredictwhich ofthesecapabilitieswillberequiredintheearly introductionofthe5GSandtheanswermayvaryfor differentoperators.Examplesoffrequentlymentioned opportunitiesforwide-areaservicesareautomotive [5],smartgridsforutilities,andpublicsafety. Alloftheseusecaseswillutilizetheestablished 5GSMBBscaleandwide-areanetworkbuild-out, specificallyifsomekeyarchitecturalconceptsare plannedforwhenintroducingthe5GS.Theseinclude technologieslikenetworkslicing,edgecomputing andnetworkexposure[6],forwhichtherequired functionalityisalreadybuilt-infromthestart. These5GStechnologiesarealsokeyenablers forlocalnetworkdeploymentsathospitals, harbors,airportsandmanufacturingfacilities[7]. Conclusion Introducingthe5GSystemforwide-areaservices inanexistingEvolvedPacketSystemnetworkhas significantimpactsacrossallnetworkdomains, includingtheRAN,packetcore,userdataand policies,andservices,aswellasaffectingdevicesand backendsystems.Nonetheless,acombined4G-5G networkisanecessarystepformostoperatorswith existingEPSdeployments. Figure 3 Migration from the EPS to the 5GS utilizing EPC-5GC tight interworking Existing EPC PGW SGW MME E-UTRAN Existing EPC PGW SMF + S/PGW-C UPF+ S/PGW-U SGW MME New 5GC with interworking Dual-mode packet core PGW SMF + S/PGW-C UPF+ S/PGW-U SGW MME E-UTRAN NG-RAN E-UTRAN NG-RAN AMF AMF ...5GS TECHNOLOGIES ARE ALSO KEY ENABLERS FOR LOCAL NETWORK DEPLOYMENTS...
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    56 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 57 ✱ MIGRATION FROM EPS TO 5GS MIGRATION FROM EPS TO 5GS ✱ 10 FEBRUARY 24, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ FEBRUARY 24, 2020 11 Further reading ❭ 5G is here, available at: https://www.ericsson.com/en/5g ❭ 5G Voice, available at: https://www.ericsson.com/en/digital-services/trending/5g-voice-evolution-where-to-start ❭ 5G Access, available at: https://www.ericsson.com/en/networks/offerings/5g ❭ 5G Core, available at: https://www.ericsson.com/en/digital-services/offerings/core-network/5g-core References 1. Ericsson Technology Review, 5G New Radio RAN and transport choices that minimize TCO, November 7, 2019, Eriksson A.C; Forsman, M; Ronkainen, H; Willars, P; Östberg, C, available at: https://www.ericsson. com/en/reports-and-papers/ericsson-technology-review/articles/5g-nr-ran-and-transport-choices-that- minimize-tco 2. Ericsson Technology Review, Simplifying the 5G ecosystem by reducing architecture options, November 30, 2018, Cagenius, T; Ryde, A; Vikberg, J; Willars, P, available at: https://www.ericsson.com/en/reports- and-papers/ericsson-technology-review/articles/simplifying-the-5g-ecosystem-by-reducing-architecture- options 3. Ericsson Technology Review, Cloud-native application design in the telecom domain, June 5, 2019, Persson, H.S; Kassaei, H, available at: https://www.ericsson.com/en/reports-and-papers/ericsson- technology-review/articles/cloud-native-application-design-in-the-telecom-domain 4. 3GPP TS 23.501, 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; System Architecture for the 5G System (5GS), available at: https://www.3gpp.org/ DynaReport/23501.htm 5. Ericsson Technology Review, Driving transformation in the automotive and road transport ecosystem with 5G, September 13, 2019, Lohmar, T; Zaidi, A; Olofsson, H; Boberg, C, available at: https://www. ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/transforming-transportation-with-5g 6. Ericsson Technology Review, Service exposure: a critical capability in a 5G world, May 7, 2019, Friman, J; Ek, M; Chen, P; Manocha, J; Soares, J, available at: https://www.ericsson.com/en/reports-and-papers/ ericsson-technology-review/articles/service-exposure-a-critical-capability-in-a-5g-world 7. Ericsson Technology Review, August 27, 2019, 5G-TSN integration meets networking requirements for industrial automation, Farkas, J; Varga, B; Miklós, G; Sachs, J, available at: https://www.ericsson.com/en/ reports-and-papers/ericsson-technology-review/articles/5g-tsn-integration-for-industrial-automation 8. Ericsson, 2019, Sharing for the best performance, available at: https://www.ericsson.com/en/networks/ offerings/5g/sharing-spectrum-with-ericsson-spectrum-sharing/download-form theauthors Ralf Keller ◆ is an expert in core network migration at Ericsson who joined the companyin1996.Hiscurrent focus is on packet core