软银：AI-RAN - 电信基础设施迈向人工智能时代 AI-RAN - Telecom Infrastructure for the Age of Al

AI-RAN:Telecom

©SoftBankCorp.

Contents

ExecutiveSummary 4

1.AI-RANVision:ShapingtheFutureofTelecom 5

1.1TelecomChallenges:BalancingMassiveCapitalInvestmentswithROI 5

1.2Opportunities:TransformingNetworkInfrastructurethroughAI-RAN 6

1.3FromCostCentertoProfitCenter 7

2.RANEvolution:FromdRAN,vRAN,CloudRAN,OpenRANtoAI-RAN 7

2.1KeyDevelopmentsinRANEvolution 7

2.2AI-NativeNetworks:TheRoleofAIinRANTransformation 9

2.3AI-RANDefinitions 9

3.HistoryofSoftBank'sAI-RANR&D 10

3.1EarlyResearchandAI-RANDevelopment 10

3.2ApplicationsofSoftBankAI-RANResearch 11

3.3PartnershipsandCollaboration 12

4.gRAN:GPU-basedAI-RANArchitecture 13

4.1KeyCharacteristicsofgRAN 13

4.2TheArchitectureofgRAN-basedAI-RAN 14

4.3gRANCaseStudy:NVIDIAAIAerial 15

5.IntroductionofAITRASbySoftBank 17

5.1KeyFeaturesofAITRAS 17

5.2KeyComponentsofAITRAS 17

5.3AI-NativeOrchestration 19

5.4EdgeAI 20

5.5KeyBenefitsofAITRAS 21

6.AITRASEvaluation 22

6.1OutdoorTestbedforAITRAS 22

6.2AITRASPerformanceEvaluation 24

6.3SoftBank’sL1EnhancementsinAITRAS 25

7.AI-and-RANVirtualizedInfrastructureinAITRAS 26

7.1SoftBankAI-and-RANApproach 26

7.2HardwareandResourceManagement 26

7.3AITRASAI-and-RANOrchestrator 27

7.4AgenticAI-ServerlessAPIPoweredbyNVIDIAAIEnterprise 28

7.5MeetingHighAvailabilityandPerformanceStandards 30

7.6SustainabilityandEnergyEfficiency 30

©SoftBankCorp.

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8.AITRASAIApplications 31

8.1TheShifttoComputing-CentricArchitecture 31

8.2UseCasesfortheAITRASAI-on-RAN 31

9.StrategicBusinessModelsandRevenueGeneration 35

9.1DemandForecasting,CustomerSegmentation,andBusinessModels 35

9.2AITRASAI-and-RANforNewRevenueGeneration 37

9.3TCOAnalysis 37

10.CaseStudy:AI-RANTCOAnalysis 37

10.1AI-RANDeploymentSimulationinUrbanArea,Tokyo 37

10.2RegionalPeakTrafficVariations 38

10.3ROIAnalysisofAI-RANwithNVIDIAGB200-NVL2 39

11.Conclusion 41

11.1ChartingtheFutureofTomorrow’sNetworks 41

11.2Long-TermVisionandSustainableGrowthStrategies 42

References 44

Acknowledgment 45

Glossary 45

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ExecutiveSummary

SoftBank’sAI-RANinitiativeaimstorevolutionizethetelecomindustrybyintegratingArtificialIntelligence(AI)intoRadioAccessNetwork(RAN),transformingtraditionalnetworksfromcostcentersintointelligent,revenue-generatingplatforms.Withmobiledatatrafficcontinuouslygrowing,AI-RANisexpectedtomeetthedualchallengesofrisinginfrastructurecostsandintensifyingmarketcompetition.Thisapproachisexpectedtoenabletelecomoperatorstooptimizenetworkperformance,reducecosts,andcreatenewrevenuestreamsthroughAI-enabledservices.

