The document discusses the future of networks, emphasizing the integration of machines and human intelligence through advancements like zero-touch networks, the Internet of Skills, and cyber-physical systems. It highlights the importance of trust technologies for security and the need for ultra-low latency connectivity to support a growing volume of data. Overall, it envisions a highly interactive and efficient collaboration between humans and machines, facilitated by intelligent network applications.

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. 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. 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 ✱
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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.
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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.
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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
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