爱立信白皮书 Massive MIMO for 5G networks

EricssonWhitePaper

BNEW-23:004809UEN

UpdatedFebruary2023

MassiveMIMO

for5Gnetworks

RecenttechnologydevelopmentshavemadeMassiveMIMOthepreferredoption

forlarge-scaledeploymentsin5Gmobilenetworks.MassiveMIMOenables

state-of-the-artbeamformingandMIMOtechniquesthatarepowerfultools

forimprovingend-userexperience,capacity,andcoverage.Asaresult,Massive

MIMOsignificantlyenhancesnetworkperformanceinbothuplinkanddownlink.

FindingthemostsuitableMassiveMIMOvariantstoachievethepotential

performancegainsandcostefficiencyinaspecificnetworkdeploymentrequires

anunderstandingofthecharacteristicsofMassiveMIMOsolutions.

MassiveMIMOfor5Gnetworks4

WhatisaMassiveMIMOsolution?

February2023

Whatisa

MassiveMIMO

solution?

AMassiveMIMOsolutionorsimplyMassiveMIMO(formerlycalledadvancedantenna

system,orAAS)isacombinationofaMassiveMIMOradioandasetofMassiveMIMO

features.AMassiveMIMOradioconsistsofanantennaarraytightlyintegratedwiththe

hardwareandsoftwarerequiredforthetransmissionandreceptionofradiosignals,and

signalprocessingalgorithmstosupporttheexecutionoftheMassiveMIMOfeatures.

Comparedtoconventionalsystems,thissolutionprovidesmuchgreateradaptivityand

steerability,intermsofadaptingtheantennaradiationpatternstorapidlytime-varying

trafficandmulti-pathradiopropagationconditions.Inaddition,multiplesignalsmaybe

simultaneouslyreceivedortransmittedwithdifferentradiationpatterns.

Multi-antennatechniques

Multi-antennatechniques(herereferredtoasMassiveMIMOfeatures)includeallvariants

ofbeamforming,null-forming,andMIMO.ApplyingMassiveMIMOfeaturestoaMassive

MIMOradioresultsinsignificantperformancegainsbecauseofthehigherdegreesof

freedomprovidedbyalargenumberofradiochains.

Beamforming

Duringtransmission,beamformingistheabilitytodirectradiopowerthroughtheradio

channeltowardaspecificreceiver,asshowninthetopleftquadrantofFigure1.By

adjustingthephaseandamplitudeofthetransmittedsignals,constructiveadditionofthe

correspondingsignalsattheuserequipmentreceivercanbeachievedwhichincreasesthe

receivedsignalstrengthand,thus,theend-userthroughput.Similarlyduringreception,

beamformingistheabilitytocollectthesignalpowerfromaspecifictransmitter.Thebeams

formedareconstantlyadaptedtothesurroundingstogivehighperformanceinbothuplink

(UL)anddownlink(DL).

MassiveMIMOfor5Gnetworks5

WhatisaMassiveMIMOsolution?

February2023

A.BeamformingB.Generalizedbeamforming

ServesingleusersbydirectingtheServesingleuserthroughsendingthe

energytowardtheuser.samedatastreamindifferentdirections

andpossiblyformingzeros(nulls)in

directionsofotherusers.

X

C.Single-userMIMOD.Multi-userMIMO

IncreasedataratesbytransmittingAthighload,servemoreusers

severaldatastreamstoauser.simultaneously.

X

Figure1:BeamformingandMIMOwiththedifferentcolorsofthefilledbeamsthatrepresent

differentdatastreams

Althoughoftenveryeffective,transmittingpowerinonlyonedirectiondoesnotalways

provideanoptimumsolution.Inmulti-pathscenarios,wheretheradiochannelcomprises

multiplepropagationpathsfromthetransmittertoreceiverthroughdiffractionaround

cornersandreflectionsagainstbuildingsorotherobjects,itisbeneficialtosendthesame

datastreaminseveraldifferentpaths(directionand/orpolarization)withphasesand

amplitudescontrolledinawaythattheyaddconstructivelyatthereceiver[2].Thisis

referredtoasgeneralizedbeamforming,asshownintheupperrightquadrantofFigure1.

Aspartofgeneralizedbeamforming,itisalsopossibletoreduceinterferencetootherUEs,

whichisknownasnull-forming.Thisisachievedbycontrollingthetransmittedsignalsina

waythattheycanceleachotheroutatUEsthatwouldotherwisebeinterfered.