AI-RANmaybeimplementedleveragingasoftware-defined,GPU-poweredarchitecturecalledgRAN(GPU-basedRAN).Thisadvancedarchitecturesupportshigh-performancenetworkoperationsbyutilizingtheparallelprocessingpowerofGPUs.gRANenablesreal-timedataprocessing,intelligentresourcemanagement,andscalablemulti-tenantoperations.AsthesameplatformsupportsbothnetworkandAIworkloads,gRANoffersunparalleledflexibility,enablingseamlessintegrationofRANservicesandAI-nativeapplicationssuchasautonomousdriving,real-timerobotics,andedgecomputing.

SoftBank’sAI-RANproduct,AITRAS,exemplifiestheconvergenceofAIandtelecominfrastructure.AITRASintegratesRANandAIworkloadsintoasingle,AI-nativecomputingenvironment,offeringcarrier-gradeRANfunctionalitywithenhancedscalabilityandefficiency.Thesystemsupportsmulti-tenantoperations,enablingnetworkproviderstorunAIservicesalongsidetraditionalnetworkfunctions,creatingnewrevenueopportunities.AITRASispoweredbyNVIDIAGH200GraceHopperSuperchip,whichenablereal-timeAIinferenceandnetworkmanagementwithoptimalpowerefficiency.

FieldandlaboratoryevaluationshaveconfirmedAITRAS’sabilitytodelivercarrier-gradestability,higherenergyefficiency,andcost-efficientoperations.Inurbantrials,thesystemsuccessfullysupportedhigh-densitytrafficscenarios,whilelabtestsconfirmedthatitspowerconsumptionwascomparabletothatofcurrentRANsystems,despitehandlingsignificantlyhigherworkloads.ThisbalancebetweenperformanceandsustainabilitypositionsAI-RANasatransformativeforceintelecominfrastructure.

Toaccelerateindustryadoption,SoftBankplayedaleadingroleinestablishingtheAI-RANAllianceincollaborationwithmajortechnologypartnerssuchasNVIDIA,Arm,Ericsson,Nokia,Samsung,andT-Mobile.Thisallianceisfosteringinnovationthroughcollaborativeresearchanddevelopmentactivities,advancingAI-RANtechnologieswhilealigningwiththeglobalstandardssetbyorganizationslike3GPPandO-RANAlliance.

SoftBankenvisionsaphaseddeploymentroadmapforAITRAS,SoftBank’sAI-RANproduct,beginningwithOver-the-Airpilotinafieldareain2024,followedbycommercializationby2026.

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1.AI-RANVision:ShapingtheFutureofTelecom

ThevisionofSoftBankAI-RANR&DistorevolutionizetelecommunicationsbyintegratingAIintothecoreofRANinfrastructure,transformingtraditionalRANintointelligent,adaptive,andrevenue-generatingplatforms.

Figure1.AI-RAN:AIandRANintegration

1.1TelecomChallenges:BalancingMassiveCapitalInvestmentswithROI

Thetelecomindustryisfacingsignificantcapitalexpenditurepressuresduetorapidlyevolvingtechnologiesandincreasingdatademands.TheGSMA'sTheMobileEconomy2024report

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revealsthatintheglobalmobilemarket,totaloperatorrevenuesareprojectedtogrowfrom$1.11trillionin2023to$1.25trillionby2030,representingamodestcompoundannualgrowthrate(CAGR)of1.74%.However,totalcapitalinvestmentsthrough2030areestimatedat$1.5trillion,exceedingtotalsingle-yearrevenues.Thishighlightsacriticalchallengefacedbyoperatorsworldwide.