architecture and technology. His work includes both technology studies and contributions to product strategies for mobile communication, including communication services in 5G, migration to the 5GS, and interworking and coexistence with legacy networks. He is also active in the GSMA, where he works on the profiling of 5G and 5G roaming. Keller holds a Ph.D. in computer science from the University of Mannheim in Germany. Torbjörn Cagenius ◆ is a senior expert in network architecture at Business Area Digital Services. He joined Ericsson in 1990 and has worked in a variety of technology areas such as fiber-to-the-home, main-remote RBS, fixed- mobile convergence, IPTV, network architecture evolution, software-defined networking and Network Functions Virtualization. In his current role, he focuses on 5G and associated network architecture evolution. Cagenius holds an M.Sc. in electrical engineering from KTH Royal Institute of Technology in Stockholm, Sweden. Anders Ryde ◆ is a senior expert in network and service architecture at Business Area Digital Services. He joined Ericsson in 1982 and has worked in a variety of technology areas in network and service architecture development for multimedia-enabled telecommunication, targeting both enterprise and residential users. This includes the evolution of mobile telephony to IMS and VoLTE. In his current role, he focuses on bringing voice and other communication services into 5G, general 5G evolution and associated network architecture evolution. Ryde holds an M.Sc. in electrical engineering from KTH Royal Institute of Technology in Stockholm. David Castellanos ◆ is a senior specialist in subscription handling for MBB at Product Development Unit User Data Management & Policy. He joined Ericsson in 1996 and has worked on identity and subscription management solutions for different access generations (2G/3G/4G) and domains (IMS and identity federation). In his current role, he is focused on identity and subscription management in 5G. Castellanos holds two bachelor’s degrees in telecom engineering from Universidad Politécnica de Madrid in Spain. Theauthorswould liketothankVictor FerraroEsperanza, PerWillars,Göran Hall,JoseMiguel DopicoandMagnus Hallenstålfortheir contributionsto thisarticle. Successfulmanagementofthistransition requiresaholisticstrategythatconsidersallnetwork domains,aswellascoveragestrategy,spectrum assetsandwhichservicestoofferwhere.Thereare severalsupportedmigrationpathsperdomaintoa full5GS,andthetransitioncanbeadaptedto addresseachoperator’sspecificneedsperdomain. Inthelongerterm,theintroductionof5GS supportingmobilebroadbandasinitialwide-area serviceisasolidfoundationtointroduceadditional servicesandusecases,meetingthefullexpectation onafuture-proof5GS.
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    58 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 59 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 2 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 3 The enhancements in the 3GPP releases 16 and 17 of 5G New Radio include both extensions to existing features as well as features that address new verticals and deployment scenarios. Operation in unlicensed spectrum, intelligent transportation systems, Industrial Internet of Things, and non-terrestrial networks are just a few of the highlights. JANNE PEISA, PATRIK PERSSON, STEFAN PARKVALL, ERIK DAHLMAN, ASBJØRN GRØVLEN, CHRISTIAN HOYMANN, DIRK GERSTENBERGER According to the latest Ericsson Mobility Report, global traffic levels hit 38 exabytes per month at the end of 2019, with a projected fourfold increase to 160 exabytes per month expected by 2025 [1]. Fortunately, the 5G system is designed to handle this massive increase in data traffic in a way that ensures superior performance with minimal impact on the net costs for consumers. ■ Theevolutionof5GNewRadio(NR)has progressedswiftlysincethe3GPPstandardizedthe firstNRrelease(release15)inmid-2018.Notonlyis release16nearlyfinalizedbutthescopeofrelease 17hasalsorecentlybeenapproved.Makingwise decisionsinthemonthsandyearsaheadwill requirethatmobilenetworkoperatorsandother industrystakeholdershaveasolidunderstanding ofbothreleases. NRdevelopmentstartedinrelease15[2][3]with theambitiontofulfillthe5Grequirementssetbythe ITU(InternationalTelecommunicationUnion)in