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Ericsson Technology Review: Technology trends 2018 - Five technology trends augmenting the connected society

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Ericsson CTO Erik Ekudden presents the five technology trends driving the creation of a future network platform that can deliver truly intuitive interaction between humans and machines.

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Ericsson Technology Review: Technology trends 2018 - Five technology trends augmenting the connected society

  1. 1. SEPTEMBER 10, 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 10, 20181 2 ✱ TECHNOLOGY TRENDS TECHNOLOGY TRENDS ✱ Rapid advancements in the use of machines to augment human intelligence are creating a new reality in which we increasingly interact with robots and intelligent agents in our daily lives, both privately and professionally. The list of examples is long, but a few of the most common applications today are found in education, health care, maintenance and gaming. My vision of the future network is an intelligent platform that enables this new reality by supporting the digitalization of industries and society. This network platform consists of three main areas: 5G access, automation through agility, and a distributed cloud. A set of intelligent network applications and features is key to hiding complexity from the network’s users, regardless of whether they are humans or machines. The ability to transfer human skills in real time to other humans and machines located all around the world has the potential to enable massive efficiency gains. Autonomous operation by machines with self-learning capabilities offers the additional advantage of continuous performance and quality enhancements. High levels of cooperation and trust between humans and machines are essential. Building and maintaining trust will require decision transparency, high availability, data integrity and clear communication of intentions. The network platform I envision will deliver truly intuitive interaction between humans and machines. In my view, there are five key technology trends that will play critical roles in achieving the vision: by erik ekudden, cto #1 The realization of zero touch #2 The emergence of the Internet of Skills #3 Highly adaptable, cyber-physical systems #4 Trust technologies for security assurance #5 Ubiquitous, high-capacity radio five technology trends augmenting the connected society
  2. 2. SEPTEMBER 10, 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 10, 2018 ✱ TECHNOLOGY TRENDS TECHNOLOGY TRENDS ✱ the realization of zero-touch #1 TH E Z ERO -TO U CH networks of the future will be characterized by the fact that they require no human intervention other than high-level declarative and implementation- independent intents. On the road to zero touch both humans and machines will learn from their interactions. This will build trust and enable the machines to adjust to human intention. Computeandintelligencewillexistinthe device,inthecloudandinvariousplacesin thenetwork.Thenetworkwillautomatically computetheimperativeactionstofulfill givenintentsthroughaclosedloop operation.Today’scomplexnetworks aredesignedforoperationbyhumansand thecomplexityisexpectedtoincrease.As machinelearningandartificialintelligence continuetodevelop,efficientlyintegrating learningandreasoning,thecompetence levelofmachineintelligencewillgrow. AUGMENTATIONOFHUMAN INTELLIGENCE Therealizationofzerotouchisaniterative processinwhichmachinesandhumans collaboratereciprocally.Machinesbuild intelligencethroughcontinuouslearning andhumansareassistedbymachinesin theirdecision-makingprocesses.Inthis collaboration,themachinesgather knowledgefromhumansandthe environmentinordertobuildmodels ofthereality.Structuredknowledgeis createdfromunstructureddatawiththe supportofsemanticwebtechnologies, suchasontologies.Themodelsare createdandevolvedwithnewknowledge tomakeinformedpredictionsandenhance automateddecisionmaking. Tomaximizehumantrustandimprove decisionquality,thereisaneedfor