Foremostamongthesechallengesisthesubstantialinvestmentcostassociatedwith5Gnetworkdeployment.Newinfrastructurerequirements,suchastheutilizationofhigherfrequencybandsandthemassdeploymentofMIMOantennas,necessitatesignificantfunding.Additionally,theimpactofincreasedtrafficfromgenerativeAIapplicationslikenewlyemergingLargeLanguageModels(LLMs)oninfrastructuremustalsobeconsidered.Thesupplyofequipmentforthisinfrastructureiscurrentlydependentonafewspecificvendors,makingitdifficulttoreducecostsandencouragecommoditization.Additionally,therapidproliferationofIoTdevicesandthegrowingpopularityofhigh-definitionvideostreamingnecessitatecontinuednetworkcapacityexpansion.Meanwhile,intensepricecompetitionin

1GSMA,TheMobileEconomy2024Report

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telecomservicesmakesitincreasinglydifficulttorecoverinvestmentsthroughtraditionaldataservicefeemodels.Furthermore,theemergenceofOver-The-Top(OTT)providerswhooperatewithouttheirowncommunicationsinfrastructureimpactstelecomoperators'profitability.

AccordingtoGSMA'sTheMobileEconomy2024report,despitediscussionsaboutapotentialslowdowningrowth,monthlyglobalmobiledatatrafficperconnectionsawasignificantincreasefrom10.2GBin2022to12.8GBin2023,representingthelargestabsolutegrowthsincedatatrackingbeganin2016.Lookingforward,itisprojectedthattotalmobiledatatrafficwillgrowatanaverageannualrateof23%between2023and2030,andexceed465exabytes(EB)permonthbytheendofthedecade.Thisnetworkresourcestrainisforcingtelecomoperatorstomakesubstantialcapitalinvestments.Consequently,dependingontheirrevenuemodels,operatorsfacetheriskofbeingunabletorecovertheirincreasinginvestmentcosts,presentingacriticalmanagementchallenge.

Concurrently,pricecompetitionfortelecomserviceshasintensified,makingitchallengingtorecoupinvestmentsthroughtraditionaldatacommunicationfeerevenuemodels.

Inthiscontext,telecomoperatorsareconfrontedwiththechallengeofimprovinginvestmentefficiency.Specifically,theyfacetwokeyissues:reducinginfrastructuredevelopmentcostsandcreatingnewrevenuestreams.Thisnecessitatesnotonlymoreefficientoperationandgreatercostreductionsinnetworkinfrastructure,butalsothedevelopmentofvalue-addedservicesandtheestablishmentofnewbusinessmodelstosecureadditionalrevenuesources.

1.2Opportunities:TransformingNetworkInfrastructurethroughAI-RAN

AI-RANpresentsauniqueopportunitytofundamentallytransformnetworkinfrastructure,makingitmoreadaptable,efficient,andcapableofsupportingnewAIservices.ByleveragingAI,telecomoperatorscanoptimizenetworkoperationsinrealtime,improveresourceutilization,andintroducenewrevenue-generatingopportunities.

OneofthekeyopportunitiesofferedbyAI-RANisitsabilitytoshiftfromastatic,hardware-dependentnetworkarchitecturetoadynamic,AIandsoftware-drivenapproach.AIallowsforintelligentdecision-makingatthenetworkedge,enablingreal-timeresponsestotrafficconditions,userdemand,andservicerequirements.Thislevelofadaptabilityensuresthatnetworkswillalwaysoperateatpeakefficiency,providebetterqualityofservice,andsuppressenergyconsumption.

Furthermore,AI-RANopensthedoortonewserviceofferingsthatwerepreviouslynotfeasible.Forexample,advancednetworkslicing,enabledbyAI-drivenresourcemanagement,allowsoperatorsto

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createcustomizedend-to-endvirtualnetworkstailoredtothespecificneedsofdifferentcustomersegments,suchaslow-latencyconnectionsforgamingandLLMinferencingandhigh-reliabilitynetworksforenterprisemissioncriticalapplications.Thisabilitytoofferdifferentiatedservicesnotonlyenhancescustomersatisfactionbutalsocreatesnewrevenuestreamsforoperators.NewEdgeAIinferencingservicesarealsopossibleonthesameAI-RANinfrastructure.