IMT-2020(InternationalMobileTelecommunications- 2020).Theoveralldesignconsistsofseveralkey components.Theextensiontomuchhighercarrier frequenciesisanimportantoneduetothecontinuing demandformoretrafficandhigherconsumerdata ratesandtheassociatedneedformorespectrum andwidertransmissionbandwidths.Theultra-lean designofNRenhancesnetworkenergyperformance andreducesinterference,whileinterworkingand LTEcoexistencewillmakeitpossibletoutilize existingcellularnetworks.Theforwardcompatibility ofNRdesignwillensurethatitispreparedforfuture evolution.Lowlatencyisalsocriticaltoimprove performanceandenablenewusecases.Extensive usageofbeamformingandamassivenumberof antennaelementsfordatatransmissionandfor control-planeproceduresarealsonotable componentsofNRdesign. Figure1showsthetimeplanfortheevolution ofNRoverthenextfewyears.Release16,thefirst stepintheNRevolution,containsseveralsignificant extensionsandenhancements.Someoftheseare extensions/improvementstoexistingfeatures, whileothersareentirelynewfeaturesthataddress newdeploymentscenariosand/ornewverticals. 5GNRrelease16–enhancements toexistingfeatures Themostnotableenhancementstoexistingfeatures inrelease16areintheareasofmultiple-input, multiple-output(MIMO)andbeamforming enhancements,dynamicspectrumsharing(DSS), dualconnectivity(DC)andcarrieraggregation (CA),anduserequipment(UE)powersaving. Multiple-input,multiple-output andbeamformingenhancements Release16introducesenhancedbeamhandling andchannel-stateinformation(CSI)feedback, aswellassupportfortransmissiontoasingleUE frommultipletransmissionpoints(multi-TRP)and full-powertransmissionfrommultipleUEantennas intheuplink(UL).Theseenhancementsincrease throughput,reduceoverhead,and/orprovide additionalrobustness[4].Additionalmobility enhancementsenablereducedhandoverdelays, inparticularwhenappliedtobeam-management mechanismsusedfordeploymentsinmillimeter (mm)wavebands[5]. Dynamicspectrumsharing DSSprovidesacost-effectiveandefficientsolution forenablingasmoothtransitionfrom4Gto5Gby allowingLTEandNRtosharethesamecarrier. Inrelease16,thenumberofrate-matchingpatterns availableinNRhasbeenincreasedtoallow spectrumsharingwhenCAisusedforLTE. Dualconnectivityandcarrieraggregation Release16reduceslatencyforsetupandactivation ofCA/DC,therebyleadingtoimprovedsystem capacityandtheabilitytoachievehigherdatarates. Unlikerelease15,wheremeasurementconfiguration andreportingdoesnottakeplaceuntiltheUEenters thefullyconnectedstate,inrelease16theconnection canberesumedafterperiodsofinactivitywithoutthe needforextensivesignalingforconfigurationand reporting[6].Additionally,release16introduces aperiodictriggeringofCSIreferencesignal transmissionsincaseoftheaggregationof carrierswithdifferentnumerology. Userequipmentpowersaving ToreduceUEpowerconsumption,release16 includesawake-upsignalalongwithenhancements tocontrolsignalingandschedulingmechanisms[7]. Figure 1 NR evolution time plan Release 14 2016 Release 15 NR NR evolution Release 16 Release 17 Release 18 Release 19 2017 2018 2019 2020 2021 2022 2023 5Gevolution:3GPP RELEASES 16 & 17 OVERVIEW
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    60 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 61 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 4 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 5 throughthecellscreatedbyanIABnode,thereby enablingmulti-hopwirelessbackhauling. ThelowerpartofthefigurehighlightsthatanIAB nodeincludesaconventionalDUpartthatcreates cellstowhichUEsandotherIABnodescanconnect. TheIABnodealsoincludesamobile-termination (MT) partprovidingconnectivityfortheIABnode to(theDUof)thedonornode. NewRadioinunlicensedspectrum Spectrumavailabilityisessentialtowireless communication,andthelargeamountofspectrum availableinunlicensedbandsisattractivefor increasingdataratesandcapacityfor3GPPsystems. Toexploitthisspectrumresource,release16enables NRoperationinunlicensedspectrum,targetingthe 5GHzand6GHzunlicensedbands.Itsupportsboth standaloneoperation,wherenolicensedspectrum isnecessary,andlicensed-assistedoperation,where acarrierinlicensedspectrumaidstheconnection setup.Thisgreatlyaddstodeploymentflexibility comparedwithLTE,whereonlylicensed-assisted operationissupported. Operationinunlicensedspectrumisdependent onseveralkeyprinciplesincludingultra-lean transmissionanduseoftheflexibleNRframestructure. Bothofthesewereincludedinrelease15. Channelaccessmechanismsbasedonlisten- before-talk(LBT)areprobablythemostobvious