transparencyinthemachine-driven decision-makingprocess.Itispossible togaininsightsintoamachine’sdecision processbyanalyzingitsinternalmodeland determininghowthatmodelsupported particulardecisions.Thisservesasabasis forgeneratingexplanationsthathumans canunderstand.Humanscanalsoevaluate decisionsandprovidefeedbacktothe machinetofurtherimprovethelearning 3 4 process.Theinteractionbetweenhumans andmachinesoccursusingnatural languageprocessingaswellassyntactical andsemanticanalysis. ROBOTSANDAGENTSCOLLABORATE WITHHUMANS Inacollaborativescenario,arobotwillbe abletoanticipatehumanintentionsand respondproactively.Forexample,an assembly-linerobotwouldautomatically adaptitspacetotheskillsofitshuman coworkers.Suchinteractionsrequirethe introductionofexplainableartificial intelligencetocultivatehumantrustin robots.Robotswillworkalongsidehumans toaidandtolearn.Robotscanalsointeract withotherdigitalizedcomponentsor digitaltwinstoreceivedirectfeedback. However,furtheradvancementsinrobot designandmanufacturingwillbeneeded toimprovetheirdexterity. Asoftwareagentinazero-touch networkactsinthesamewayasahuman operator.Theagentshouldbeabletolearn theroleinrealtime,aswellasthepattern andtheproperactionsforagiventask. Inparticular,itshouldbeabletohandle awiderangeofrandomvariationsinthe task,includingcontaminateddatafrom therealworldthatoriginatesfrom incidentsandmistakes.Theseagentswill learnthroughacombinationof reinforcementlearning(wheretheagent continuallyreceivesfeedbackfrom theenvironment)andsupervised/ unsupervisedlearning(suchas classification,regressionandclustering) frommultipledatastreams.Anagentcan bepre-trainedinasafeenvironment, aswithinadigitaltwin,andtransferred toalivesystem.Domainknowledgeisa keysuccessfactorwhenapplyingagents tocomplextasks. Techniquessuchasneuralnetworks offersignificantadvantagesinlearning patterns,butthecurrentapproachistoo rigid.Differentialplasticityisanother techniquethatlookspromising.
  3. 3. SEPTEMBER 10, 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 10, 2018 the emergence of the internet ofskills #2 TH E I NTERN E T O F S KI LL S allows humans to interact in real time over great distances – both with each other and with machines – and have similar sensory experiences to those that they experience locally. Current application examples include remote interactive teaching and remote repair services. A fully immersive Internet of Skills will become reality through a combination of machine interaction methods and extended communication capabilities. Internet of Skills-based systems are characterized by the interplay of various devices with sensing, processing and actuation capabilities near the user. Currentsystemslacktheaudio,visual, hapticandtelecommunicationcapabilities necessarytoprovideafullyrealistic experience.ToenabletheInternetofSkills, theinterplaybetweenhumansandrobots, andbetweenhumansandvirtualcontent, isofparticularimportance.Bothindustry andconsumersareshowinggreatinterest andopennessinusingthesenew capabilities. HUMANSKILLSDELIVERED WITHOUTBOUNDARIES Anauthenticvisualexperiencerequires real-time3Dvideocapturing,processing andrendering.Thesecapabilitiesmakeit possibletocreatea3Drepresentationof thecapturedworldandprovidethe experienceofbeingimmersedinaremote orvirtualenvironment.Whiletoday’suser devicesdon’tyetprovidethenecessary resolution,fieldofview,depthperception, wearabilityandpositioningcapabilities, thequalityandperformanceofthese technologycomponentsissteadily improving. Spatialmicrophoneswillbeusedto separateindividualsoundsourcesinthe spacedomain.Thisimpliesthattherewill beanincreasedamountofdataneededto capturetheaudiospatialaspects.Spatial audiorenderingperformanceisverymuch tiedtoefficienthead-relatedfiltermodels. Newformatsforexchangingspatialaudio streamshavebeenspecifiedand compressiontechniquesarebeing developed. Hapticcomponentsallowuserstofeel shapes,textures,motionandforcesin3D. Deviceswillalsotrackthemotionsand forcesappliedbytheuserduring interaction.Withcurrenttechnologiesthe userneedstowearorholdaphysical device,butfutureultrasoundbasedhaptic