1.3FromCostCentertoProfitCenter

AI-RANisseenasastrongapproachtoenhancingthereturnoninvestmentinnetworkinfrastructurefortelecomoperators.OneofAI-RAN'skeyfeatures,multi-tenancy,notonlyutilizesRANresourcesforhigh-throughputbroadbandcapacity,wirelessqualityimprovement,andnetworkoptimizationbutalsoflexiblyallocatesresourcesforedgecomputinginfrastructuresthatsupportAItrainingandinferencing.Thismulti-purposecapabilityenablesoperatorstoimprovemobilenetworkqualitywhilecreatingnewrevenueopportunities.

ByadoptingAI-RAN,telecomoperatorscanmaximizetheprofitabilityoftheirnetworkinvestmentsandestablishsustainablegrowthmodels.Thistransformationconvertstraditionalnetworkinfrastructurefromacostcenterintoaprofitcenter,enablingoperatorstoachievesustainablegrowththroughnewbusinessmodels.

Figure2.AI-RANredefinestelecombusiness

2.RANEvolution:FromdRAN,vRAN,CloudRAN,OpenRANtoAI-RAN

2.1KeyDevelopmentsinRANEvolution

TheRANlandscapehasundergonearemarkableevolution,transitioningfromtraditionalhardware-centricmodelstomoreadvanced,AIandsoftware-basedarchitectures.ThisevolutioncanbecharacterizedbytheprogressionfromDistributedRAN(dRAN)toVirtualizedRAN(vRAN),CloudRAN(C-

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RAN),OpenRAN,andultimatelyAI-RAN.

2.1.1EvolutionfromdRANtovRAN,C-RAN,andOpenRANtowardAI-RAN

dRANrepresentsthetraditionalRANsetup,whereradiounits,distributedacrosssites,arecloselycoupledwithbasebandunitsforsignalprocessing.Thissetupoftenleadstoincreasedcostsincludingsiteassets,inefficientresourceuse,anddelaysinserviceevolution.

vRANemergedasaresponsetothesechallengesbyvirtualizingbasebandfunctions.WithvRAN,networkfunctionscouldbeseparatedfromdedicatedhardwareanddeployedoncommercialoff-the-shelf(COTS)hardware,enhancingflexibilityandscalability.

C-RANfurtheradvancedthisconceptbycentralizingbasebandprocessinginacloudenvironment.Thecentralizedprocessingreducedhardwarerequirementsatindividualsites,allowingbetterpoolingofresourcesandcentralizedmanagement.Itimprovedefficiencybutrequiredarobustbackhaultomanagelatencychallenges.

OpenRANbuildsuponthevirtualizedandcloud-basedapproachesbyintroducingstandardizationandinteroperability.ItdisaggregatesRANcomponents,allowingoperatorstomixandmatchsolutionsfrommultiplevendors,breakingvendorlock-in,reducingcosts,andencouraginginnovation.Thisopennesssupportsgreaterflexibilityandadaptabilityinnetworkdeployments.

AI-RANintegratesAIcapabilitiesintoRANoperationsoveracommonacceleratedinfrastructureandsorepresentsthegreatestadvance.ByprovidingAIandRAN,AIforRAN,andAIonRAN,operatorscanmovebeyondmereconnectivityandmakenetworksmoreintelligent,self-optimizing,andproactive.

2.1.2DriversofTransformation

Thekeydriversbehindthesetransformationsincludecostoptimization,performanceoptimization,improvingflexibilitywithsoftware,enhancingoperationalefficiencyandcapturingnewmonetizationopportunities.TraditionalRANsolutionsrequiresignificantcapitalexpenditure(CAPEX)forspecializedhardware,whiledRANalsofacedscalabilitylimitationsandhighoperationalcosts.Movingtovirtualizedandcloud-basedsolutionsaddressesthesechallenges,allowingoperatorstominimizecostsandfullyutilizethescalabilitypotentialofthecloudinfrastructure.OpenRANandAI-RANtakethesebenefitsfurtherbyenablingflexibilitythroughopeninterfaces,greateroperationalefficiency,andnewmonetizationmethodsbasedonAIservices.