areaofenhancementinrelease16.NRlargely reuses thesameLBTmechanismasdefined forWi-FiandLTEinunlicensedspectrum. Interestingly,itwasdemonstratedduring standardizationthatreplacingoneWi-Finetwork withanNRnetworkcanleadtoimproved performancefortheremainingWi-Finetworks[9] aswellasfortheNRnetworkitself. IndustrialIoTandultra-reliable low-latencycommunication TheIIOTisamajorverticalfocusareaforNR release16.TowidenthesetofpotentialIIoTuse casesandsupportincreaseddemandfornewuse casessuchasfactoryautomation,electricalpower distributionandthetransportindustry,release16 includeslatencyandreliabilityenhancementsthat buildonthealreadyverylowair-interfacelatency andhighreliability[10]providedbyrelease15. Supportfortime-sensitivenetworking(TSN), whereveryaccuratetimesynchronizationis essential,isalsointroduced.Figure3illustrates TSNintegrationin5GNR. 5GNRrelease16–newverticals anddeploymentscenarios Themostnotablenewverticalsanddeployment scenariosaddressedinrelease16areintheareasof: ❭ Integrated access and backhaul (IAB) ❭ NR in unlicensed spectrum ❭ Features related to Industrial Internet of Things (IIoT) and ultra-reliable low latency communication (URLLC) ❭ Intelligent transportation systems (ITS) and vehicle-to-anything (V2X) communications ❭ Positioning. Integratedaccessandbackhauling IABprovidesanalternativetofiberbackhaulby extendingNRtosupportwirelessbackhaul[8]. Asaresult,itispossibletouseNRforawirelesslink fromcentrallocationstodistributedcellsitesand betweencellsites.Thiscansimplifythedeployment ofsmallcells,forexample,andbeusefulfortemporary deploymentsforspecialeventsoremergency situations.IABcanbeusedinanyfrequencyband inwhichNRcanoperate.However,itisanticipated thatmm-wavespectrumwillbethemostrelevant spectrumforthebackhaullink.Furthermore, theaccesslinkmayeitheroperateinthesame frequencybandasthebackhaullink(knownas inbandoperation)orbyusingaseparatefrequency band(out-of-bandoperation). Architecture-wise,IABisbasedontheCU/DU splitintroducedinrelease15.TheCU/DUsplit impliesthatthebasestationissplitintotwoparts– acentralizedunit(CU)andoneormoredistributed units(DUs)–wheretheCUandDU(s)maybe physicallyseparateddependingonthedeployment. TheCUincludestheRRC(radioresourcecontrol) andPDC(packetdataconvergence)protocols,while theDUincludestheRLC(radiolinkcontrol)and MAC(multipleaccesscontrol)protocolsalongwith thephysicallayer.TheCUandDUareconnected throughthestandardizedF1interface. Figure2illustratesthebasicstructureofa networkutilizingIAB.TheIABnodecreatescells ofitsownandappearsasanormalbasestationto UEsconnectingtoit.ConnectingtheIABnodeto thenetworkusesthesameinitial-accessmechanism asaterminal.Onceconnected,theIABnodereceives thenecessaryconfigurationfromthedonornode. AdditionalIABnodescanconnecttothenetwork Figure 2 High-level architecture of IAB CU DU MT DU MT DU F1 Donor node IAB node Backhaul based on IAB Access link Donor node IAB node IAB node Conventional backhaul Access link Access link Backhaul based on IAB IAB node F1 Figure 3 Overview of the TSN integration Ethernet TSN domain Ethernet TSN domain5G domain: supporting Ethernet/TSN Time reference IIoT device UE 5G Core RAN Ethernet bridge Programmable logic controller TSN control
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    62 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 63 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 6 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 7 AlthoughmanyoftheURLLC-related improvementsaresmallinthemselves,taken togethertheysignificantlyenhanceNRinthearea ofURLLC[11]. Theinter-UEdownlink(DL)preemptionthatis alreadysupportedinrelease15isextendedin release16toincludetheUL,suchthataUE’s previouslyscheduledlower-priorityUL transmissioncanbepreempted(thatis,cancelled) byanotherUE’shigher-priorityULtransmission. Release16alsosupportsstandardizedhandling ofintra-EUULresourceconflicts. Toreducelatency,release16supportsmore frequentcontrol-channelmonitoring.Furthermore, forbothULconfiguredgrantandDLsemi-persistent scheduling,multipleconfigurationscanbeactive simultaneouslytosupportmultipleservices.These enhancementsareespeciallyusefulincombination withTSNtraffic,wherethetrafficpatternisknown tothebasestation. Intelligenttransportationsystems andvehicle-to-anythingcommunications