deviceswillofferacontact-freesolution. Standardizationeffortsforhaptic communicationwillallowforaquicker adoptionofhapticcapabilities. INSTANTINTERACTION ANDCOMMUNICATION Communicationbetweenhumansand machineswillbecomemorenatural,tothe pointthatitiscomparabletointerpersonal interaction.Naturaluserinterfacessuchas voiceandgesturewillbecommonplace. Theuseofvision-basedsensorswillallow foranintuitivetypeofinteraction.Tobetter understandhuman-machineinteraction thereisaneedtoevolvetheunderstanding ofkinesiology,ergonomics,cognitive scienceandsociology,andtoincorporate themintoalgorithmsandindustrialdesign. Thiswouldmakeiteasiertoconveya machine’sintentbeforeitinitiatesactions, forexample. Largevolumesof3Dvisualinformation imposehighnetworkcapacitydemands, makingultra-lowlatencyandhigh bandwidthcommunicationtechnologies essential.Enablingthebestuser experiencerequirestheuseofnetwork edgecomputerstoprocessthelarge volumesof3Dvisual,audioandhaptic information.Thissetupsavesdevice batterylifetimeandreducesheat dissipation,aswellasreducingnetwork load. ✱ TECHNOLOGY TRENDS TECHNOLOGY TRENDS ✱ 5 6
  4. 4. SEPTEMBER 10, 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 10, 2018 highly adaptable cyber-physical systems #3 ✱ TECHNOLOGY TRENDS TECHNOLOGY TRENDS ✱ A CYB ER- PHYS I CAL system is a composite of several systems of varying natures that will soon be present in all industry sectors. It is a self-organizing expert system created by the combination of model of models, dynamic interaction between models and deterministic communication. A cyber-physical system presents a concise and comprehensible system overview that humans can understand and act upon. Themainpurposeofacyber-physical systemistocontrolaphysicalprocessand usefeedbacktoadapttonewconditions inrealtime.Itbuildsupontheintegrationof computing,networkingandphysical processes.Anexampleofacyber-physical systemisasmartfactorywhere mechanicalsystems,robots,rawmaterials, andproductscommunicateandinteract. Thisinteractionenablesmachine intelligencetoperformmonitoringand controlofoperationsatallplantlevels. SYNERGISTICINTEGRATIONOF COMPUTATION,NETWORKINGAND PHYSICALPROCESSES Themainchallengeistheorchestration ofthenetworkedcomputationalresources formanyinterworkingphysicalsystems withdifferentlevelsofcomplexity.Cyber- physicalsystemsaretransformingtheway peopleinteractwithengineeredsystems, justastheinternethastransformedthe waypeopleinteractwithinformation. Humanswillassumeresponsibility onawideroperatingscale,supervising theoperationofthemostlyautomated andself-organizingprocess. Acyber-physicalsystemcontains differentheterogeneouselementssuchas mechanical,electrical,electromechanical, controlsoftware,communicationnetwork andhuman-machineinterfaces.Itisa challengetounderstandtheinteractionof thephysical,cyberandhumanworlds. Systemmodelswilldefinetheevolutionof eachsystemstateintime.Anoverarching modelwillbeneededtointegrateallthe respectivesystemmodelswhilecontem- platingallpossibledynamicinteractions. Thisimpliesacontrolprogramthatdelivers adeterministicbehaviortoeach subsystem.Currentdesigntoolsneedto beupgradedtoconsidertheinteractions betweenthevarioussystems,their interfacesandabstractions. MODELOFMODELSCREATES THECYBER-PHYSICALSYSTEM Withinthecyber-physicalsystemall systemdynamicsneedtobeconsidered throughamodelthatinteractswithallthe sub-models.Manyfactorsimpactthe dynamicsoftheinteractionsbetweenthe systems,includinglatency,bandwidthand reliability.Forawirelessnetwork,factors suchasthedevicelocation,thepropagation conditionsandthetrafficloadchangeover time.Thismeansthatnetworksneedtobe modeledinordertobeintegratedinthe modelofmodels. Thetimeittakestoperformataskmay becriticaltoenableacorrectlyfunctioning system.Physicalprocessesare compositionsofmanythingsoccurringin parallel.Amodeloftimethatisconsistent withtherealitiesoftimemeasurementand timesynchronizationneedstobe standardizedacrossallmodels. EXAMPLE:INDUSTRY4.0 Thefactoryofthefutureimplementsthe conceptofIndustry4.0,whichincludesthe transformationfrommassproductionto masscustomization.Thisvisionwillbe realizedthroughlarge-scaleindustrial