Notethattheconceptofgeneralizedbeamformingcanbeconsiderablymorecomplexthan

illustratedinFigure1,seeforexample[2,Ch.6].

MIMO(MultipleInput,MultipleOutput)techniques

Spatialmultiplexing,herereferredtoasMIMO,istheabilitytotransmitmultipledata

streams,usingthesametimeandfrequencyresource,whereeachdatastreamcanbe

beamformeddifferently.ThepurposeofMIMOistoincreasethroughput.MIMObuilds

onthebasicprinciplethatwhenthereceivedsignalqualityishigh,itisbettertoreceive

multiplestreamsofdatawithreducedpowerperstream,thanonestreamwithfullpower.

MassiveMIMOfor5Gnetworks6

WhatisaMassiveMIMOsolution?

February2023

Thepotentialislargewhenthereceivedsignalqualityishigh,andthebeamscarryingthe

datastreamsaredesignednottointerferewitheachother.Thepotentialdiminisheswhen

themutualinterferencebetweenstreamsincreases.MIMOworksinbothULandDL,butfor

simplicity,thedescriptionbelowwillbebasedontheDL.Thedetailsofhowtheseworkare

explainedindetailin[2,Ch.4,6,13].

Single-userMIMO(SU-MIMO)istheabilitytotransmitoneormultipledatastreams,also

calledlayers,fromonetransmittingarraytoasingleuser.SU-MIMOcantherebyincrease

thethroughputforthatuserandincreasethecapacityofthenetwork.Thenumberoflayers

thatcanbesupported,calledtherank,dependsontheradiochannelandtheminimum

numberofantennasoneachside.TodistinguishbetweenDLlayers,aUEmusthaveatleast

asmanyreceiverantennasastherearelayers.

SU-MIMOcanbeachievedbysendingdifferentlayersondifferentpolarizationsinthesame

direction.SU-MIMOcanalsobeachievedinamulti-pathenvironment,wherethereare

manyradiopropagationpathsofsimilarstrengthbetweentheMassiveMIMOradioandthe

UE,bysendingdifferentlayersondifferentpropagationpaths,asshowninthebottomleft

quadrantofFigure1.

Inmulti-userMIMO(MU-MIMO),whichisshowninthebottomrightquadrantofFigure1,

differentlayersinseparatebeamsaretransmittedtodifferentusersusingthesametime

andfrequencyresource,therebyincreasingthenetworkcapacity.TouseMU-MIMO,the

systemneedstofindtwoormoreusersthatneedtotransmitorreceivedataatthevery

sametime.Also,forefficientMU-MIMO,theinterferencebetweentheusersshouldbekept

low.Thiscanbeachievedbyusinggeneralizedbeamformingwithnullformingsuchthat

whenalayerissenttooneuser,nullsareformedinthedirectionsoftheothersimultaneous

users.

TheachievablecapacitygainsfromMU-MIMOdependonreceivingeachlayerwithgood

signal-to-interference-and-noise-ratio(SINR).AswithSU-MIMO,thetotalDLpoweris

sharedbetweenthedifferentlayers,andthereforethepower(andthusSINR)foreachuser

isreducedasthenumberofsimultaneousMU-MIMOusersincreases.Also,asthenumberof

usersgrows,theSINRwillfurtherdeteriorateduetomutualinterferencebetweentheusers.

Therefore,thenetworkcapacitytypicallyimprovesasthenumberofMIMOlayersincreases

toapointatwhichpowersharingandinterferencebetweenusersresultindiminishing

gainsandeventuallyalsolosses.

ItshouldbenotedthatthepracticalbenefitsofmanylayersinMU-MIMOarelimitedbythe

factthat,intoday'srealnetworks,evenwithahighnumberofsimultaneouslyconnected

users,theretendnottobemanyuserswhowanttoreceivedatasimultaneously.Thisisdue

tothebursty(chatty)natureofdatatransmissiontomostusers.SincetheMassiveMIMO

andthetransportnetworkmustbedimensionedforthemaximumnumberoflayers,the

CSPneedstoconsiderhowmanylayersarerequiredintheirnetworks.Intypicalmobile

broadband(MBB)deploymentswiththecurrent64T64RMassiveMIMOvariants,thevast

majorityoftheDLandULcapacitygainscanbeachievedwithupto8layers.Forother

servicesthanMBB,e.g.fixedwirelessaccess(FWA),thereisuseformorelayerscompared

toMBB.EightlayersarehoweverusuallysufficientalsoforFWA.