ITS,whichprovidearangeoftransportand traffic-managementservices,areanothermajor verticalfocusareainrelease16.Amongother benefits,ITSsolutionsimprovetrafficsafetyaswell asreducingtrafficcongestion,fuelconsumption andenvironmentalimpacts.TofacilitateITS, communicationisrequirednotonlybetween vehiclesandthefixedinfrastructurebutalso betweenvehicles.Currently,25usecasesfor advancedV2Xcommunicationshavebeendefined, includingvehicleplatooningandcooperative communicationusingextendedsensors[12]. Inrelease15,communicationwithfixed infrastructureisprovidedbytheaccess-link interfacebetweenthebasestationandtheUE. Release16addstheoptionoftheNRsidelink(PC5), whichcanoperateinin-coverage,out-of-coverage andpartial-coveragescenarios,utilizingallNR frequencybands.Itsupportsunicast,groupcastand broadcastcommunication,andhybridautomatic repeatrequest(hybrid-ARQ)retransmissionscan beusedforscenariosthatrequiremorerobust communication.Groupscanbeeitherconfigured orformed,andthegroupmemberscommunicate usinggroupcasttransmissions.Atruckplatoon, forexample,couldbeconfiguredusingdedicated hybrid-ARQsignalingbetweenthereceivers andtransmitter,orformedinadynamicmanner basedonthedistancebetweenthetransmitter andreceiver(s). Positioning Formanyyears,UEpositioninghasbeen accomplishedwithGlobalNavigationSatellite Systemsassistedbycellularnetworks.Thisapproach providesaccuratepositioningbutistypicallylimited tooutdoorareaswithsatellitevisibility.Thereis currentlyarangeofapplicationsthatrequires accuratepositioningnotonlyoutdoorsbutalso indoors.Architecture-wise,NRpositioningisbased ontheuseofalocationserver,similartoLTE.The locationservercollectsanddistributesinformation relatedtopositioning(UEcapabilities,assistance data,measurements,positionestimatesandsoon) totheotherentitiesinvolvedinthepositioning procedures.Arangeofpositioningmethods,both DL-basedandUL-based,areusedseparatelyorin combinationtomeettheaccuracyrequirementsfor differentscenarios. DL-basedpositioningissupportedbyproviding anewreferencesignalcalledthepositioning referencesignal(PRS).ComparedwithLTE, thePRShasamoreregularstructureandamuch largerbandwidth,whichallowsforamoreprecise correlationandtimeofarrival(ToA)estimation. TheUEcanthenreporttheToAdifferencefor PRSsreceivedfrommultipledistinctbasestations, andthelocationservercanusethereportsto determinethepositionoftheUE. UL-basedpositioningisbasedonrelease15 soundingreferencesignals(SRSs)withrelease16 extensions.BasedonthereceivedSRSs,thebase stationscanmeasureandreport(tothelocation server)thearrivaltime,thereceivedpowerand theangleofarrivalfromwhichtheposition oftheUEcanbeestimated. ThetimedifferencebetweenDLreceptionand ULtransmissioncanalsobereportedandusedin round-triptime(RTT)basedpositioningschemes, wherethedistancebetweenabasestationand aUEcanbedeterminedbasedontheestimated RTT.BycombiningseveralsuchRTT measurements,involvingdifferentbasestations, thepositioncanbedetermined. 5GNRrelease17 Theworkitemsapprovedbythe3GPPinDecember 2019willleadtotheintroductionofnewfeaturesfor thethreemainusecasefamilies:enhancedmobile broadband(eMBB),URLLC and massivemachine- typecommunications(mMTC).Thepurposeisto supporttheexpectedgrowthinmobile-datatraffic, aswellascustomizingNRforautomotive,logistics, publicsafety,mediaandmanufacturingusecases. Theenhancementstoexistingfeaturesintroducedin release17willbeforfunctionalityalreadydeployed inliveNRnetworksorrelatetospecificnew requirementsthatareemerginginthemarket. ThetablepresentedinTable1summarizesthe scopeoftheenhancementstoexistingNRfeatures inrelease17,whilethetablein Table2 summarizes thescopeofthenewfeatures. Terms and abbreviations CA – Carrier Aggregation | CSI – Channel-State Information | CU – Centralized Unit | DC – Dual Connectivity | DL – Downlink | DSS – Dynamic Spectrum Sharing | DU – Distributed Unit | eMBB – Enhanced Mobile Broadband | FR1, FR2 – Frequency Range 1, 2 | hyrbrid-ARQ – Hybrid Automatic Repeat Request | IAB – Integrated Access and Backhaul/Backhauling | IIoT – Industrial Internet of Things | IoT – Internet of Things | ITS – Intelligent Transportation Systems | LBT – Listen-Before-Talk | LTE-M – LTE Machine-Type Communications | MIMO – Multiple-Input, Multiple-Output | mMTC – Massive Machine-Type Communications | MT – Mobile Termination | MTC – Machine-Type Communications | Multi-TRP – Multiple Transmission Points | NR – New Radio | PC5 – Direct Mode Interface | PRS – Positioning Reference Signal | RTT – Round-Trip Time | SON – Self-Organizing Networks | SRS – Sounding Reference Signal | ToA – Time of Arrival | TSN – Time-Sensitive Networking | UE – User Equipment | UL – Uplink | URLLC – Ultra-Reliable Low-Latency Communication | V2X – Vehicle-to-Anything | XR – Anything Reality CURRENTLY, 25 USE CASES FOR ADVANCED V2X COMMUNICATIONS HAVE BEEN DEFINED THE ENHANCEMENTS… RELATE TO SPECIFIC NEW REQUIREMENTS THAT ARE EMERGING IN THE MARKET