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2.2AI-NativeNetworks:TheRoleofAIinRANTransformation

AIisplayingatransformativeroleintheevolutionofRANbyprovidingadvancedtoolstooptimizeperformance,automateresourceallocation,andwillultimatelytransformhowmodernnetworksoperate.

ImprovingSpectralEfficiency,OptimizingPerformanceandResourceManagement:AIisfundamentallychanginghowRANresourcesaremanagedbymakingoperationsmoreadaptiveandefficient.IntraditionalRANsetups,resourceallocationandmanagementrequiremanualconfiguration,makingithardtoreacttouserdemands.AI,however,enablesalevelofdynamicadaptabilitythatwaspreviouslyunachievable.Forexample,AI-nativemodelscanautomaticallyallocatebandwidthbasedonreal-timeusagepatterns,manageinterferencemoreeffectively,andensureoptimalloadbalancingacrossthenetwork.Thisimprovesspectralefficiencyandoptimizesperformanceandmakesbetteruseoftheavailableinfrastructure.

FromReactivetoPredictiveModels:OneofthemostsignificantcontributionsofAItoRANisitsabilitytoconvertnetworksthatmerelyreactintothosethatcanpredict.Traditionally,networkmanagementrespondstoissuesonlyaftertheyoccur.AIchangesthisparadigmasitspredictivecapabilitiesallownetworkstoanticipateproblemsandtakepreventiveaction.Machinelearningalgorithmscananalyzevastamountsofnetworkdatatoidentifypatternsandpredictpotentialfaultsorcongestionpointsbeforetheyimpactservicequality.Thisnotonlyimprovesreliabilitybutalsohelpsminimizedowntimeandoperationalcosts.

UnlockingRevenuePotential:AI-nativenetworksaretransformingRANassetsfromtraditionalcostcentersintorevenue-generatingcenters.GenerativeAIintroducesnewuserexperiencesbyprovidingEdgeAIinferencinganddynamicresourceallocation,leadingtobetterservicequalityandhighercustomersatisfaction.GenerativeAIalsoidentifiesopportunitiesformonetizingnetworkcapabilities,suchasofferingpremiumservices,targetedadvertising,andedgecomputingsolutions.ThisshiftnotonlymaximizesthevalueofRANinvestmentsbutalsopositionsnetworksasstrategicassetsdrivingprofitability.

AI-RANisthusattheforefrontofamoreproactiveandefficientnetworkmanagementapproach,transformingRANintoakeyenablerofintelligentandautonomousnetworkservices.

2.3AI-RANDefinitions

AI-RANreferstotheapplicationofartificialintelligencetechnologytotheRAN.Itaimstoimprovemobilenetworkefficiencyandoptimizepowerconsumption,whileenhancingtheutilizationoftheexistinginfrastructure.TheconceptinvolveshostingbothAIapplicationsandvirtualRAN(vRAN)softwareonthe

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sameinfrastructure,allowingtelecomoperatorstogeneraterevenuefrombothnetworkaccessandAIserviceswithasinglecapitalinvestment.

TheAI-RANAlliancehasestablishedthreeitemstoaddressdifferentaspectsofAIintegrationinRAN:

AI-for-RAN:FocusesonusingAItoenhanceRANperformance.ItexploreshowAIcanimproveoperationefficiency,boostcapacity,andachievekeyperformancetargetsintheradioaccessnetwork.

AI-and-RAN:InvestigateshowtousethesameinfrastructuretorunbothRANworkloadsandAIworkloadssimultaneously.ThegoalistoincreaseresourceutilizationandopenupnewrevenuestreamsfortelcosbyhostingvariousAIapplicationsonthesameplatformsthatrunnetworkfunctions.