automationtogetherwiththedigitalization ofmanufacturingprocesses. Humansassumetheroleofsupervising theoperationoftheautomatedandself- organizingproductionprocess.Inthis contextitwillbepossibletorecognizeall thesystemmodelsthatneedtointeract: • Physicalandroboticsystemssuchas conveyors,roboticarmsandautomated guidedvehicles • Controlsystemssuchasrobot controllersandprogrammablelogic controllersforproduction • Softwaresystemstomanageallthe operations • Bigdataandanalytics-based softwaresystems • Electricalsystemstopower machinesandrobots • Communicationnetworks • Sensorsanddevices. Themastermodelconsistsofandinteracts withallthelistedprocessesabove,resulting intherealizationofthefinalproduct. 7 8
  5. 5. SEPTEMBER 10, 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 10, 2018 trust technologies for security assurance #4 ✱ TECHNOLOGY TRENDS TECHNOLOGY TRENDS ✱ TRUS T TECH N O LO G I E S will provide mechanisms to protect networks and offer security assurance to both humans and machines. Artificial intelligence and machine learning are needed to manage the complexity and variety of security threats and the dynamics of networks. Rapidly emerging confidential computing – together with possible future multi-party computation – will facilitate secure cloud processing of private and confidential data. Performance and security demands are driving the development of algorithms and protocols for identities. Theuseofcloudtechnologiescontinuesto grow.Billionsofnewdeviceswithdifferent capabilitiesandcharacteristicswillallbe connectedtothecloud.Manyofthemare physicallyaccessibleandthusexposed andvulnerabletoattackortobeing misusedasinstrumentsofattack.Digital identitiesareneededtoproveownership ofdataandtoensurethatservicesonly connecttoothertrustworthyservices. Flexibleanddynamicauditingand complianceverificationarerequiredto handlenewthreats.Furthermore,thereisa needforautomatedprotectionthatadapts tooperatingmodesandperformsanalytics onthesysteminoperation. PROTECTIONDRIVENBY ARTIFICIALINTELLIGENCE Artificialintelligence,machinelearningand automationarebecomingimportanttools forsecurity.Machinelearningaddresses areassuchasthreatdetectionanduser behavioranalytics.Artificialintelligence assistssecurityanalystsbycollectingand siftingthroughthreatinformationtofind relevantinformationandcomputing responses.However,thereisaneedto addressthecurrentlackofopen benchmarkstodeterminethematurityof thetechnologyandpermitcomparisonof products. Whilethecurrenttrendistocentralize dataandcomputation,security applicationsfortheInternetofThingsand futurenetworkswillrequiremore distributedandhierarchicalapproachesto supportbothfastlocaldecisionsand slowerglobaldecisionsthatinfluencelocal policies. CONFIDENTIALCOMPUTING TOBUILDTRUST Confidentialcomputingusesthefeatures ofenclaves–trustedexecution environmentsandrootoftrust technologies.Codeanddataiskept confidentialandintegrityprotectionis enforcedbyhardwaremechanisms,which enablestrongguaranteesthatdataand processingarekeptconfidentialinthe cloudenvironmentandprevent unauthorizedexposureofdatawhendoing analytics.Confidentialcomputingis becomingcommercialincloudsystems. Researchisunderwaytoovercomethe remainingchallenges,includingimproving theefficiencyofthetrustedcomputing base,reducingcontextswitchoverheads whenportingapplicationsandpreventing sidechannelinformationleakage. Multi-partycomputationenables partiestojointlycomputefunctionsover theircombineddatainputswhilekeeping thoseinputsprivate.Inadditionto protectingtheconfidentialityoftheinput data,multi-partyprotocolsmust guaranteethatmaliciouspartiesarenot abletoaffecttheoutputofhonestparties. Althoughmulti-partycomputationis alreadyusedinspecialcases,itslimited functionalityandhighcomputation complexitycurrentlystandinthewayof wideadoption.Timewilltellifitbecomes aspromisingasconfidentialcomputing. PRIVACYREQUIRESSECURE IDENTITIES Digitalidentitiesarecrucialtomaintaining ownershipofdataandforauthenticating andauthorizingusers.Solutionsthat addressidentitiesandcredentialsfor machinesareequallyimportant.The widespreaduseofwebandcloud technologieshasmadetheneedfor efficientidentitysolutionsevenmore urgent.Inaddition,betteralgorithmsand newprotocolsforthetransportlayer securityprovideimprovedsecurity,lower latencyandreducedoverhead.Efficiency isparticularlyimportantwhen orchestratingandusingidentitiesformany dynamiccloudsystems,suchasthose realizedviamicroservices,forexample. Whenquantumcomputerswithenough computationalpowerareavailable,all existingidentitysystemsthatusepublic- keycryptographywilllosetheirsecurity. Developingnewsecurealgorithmsforthis post-quantumcryptographyeraisan activeresearcharea. 9 10