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    64 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 65 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 8 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 9 eMBB feature IAB • Additionof(limited)supportfornetworktopologychanges • Improvedduplexingofaccessandbackhaullinks (simultaneousoperation onchildandparentlink,forexample) • Routingenhancements MIMO • Improvementsbasedonexperiencefromcommercialnetworksfocusingon multi-beamoperationmainlyforfrequencyrange2(FR2),supportformulti-TRP deployment,SRSs,andCSImeasurementandreporting DSS • Cross-carrierschedulingenhancements • Otherschedulingenhancements Coverage • Enhancedwide-areacoverageforbothFR1andFR2(tobestudied) • Focusonmobilebroadbandandvoiceservicesusecases,withtheexception ofthelow-powerwideareausecase Multi-radio dual connectivity • Moreefficientactivation/deactivationmechanismofsecondarycells • Conditionalprimary-secondarycellchange/addition UE power saving • Improvedmechanismsintheareaofdiscontinuousreceptionandblinddecoding ofcontrolchannels Data collection • Simplifieddeploymentandenhancementstosupportself-organizingnetworks (SON)withimproveddata-collectionmechanismsforSONandminimization ofdrivetests QoE management and optimizations for diverse services • GenericframeworkfortriggeringandconfiguringQoEmeasurementcollection andreportingforvarious5Gusecases URLLC feature IIoT and URLLC support • ImprovedsupportforfactoryautomationandURLLC,includingphysicallayer feedbackenhancementsandenhancementsforsupportoftimesynchronization • IdentificationofenhancementsforURLLC/IIoToperationincontrolled environmentsonunlicensedbands Positioning • Higheraccuracy(horizontalandvertical)andlowerlatency,especiallyfor IIoTusecases Sidelink • FocusonV2X,publicsafetyandcommercialusecases • Resourceallocationenhancement • Sidelinkdiscontinuousreception RAN slicing (also relevant for the mMTC use case) • MechanismstoenableUEfastaccesstothecellsupportingtheintendedslice • Mechanismstosupportservicecontinuityforintra-radio-accesstechnology handoverserviceinterruption mMTC feature Small data transmissions in inactive state • Reducedoverheadfromconnectionestablishment • Usecases:keep-alivemessages,wearablesandvarioussensors Table 1 Summary of release 17 enhancements to existing features eMBB feature Supporting NR from 52.6GHz to 71GHz •ExtendedNRfrequencyrangetoallowexploitationofmorespectrum, includingthe60GHzunlicensedband •DefinitionofnewOFDM(orthogonalfrequency-divisionmultiplexing) numerologyandchannelaccessmechanismtocomplywiththeregulatory requirementsapplicabletounlicensedspectrum Multicast and broadcast services • PrimarilytargetedatV2X,publicsafety,IPmulticast,softwaredelivery andInternetofThings(IoT)applications Support for multi-SIM devices • Pagingcollisionavoidance • NetworknotificationwhenaUEswitchesnetworks Support for non- terrestrial networks • Supportforsatellites(especiallyLowEarthorbitandgeostationarysatellites) andhigh-altitudeplatformsasanadditionalmeanstoprovidecoverage inruralareas Multi-radio dual connectivity • L2versusL3relaying(studyandcompare) • Scenariosincludesingle-hop,UE-to-UEandUE-to-networkrelaying Sidelink relaying • Improvedmechanismsintheareaofdiscontinuousreceptionandblind decodingofcontrolchannels Data collection • SimplifieddeploymentandenhancementstosupportSONwithimproveddata- collectionmechanismsforSONandminimizationofdrivetests URLLC feature Anything reality (XR) evaluations • Evaluateneedsintermsofsimultaneouslyprovidingveryhighdatarates andlowlatencyinaresource-efficientmanner • Intendedtosupportvariousformsofaugmentedrealityandvirtualreality, collectivelyreferredtoasXR mMTC feature Support of reduced- capability NR devices • Targetedatmid-tierapplicationssuchasmachine-typecommunicationsfor industrialsensors,videosurveillance,andwearableswithdataratesbetween NarrowbandIoT/LTE-Mdataratesand“full”NRdatarates • Addressesissuesincludingcomplexityreduction,UEpowersavingandbattery lifetimeenhancement Table 2 Summary of new functionality added in release 17