AI-on-RAN:AddressessolutionsforrunningAIapplicationsontheradioaccessnetwork.ItfocusesonenhancingRANtoensureitcanhandletheincreasingdemandsofAIandgenerativeAIapplicationswithoutcompromisingkeyfactorslikelatencyandsecurity.

Figure3.ThreeitemstoaddressdifferentaspectsofAIintegrationinRAN

TheseitemscollectivelyaimtointegrateAIintothefabricoftheradioaccessnetwork,transformingnetworksintoself-organizing,self-optimizing,andself-managingsystemsthatcanhandlereal-timechanges,anticipatemaintenanceneeds,andmoreefficientlymanageresources.

3.HistoryofSoftBank'sAI-RANR&D

3.1EarlyResearchandAI-RANDevelopment

SoftBankcontinuestoexplorenewwaystocreatevaluebyintegratingtraditionaltelecominfrastructure

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withAIamidsttherapidinnovationinAItechnology.Recognizingthatthefullperformanceof5Gremainsunrealizedsinceitsintroduction,SoftBankbeganeffortstoenhance5GthroughAIandMachineLearning(ML).

SoftBankisleadingthedevelopmentofAI-RAN,anewarchitecturethatintegratesAIapplicationsandsoftware-basedRANintoasinglecomputer.AI-RANenhancesthecapabilitiesandqualityofRANwhilealsoprovidingasharedcomputingplatformforAIapplicationsacrossvariousindustries.SoftBankaimstodeployAI-RANequipmentinAIdatacentersdistributedthroughoutJapan,directlyconnectingSoftBankbasestationstotheseAIdatacenterstooffersecureandlow-latencyAIservices.

3.2ApplicationsofSoftBankAI-RANResearch

Inthepast,theprimarystrategyforachievinghigh-speed,high-capacitywirelesscommunicationwastoincreasethefrequencybandsused,asevidencedbythetransitionfrom3GtoLTEand5G.However,theemergenceofAI-RAN,whichcanenhanceuserexperiencewithoututilizingmorefrequencybands,holdsgreatpotentialforeffectivelyutilizingthefinitepublicresourceoftheradiospectrum.Withthetransitionfrom5Gto6Gnetworksapproaching,theimportanceofAI-RANisexpectedtostrengthenevenmore.

TheAI-RANdatacenterbeingdevelopedbySoftBankwillallowboth"RANoperations"and"AIapplications"torunsimultaneouslyonthesameserver.Thisadvancementenablestelecomoperatorstosecuretworevenuestreams—RANandAI—withasinglecapitalinvestment.Moreover,byintegratingdifferentservices,operatorscanimprovetheoperationalefficiencyoftheirinfrastructure.Consequently,AI-RANholdsthepotentialtosignificantlyimprovethereturnoncapitalinvestmentfortelecomoperators.

CaseStudy:ApplicationofAIforchannelinterpolationinlowerlayersofwirelesscommunication

Indenseenvironmentswithmultiplebasestationsandterminals,radiosignalsareoftendistortedbymultipathfading.Asaresult,conventionalsignalprocessingtechnologymayfailtoaccuratelyestimatewirelesscharacteristics,leadingtolowerthroughput.

Toaddressthis,weappliedAI-nativesuper-resolutiontechnology,originallyusedinimageanalysis,toradiosignalprocessing.SimulationswereconductedtoevaluatethepotentialuplinkthroughputimprovementsbyreconstructingdegradedsignalsusingAI.AftertrainingtheAImodelwithsimulatedradiosignaldatabasedonreal-worldenvironmentalconditionsandtestingitwithuplinksignals,a30%improvementinuplinkthroughputcomparedtoconventionalsignalprocessingtechnologywasobserved(Figure4).

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Figure4.Comparisonresultofuplinksignals:30%throughputgain

3.3PartnershipsandCollaboration

Figure5.AI-RANAlliancelaunchceremonyatGSMAMobileWorldCongressBarcelona2024

SoftBankisacceleratingthedevelopmentofAI-RANthroughitspartnershipwithNVIDIAandotherindustryleaders,havingbegunthedevelopmentofAI-RANsolutionsonnewhardwaresuchastheNVIDIAGraceHopper200Superchip(GH200),whichiscurrentlyevolvingintotheNVIDIAGraceBlackwellplatform.