MassiveMIMOfor5Gnetworks7

WhatisaMassiveMIMOsolution?

February2023

AcquiringchannelknowledgeforMassiveMIMO

Knowledgeoftheradiochannelsbetweentheantennasoftheuserandthoseofthe

basestationisakeyenablerforbeamformingandMIMO,bothforULreceptionandDL

transmission.ThisallowstheMassiveMIMOtoadaptthenumberoflayersanddetermine

howtobeamformthem.

ForULreceptionofdatasignals,channelestimatescanbedeterminedfromknownsignals

receivedontheULtransmissions.Channelestimatescanbeusedtodeterminehowto

combinethesignalsreceivedtoimprovethedesiredsignalpowerandmitigateinterfering

signals,eitherfromothercellsorwithinthesamecell.

DLtransmission,ontheotherhand,istypicallymorechallengingthanULreceptionbecause

channelknowledgeneedstobeavailablebeforetransmission.Whereasbasicbeamforming

hasrelativelylowrequirementsonthenecessarychannelknowledge,generalized

beamforminghashigherrequirementsasmoredetailsaboutthemulti-pathpropagation

areneeded.Furthermore,mitigatinginterferencebyusingnull-formingforMU-MIMOis

evenmorechallenging,sincemoredetailsofthechannelstypicallyneedtobecharacterized

withhighgranularityandaccuracy.TherearetwobasicwaysofacquiringDLchannel

knowledge:UEfeedbackandULchannelestimation.

ToacquireDLchannelknowledgebasedonUEfeedback,thebasestationtransmitsknown

signalsintheDLthatUEscanuseforchannelestimation.Relevantchannelinformationis

thenextractedfromthechannelestimatesandfedbacktothebasestation.

WhattypeofDLchannelknowledgecanbeacquiredbasedonULchannelestimation,also

referredtoasULsounding,dependonwhethertimedivisionduplex(TDD)orfrequency

divisionduplex(FDD)isused.ForTDD,thesamefrequencyisusedforbothULandDL

transmission.Sincetheradiochannelisreciprocal(thesameinULandDL),detailedshort-

termchannelestimatesfromULtransmissionofknownsignalscanbeusedtodetermine

theDLtransmissionbeams.Thisisreferredtoasreciprocity-basedbeamforming.Forfull

channelestimation,signalsshouldbesentfromeachUEantennaandacrossallfrequencies.

ForFDD,wheredifferentfrequenciesareusedforULandDL,thechannelisnotfully

reciprocal.Longer-termchannelknowledge(suchasdominantdirections)can,however,be

obtainedbysuitableaveragingofULchannelestimatestatistics.

ThesuitablechannelknowledgeschemetousedependsonULcoverageandUE

capabilities.IncaseswhereULcoverageislimiting,UEfeedbackoffersamorerobust

operation,whereasfullULchannelestimationisapplicableinscenarioswithgood

coverage.Inshort,bothreciprocityandUEfeedback-basedbeamformingareneeded.

Antennaarraystructure

Thepurposeofusingarectangularantennaarray,asshowninsectionAofFigure2,isto

enablehigh-gainbeamsandmakeitpossibletosteerthosebeamsoverarangeofanglesin

horizontalandverticaldirections.Thegainisachieved,inbothULandDL,byconstructively

combiningsignalsfromseveralantennaelements.Typically,themoreantennaelements

thereare,thehigherthegain.Steerabilityisachievedbyindividuallycontrollingthe

amplitudeandphaseofsmallerpartsoftheantennaarray.Thisisusuallydonebydividing

MassiveMIMOfor5Gnetworks8

WhatisaMassiveMIMOsolution?

February2023

theantennaarrayintosocalledsub-arrays(groupsofnon-overlappingelementspairs),as

showninsectionCofFigure2andbyapplyingtwodedicatedradiochainspersub-array

(oneperpolarization)toenablecontrol,asshowninsectionD.Inthisway,itispossibleto

controlthedirectionandotherpropertiesofthecreatedbeam.

A.B.C.D.

Figure2:Atypicalantennaarray(A)ismadeupofrowsandcolumnsofindividualdual-polarized

antennaelementpairs(B).Antennaarrayscanbedividedintosub-arrays(C),witheachsub-array

(D)connectedtotworadiochains,normallyoneperpolarization.