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    66 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 67 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 10 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 11 Further reading ❭ Leading the way to 5G through standardization, available at: https://www.ericsson.com/en/blog/2019/5/lte- nr-interworking-in-5G ❭ A new standard for Dynamic Spectrum Sharing, available at: https://www.ericsson.com/en/blog/2019/6/ dynamic-spectrum-sharing-standardization ❭ Standardizing a new paradigm in base station architecture, available at: https://www.ericsson.com/en/ blog/2019/9/standardizing-a-new-paradigm-in-base-station-architecture ❭ Drones and networks: mobility support, available at: https://www.ericsson.com/en/blog/2019/1/drones-and- networks-mobility-support ❭ How to identify uncertified drones with machine learning, available at: https://www.ericsson.com/en/ blog/2019/5/how-to-identify-uncertified-drones-machine-learning ❭ An overview of remote interference management for 5G, available at: www.ericsson.com/en/blog/2019/9/ overview-of-remote-interference-management References 1. Ericsson Mobility Report, November 2019, available at: https://www.ericsson.com/en/mobility-report/ reports/november-2019 2. Academic Press, Oxford, UK, 5G NR: The Next Generation Wireless Access Technology, 2018, Dahlman, E; Parkvall, S; Sköld, J 3. IEEE Wireless Communications, pp. 124-130, Ultra-Reliable and Low-Latency Communications in 5G Downlink: Physical Layer Aspects, June 2018, Ji, H; Park, S; Yeo, J; Kim, Y; Lee, J; Shim, B, available at: https://ieeexplore.ieee.org/document/8403963 4. 3GPP RP-182863, Enhancements on MIMO for NR, available at: www.3gpp.org 5. 3GPP RP-190489, NR mobility enhancements, available at: www.3gpp.org 6. 3GPP RP-191600, LTE-NR & NR-NR Dual Connectivity and NR Carrier Aggregation enhancements, available at: www.3gpp.org 7. 3GPP TR 38.840, Study on User Equipment (UE) power saving in NR, available at: www.3gpp.org 8. 3GPP TR 38.874, NR; Study on Integrated Access and Backhaul, available at: www.3gpp.org 9. 3GPP TR 38.889, Study on NR-based access to unlicensed spectrum, available at: www.3gpp.org 10. 3GPP TR 38.824, Study on physical layer enhancements for NR ultra-reliable and low latency case (URLLC) , available at: www.3gpp.org 11. 3GPP TR 38.825, Study on NR industrial Internet of Things (IoT) , available at: www.3gpp.org 12.3GPP TR 38.885, Study on NR Vehicle-to-Everything (V2X) , available at: www.3gpp.org Conclusion Theenhancementsinthe3GPP’sreleases16and 17willplayacriticalroleinexpandingboththe availabilityandtheapplicabilityof5GNewRadio toawiderangeofnewapplicationsandusecases inbothindustryandpublicservices.Inorderto makethedetailsofthesetworeleasesmoreeasily digestible,wehaveidentifiedwhatweconsider tobethemostsignificantenhancementsand groupedthemintotwocategories:enhancements toexistingfeaturesandfeaturesthataddress newverticalsanddeploymentscenarios. FromEricsson’spointofview,theoverall ambitionoftheNRevolutionfromause-case perspectivemustbetoensurethat5GNRcovers allrelevantusecasestofulfillthevisionofubiquitous connectivity–thatis,theabilitytoconnectanything anywhereatanytime.Fromafeaturesperspective, webelievethattheevolutionofNRfunctionality mustbedrivenbythegoalofincreasingefficiency andeffectivenesswhenandwhereitiscommercially justified. Lookingahead,itiscriticalthattheindustry workstogethertoensurethatNRiseasytodeploy andoperate,andthatitcontinuestoprovide superiorperformancecomparedwithcompeting technologies.WemustalsoensurethatNRprovides ahighdegreeofenergyefficiencyonboththenetwork anddevicesides,andthatitretainsitsabilityto coexistsmoothlywithLTE. AtEricsson,weareconvincedthatthebestway forwardisforNRtocontinuetosupportalluse casesfromoneplatform,withafocusonforward compatibility,sufficientconfigurabilityandmaximal simplicity.Wemustalsoworktoavoidunnecessary updatesinthenetworkhardwareandensurethat functionalityisspecifiedinacommonwaythat benefitsmultipleusecases.