  6. 6. SEPTEMBER 10, 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 10, 2018 ✱ TECHNOLOGY TRENDS TECHNOLOGY TRENDS ✱ TH E WI RELE SS access network is becoming a general connectivity platform that enables the sharing of data anywhere and anytime for anyone and anything. There is good reason to believe that rapidly increasing data volumes will continue in the foreseeable future. Ultra-reliable connectivity requires ultra-low latency, which will be needed to support demanding use cases. The focus will be on enabling high data rates for everyone, rather than support for extremely high data rates that are only achievable under specific conditions or for specific users. Afewtechnologieswillneedtobe enhancedinordertocreateaubiquitous, high-capacityradionetwork.Thecommon denominatorforthesetechnologiesistheir capabilitytoenableandutilizehigh frequenciesandwidebandwidth operations.Coverageisaddressed throughbeamformingandflexibilityin deviceinterworking.Thechallengeisto supportdatavolumesanddemanding- trafficusecases,withoutacorresponding increaseincostandenergyconsumption. DEVICESACTASNETWORKNODES Toenhancedevicecoverage,performance andreliability,simultaneousmulti-site connectivityacrossdifferentaccess technologiesandaccessnodesisrequired. Wirelesstechnologywillbeusedforthe connectivitybetweenthenetworknodes, asacomplementtofiber-basednetworks. Devicecooperationwillbeusedtocreate virtuallargeantennaarraysonthedevice sidebycombiningtheantennasofmultiple devices.Theborderlinebetweendevices andnetworknodeswillbemorediffuse. Massiveheterogenousnetworkswill haveamuchmoremesh-likeconnectivity. Advancedmachinelearningandartificial intelligencewillbekeytothemanagement ofthisnetwork,enablingittoevolveand adapttonewrequirementsandchangesin theoperatingenvironment. NOSURPRISE–EXPONENTIAL INCREASEDDATARATES Meetingfuturebitratedemandswill requiretheuseoffrequencybandsabove 100GHz.Operationinsuchspectrumwill enableterabitdatarates,althoughonlyfor short-rangeconnectivity.Itwillbean implementationchallengetogenerate substantialoutputpowerandhandleheat dissipation,consideringthesmall dimensionsofTHzcomponentsand antennas.Spectrumsharingwillbefurther enabledbybeamforming,whichismade possiblebythehighfrequency. Integratedpositioningwillbeenabledby high-frequencyandwide-bandwidth operationincombinationwithverydense deploymentsofnetworknodes.High- accuracypositioningisimportantfor enhancednetworkperformanceandisan enablerfornewtypesofend-userservices. Thepositioningofmobiledevices,both indoorandoutdoor,willbeanintegrated partofthewirelessaccessnetworks. Accuracywillbewellbelowonemeter. ANEWTRADE-OFFBETWEEN ANALOGANDDIGITALRADIO FREQUENCYHARDWARE Forthepast20yearstherehasbeen acontinuoustrendtowardmoving functionalityfromtheanalogtothedigital radiofrequencydomain.However,the trendisreversedforverywideband transmissionatveryhighfrequencies, incombinationwithaverylargenumber ofantennas.Thismeansthatanew implementationbalanceandinterplay betweentheanaloganddigitalradio frequencydomainswillemerge. Increasinglysophisticatedprocessingis alreadymovingovertotheanalogdomain. Thiswillsoonalsoincludeutilizing correlationsbetweendifferentanalog signalsreceivedondifferentantennas,for example.Thecompressionrequirements ontheanalog-to-digitalconversionis reduced.Thesplitbetweenanalogand digitalradiofrequencyhardware implementationwillchangeovertimeas technologyandrequirementsevolve. ubiquitous, high-capacity radio #5 11 12

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