Toseehowanantennaarraycreatessteerablehigh-gainbeams,westartwithan

antennaarrayofaspecificsize,whichisthendividedintosub-arraysofdifferentsizes.For

illustrativepurposes,wedescribeonlyonedimension.Thesameprinciplesdo,however,

applytobothverticalandhorizontaldimensions.

Thearraygainisreferredtoasthegainachievedwhenallsubarraysignalsareadded

constructively(inphase).Thesizeofthearraygain,relativetothegainofonesub-array,

dependsonthenumberofsub-arrays–forexample,twosub-arraysgiveanarraygainof2

(i.e.3dB).Bychangingthephasesofthesub-arraysignalsinacertainway,thisgaincanbe

achievedinanydirection,asshowninsectionAofFigure3.

Eachsub-arrayhasacertainradiationpatterndescribingthegainindifferentdirections.

Thegainandbeamwidthdependonthesizeofthesub-arrayandthepropertiesofthe

individualantennaelements.Thereisatrade-offbetweensub-arraygainandbeam

width–thelargerthesub-array,thehigherthegainandthenarrowerthebeamwidth,as

illustratedinsectionBofFigure3.

Thetotalantennagainistheproductofthearraygainandthesub-arraygain,asshown

insectionCofFigure3.Thetotalnumberofelementsdeterminesthemaximumgainand

thesub-arraypartitioningallowsthesteeringofhigh-gainbeamsovertherangeofangles.

Moreover,thesub-arrayradiationpatterndeterminestheenvelopeofthenarrowbeams

(thedashedshapeinsectionCofFigure3).Thishasanimplicationonhowtochoosean

antennaarraystructureinarealdeploymentscenariowithspecificcoveragerequirements.

Sinceeachsub-arrayisnormallyconnectedtotworadiochainsandeachradiochain

isassociatedwithacostintermsofadditionalcomponents,itisimportanttoconsider

MassiveMIMOfor5Gnetworks9

WhatisaMassiveMIMOsolution?

February2023

theperformancebenefitsofadditionalsteerabilitywhenchoosingacost-efficientarray

structure.

A.B.C.

6dB

4α4α

6dB

3dB

2α2α

3dB

6dB

αα

0dB

ArraygainxSub-arraygain=Totalantennagain

Figure3:Anarrayofsub-arrayssupportinghightotalantennagainandsteerability

MassiveMIMOfor5Gnetworks10

Deploymentscenarios

February2023

Deployment

scenarios

DeterminingwhatkindofMassiveMIMOconfigurationismostappropriateandcost-

effectiveforaparticulardeploymentscenariorequiresamixofknowledgeaboutthe

scenario,possiblesiteconstraints,andavailableMassiveMIMOfeatures,particularlythe

needforverticalsteerabilityofbeams,theapplicabilityofreciprocity-basedbeamforming

andthegainfromMU-MIMO.Itshouldbenotedthathorizontalbeamformingisavery

effectivefeaturethatprovideslargegainsinallscenariossincetheusersaregenerally

spreadinthehorizontaldimension.Therefore,alargenumberofcolumnsisbeneficialinall

scenarios.

WehavechosenthreetypicalusecasestoillustratedifferentaspectsofMassive

MIMOdeployment:rural/suburban,urbanlow-rise,anddenseurbanhigh-rise.More

comprehensiveandpracticallyusefulrecommendationscanbefoundin[3].Thescenarios,

includingrelevantcharacteristics,suitableMassiveMIMOconfigurations,andperformance

potentialaredepictedinFigure4.Moreelaborateevaluationsoftheperformance

achievablewithMassiveMIMOareavailableinreference[2]and[3].

MassiveMIMOfor5Gnetworks11

Deploymentscenarios

February2023

2x1sub-array-64T64RRelativecapacity

Denseurbanhigh-rise

ISD-200-500m

A.

2T16T32T64T

MU-MIMOSU-MIMO

4x1sub-array-32T32RRelativecapacity

Urbanlow-rise

ISD-500-1000m

B.

2T16T32T64T

MU-MIMOSU-MIMO

8x1sub-array-16T16RRelativecapacity

Table1:Comparisonofthesimplestregular5GdevicewithSurburbanthesimplestandrural

andthemostadvancedRedCapdeviceISD>1000m

C.