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    68 ERICSSON TECHNOLOGYREVIEW ✱ #01 2020 #01 2020 ✱ ERICSSON TECHNOLOGY REVIEW 69 ✱ 5G NR EVOLUTION 5G NR EVOLUTION ✱ 12 MARCH 9, 2020 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ MARCH 9, 2020 13 theauthors Asbjørn Grøvlen ◆ is a principal researcher in physical layer standardization who joined Ericsson in 2014. He currently works as Ericsson’s technical coordinator for 3GPP RAN WG1 and has been involved in the standardization of wireless-access technologies from 3G to 4G LTE and 5G NR. His contribution to NR (5G) has been on initial access and mobility. Grøvlen holds an M.Sc. in electrical engineering from the Norwegian University of Science and Technology in Trondheim. Christian Hoymann ◆ joined Ericsson Research in 2007 and currently leads a research group at Ericsson Eurolab in Aachen, Germany. His team focuses on standardization of 4G and 5G radio networks (Wi-Fi, LTE and NR). In addition, he heads up Ericsson’s 3GPP RAN standardization delegation as the company’s technical coordinator for 3GPP RAN. Hoymann holds a Ph.D. in electrical engineering from RWTH Aachen University, Germany. Dirk Gerstenberger ◆ joined Ericsson in 1997 after earning a Dipl-Ing. in electrical engineering from Paderborn University in Germany. He is currently a manager at the Standards & Technology department within Business Area Networks at Ericsson, working with the evolution of radio-access standards and radio-network deployments. Gerstenberger led the radio-access standardization as head of Ericsson’s RAN1 delegation and chairman of 3GPP RAN1 during standardization of 3G and 4G, and he was also engaged in industry initiatives leading to the standardization of 5G. He received the Ericsson Inventor of the Year award in 2008 and is named as the inventor in more than 100 patents. theauthors Janne Peisa ◆ has worked at Ericsson in the research and development of 3G, 4G and 5G systems since 1998. He is currently responsible for coordinating Ericsson’s research on 5G evolution and beyond 5G activities. Previously, he coordinated Ericsson’s RAN standardization activities in the 3GPP and led Ericsson Research’s 5G program. In 2001, he received the Ericsson Inventor of the Year award. Peisa has authored several publications and patents and holds a Ph.D. in theoretical physics from the University of Helsinki, Finland. Patrik Persson ◆ joined Ericsson Research in 2007 and currently serves as a principal researcher. Since 2014 he has been responsible for the Ericsson back-office work in the 3GPP RAN standardization of 4G and 5G. Prior to that, he worked extensively in the areas of antennas and propagation as well as proprietary development of LTE. Persson holds a Ph.D. in electrical engineering from KTH Royal Institute of Technology in Stockholm, Sweden. Stefan Parkvall ◆ is a senior expert working with future radio access. He joined Ericsson in 1999 and played a key role in the development of HSPA, LTE and NR radio access. Parkvall has also been deeply involved in 3GPP standardization for many years. He is an IEEE (Institute of Electrical and Electronics Engineers) fellow and has coauthored several popular books, including 4G: LTE/ LTE-Advanced for Mobile Broadband, and 5G NR: The Next Generation Wireless Access Technology. He has more than 1,500 patents in the area of mobile communication and holds a Ph.D. in electrical engineering from KTH Royal Institute of Technology. Erik Dahlman ◆ joined Ericsson in 1993 and is currently a senior expert in radio-access technologies within Ericsson Research. He has been involved in the development of wireless-access technologies from early 3G to 4G LTE to 5G NR. He is currently focusing on the evolution of 5G as well as technologies applicable beyond 5G wireless access. He is the coauthor of the books 3G Evolution: HSPA and LTE for Mobile Broadband, 4G: LTE and LTE-Advanced for Mobile Broadband, 4G: LTE- Advanced Pro and the Road to 5G, and, most recently, 5G NR: The Next Generation Wireless Access Technology. Dahlman holds a Ph.D. in telecommunications from KTH Royal Institute of Technology.
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    ISSN 0014-0171 284 23-3352| Uen © Ericsson AB 2020 Ericsson SE-164 83 Stockholm, Sweden Phone: +46 10 719 0000