Ericsson Technology Review: issue 1, 2020

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
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

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

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

✱ 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

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...

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
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

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

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
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

✱ 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.

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)
(3GPP)
Distributed cloud infrastructure
(ETSI)
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

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

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

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
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
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.

✱ 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

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

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
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

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
Mobility
Coverage
Interference
Recommendations and actions
Mobility
Coverage
Interference
ERICSSON CONTINUES
TO INVEST SIGNIFICANT
R&D RESOURCES IN THE
USE OF AI

Ericsson Technology Review: issue 1, 2020

Ericsson Technology Review: issue 1, 2020

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Ericsson Technology Review: issue 1, 2020