2T16T32T64T

MU-MIMOSU-MIMO

Figure4:SuitableMassiveMIMOconfigurations,schematicMU-MIMOandSU-MIMOusage

ranges,andtypicalcapacitygainsindifferentdeploymentscenarios

Deploymentscenario#1:Denseurbanhigh-rise

AsdepictedinsectionAofFigure4,thedenseurbanhigh-risescenarioischaracterized

byhigh-risebuildings,shortinter-site-distances(ISDs)of200-500m,largetrafficvolume,

andhighsubscriberdensitywithsignificantuserspreadintheverticaldimension.Themain

networkevolutiondriverhasincreasedcapacityorequivalentlyhighend-userthroughput

foragiventrafficload.

Forconventionalnon-beamformedsystemssuchas2T2R,theverticalspreadofusers

incombinationwiththesmallISDcreatesasituationwheremanyusersareoutsidethe

verticalmainbeamofthenearestbasestation.Togetherwiththehighsitedensity,this

leadstoasituationwherethesignalsfrominterferingbasestationsarestrong,andsevere

interferenceproblemsmayoccur.

DesiredMassiveMIMOcharacteristicsinthedenseurbanhigh-risescenarioincludean

antennaarealargeenoughtoensuresufficientcoverage(ULcell-edgedatarate).Further,

theverticalcoveragerangeneedstobelargeenoughtocovertheverticalspreadofusers.

Thiscallsforsmallsub-arrays,whichhaveawidebeamintheverticaldirection.Partitioning

theantennaintosmallverticalsub-arraysresultsinhigh-gainbeamsthatcanbesteered

overalargerangeofanglesandeffectivelyaddressestheinterferenceproblemsseenwith

conventionalsystems.TheMassiveMIMOradioneedstohaveasufficientnumberofradio

chainstosupporttherelativelylargenumberofsub-arrays.Thegoodcoverageandlarge

spreadofusersmeanthatthepotentialforreciprocity-basedbeamformingandMU-MIMO

witharelativelylargenumberofmultiplexedusersishigh,andtheMassiveMIMOradio

MassiveMIMOfor5Gnetworks12

Deploymentscenarios

February2023

shouldsupportthesetechniques.Agoodtrade-offbetweencomplexityandperformance

couldbeachievedwith64radiochainscontrollingsmallsub-arrays.

Deploymentscenario#2:Urbanlow-rise

Theurbanlow-risescenarioillustratedinsectionBofFigure4representsmanyofthelarger

citiesaroundtheworld,includingtheoutskirtsofmanyhigh-risecities.Basestationsare

typicallydeployedonrooftops,withinter-sitedistancesofafewhundredmeters.Compared

tothedenseurbanhigh-risescenario,trafficperareaunitislower.Thereisgenerallya

mixofbuildingtypes,whichcreatesmultipathpropagationbetweentheMassiveMIMO

radioandtheUE.MaximizingtheantennaareaisimportantforimprovingtheULcell-edge

datarates,especiallyforhigherfrequencybandsemployingTDD.DuetolargerISDsand

decreasedverticalspreadofusers(lowerbuildings),theverticalcoveragerangecanbe

decreasedcomparedtodenseurbanhigh-rises;hence,largerverticalsub-arrayscanbe

usedandthereislessgainfromverticalbeamforming.Usinglargersub-arraysforagiven

antennaareameansthatfewerradiochainsarerequired.Reciprocity-basedbeamforming

schemeswillworkformostusers,buttherewillbeuserswithpoorcoveragethatneedto

relyontechniquessuchasfeedback-basedbeamforming.MU-MIMOisalsoappropriate

athighloadsduetothemulti-pathpropagationenvironment,goodlinkqualities,andUE

pairingopportunities.Agoodtrade-offbetweencomplexityandperformanceisaMassive

MIMOradiowith16to32radiochains.

Deploymentscenario#3:Rural/suburban

Ruralorsuburbanmacroscenarios,asdepictedinsectionCofFigure4,arecharacterized

byrooftoportower-mountedbasestationswithinter-sitedistancesrangingfromone

toseveralkilometers,lowormediumpopulationdensityandverysmallverticaluser

distribution.ThisscenariocallsforaMassiveMIMOradiowithalargeantennaareaand

theabilitytosupporthorizontalbeamforming.Verticalbeamforming,however,doesnot

provideanysignificantgainsastheverticaluserspreadislow.Therefore,largevertical

sub-arrayswithsmallverticalcoverageareasarepossible.Reciprocity-basedbeamforming

issupportedforasmallerfractionofusersthanintheotherscenarios,andMU-MIMOgains

aremorelimited.Agoodtrade-offbetweencomplexityandperformanceisaMassiveMIMO

radiowith8to16radiochains.

MassiveMIMOfor5Gnetworks13

EvolutionofMassiveMIMO

February2023

Evolutionof

MassiveMIMO

ThebriefexplanationofMassiveMIMOabovereflectsthesolutionsinusetodate(2022-

Q4).TheevolutionofMassiveMIMOisveryrapid,andseveraltracksarebeinginvestigated

toachievehigherperformance.Afewexamplesincludetheuseofhighernumbersofradio

chains,largerarraypanels,theuseofnewandhigherfrequencies,andtheuseofmultiple

transmissionpoints(multi-TRP).Inadditiontoadvancementsintechnologiesspecific

toMassiveMIMO,theuseofinterworkingbetweenMassiveMIMOandconventional

radiosonotherfrequencybandsaddadditionalcapacitybeyondthesumofthetwo,

respectively.Otherdevelopingtechnologies,e.g.artificialintelligenceandmachine

learning(AI/ML)willalsobeappliedinMassiveMIMOtoimproveperformance.Yetother

technologydevelopments,relatingtoforexampleenergyperformance,costefficiency,

andsitedeployment,arecomingintousetomakeMassiveMIMOahighlycompetitiveand

commerciallyviableoptionformassdeploymentinalargevarietyofscenarios.

MassiveMIMOisalsousedtosupportagrowingnumberofservicesinadditiontoMBB.

TodayMassiveMIMOisalreadyusedforFWA,IoTandnewindustriesandinthenear

futurealsoXRservices.Withthedevelopmentofprivatenetworks,thenumberofservices

supportedisexpectedtogrowveryfast.

MassiveMIMOfor5Gnetworks14

Conclusion

February2023

Conclusion

RecenttechnologydevelopmentshavemadeMassiveMIMO(advancedantennasystems)

apreferredoptionforlarge-scaledeploymentsin4Gand5Gmobilenetworks.Massive

MIMOenablesstate-of-the-artbeamformingandMIMOtechniquesthatarepowerful

toolsforimprovingend-userexperience,capacity,andcoverage.Asaresult,MassiveMIMO

significantlyenhancesnetworkperformanceinbothuplinkanddownlink.

TheMassive-MIMOsolutiontoolboxisversatileandselectingasuitableMassiveMIMO

(2x)solutiondependsonaspectssuchasdeploymentenvironment,trafficloadvariations

andease-of-deployment.MassiveMIMOproductsprovidesignificantbenefitsacrossa

verywiderangeofdeploymentscenarios,makingitpossibleformobilenetworkoperators

toenjoythebenefitsofcost-efficientMassiveMIMOacrosstheirnetworks.MassiveMIMO

solutionshavealreadyproveninvaluableinmany5Gdeployments,andtheirimportance

willlikelytoincreaseevenfurtherinfuturenetworkdeployments.

MassiveMIMOfor5Gnetworks15

Keyterms

February2023

Keyterms

MassiveMIMOradio

Hardwareunitthatcomprisesanantennaarray,radiochains

andpartsofthebaseband,alltightlyintegratedtofacilitateMassiveMIMOfeatures

MassiveMIMOfeature

Amulti-antennafeature(suchasbeamformingorMIMO)thatcanbeexecutedinthe

MassiveMIMOradio,inthebasebandunitorboth

MassiveMIMO

MassiveMIMOradio+MassiveMIMOfeatures

MassiveMIMOfor5Gnetworks16

References

February2023

References

1.EricssonMobilityReport,June2022availableat

/49d3a0/assets/local/reports-papers/mobility-report/

documents/2022/ericsson-mobility-report-june-2022.pdf

2.Asplund,etal,“AdvancedAntennaSystemsfor5GNetworkDeployments:Bridging

theGapBetweenTheoryandPractice”,1stEdition,Elsevier2020,ISBN:978-0-12-

820046-9,AdvancedAntennaSystemsfor5GNetworkDeployments-1stEdition

()

3.Asplundetal,“TheMassiveMIMOhandbook”,Ericsson2022

/Massive-MIMO-handbook-extended-version-download.

html

MassiveMIMOfor5Gnetworks17

Furtherreading

February2023

Furtherreading

1.EricssonTechnologyReview,Designingforthefuture:the5GNRphysicallayer,

availableat:/en/ericsson-technology-review/archive/2017/

designing-for-the-future-the-5g-nr-physical-layer

2.EricssonTechnologyReview,EvolvingLTEtofitthe5