TopromotethewidespreadadoptionanddevelopmentofAI-RANtechnology,SoftBankhaspartneredwithindustryleadersincludingNVIDIA,Arm,T-Mobile,Ericsson,Nokia,andSamsungtoestablishtheAI-

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RANAlliance

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.SinceitslaunchatGSMAMobileWorldCongressBarcelona2024,thealliancehasgrownto58members(asofDecember2024),encompassingadiversemixoftelecomoperators,semiconductorcompanies,andacademicinstitutionsunitedbythemissionofadvancingRANperformanceandcapabilitiesthroughAIinnovation.

SoftBankbelievesthatAI-RANhasthepotentialtobecomethetechnologythatwillsignificantlyimpactnotonlythetelecomindustrybutalsosocietyasawhole.Withtheimminenttransitionfrom5Gto6Gnetworks,theimportanceofAI-RANisundeniable.SoftBankwillcontinuetofocusoncontinuingAI-RANdevelopmenttoinitiateanewparadigmshiftforthe6Gera.

SoftBank'sAI-RANR&Deffortshavethepotentialtorevolutionizetelecomnetworksandcreatenewbusinessopportunitiesacrossvariousindustries.

4.gRAN:GPU-basedAI-RANArchitecture

gRAN,atermintroducedbySoftBank,standsforGPU-basedRANthatoffersanarchitecturefordeployingAI-RAN,consideredthedesirableevolutionarystageofRANfollowingvRAN,cRAN,andO-RAN.TheintroductionofgRANmarksasignificanttechnologicalleapintheevolutionoftheradioaccessnetwork.ByleveragingthepowerofGPUsinadditiontoCPUs,gRANenhancestheefficiency,scalability,andflexibilityofRANinfrastructures;itsupportsadvancedAI-nativefunctionsandmeetstheever-growingdemandsofAIapplicationsandmoderntelecomnetworks.

4.1KeyCharacteristicsofgRAN

ThetransitionfromtraditionalRANarchitecturestosoftware-drivenapproacheswithhigherperformancehaspavedthewayforgRAN.AsRANbecomesvirtualizedandopen,andmostimportantlysoftware-defined,itlaysthefoundationforportingRANoverGPU-basedacceleratedinfrastructure,andbringinganewlevelofcomputationalpowerandefficiencywithgRAN.

4.1.1WhyGPUsforvRANEvolution?

GPUsarewell-suitedforhandlingthehighlyparallelprocessingworkloadscommoninmodernRANenvironments.UnlikeCPUs,whichareoptimizedforserialprocessing,GPUsexcelatexecutingintensivematrixcalculationsandmultipletaskssimultaneously,makingthemidealforreal-timeRANdataandsignalprocessing.Thisparallelismisparticularlyimportantinhandlingthecomplexalgorithmsrequiredfor5Gandfuture6Gtechnologies,suchasmassiveMIMO,beamforming,and

2FormoredetailsabouttheAI-RANAlliance’smissionandinitiatives,refertotheirwhitepaperat

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energyreduction.UtilizingGPUsinadditiontoCPUsallowstelecomoperatorstohandletheseworkloadsmoreefficiently,reducelatency,andimproveoverallnetworkperformanceandefficiency.

4.1.2TheTechnologicalLeapwithGPU-basedvRAN(gRAN)

GPU-basedRANenablesmodernAIserviceslikeLLMinferencingbyprovidingthecomputationalpowerneededforreal-timedataprocessinganddecision-makingattheedge.ThisallowsRANtohandlecomplexAIworkloadsefficiently,reducinglatencyandenhancingresponsiveness.Additionally,GPUsfacilitatedynamicresourceoptimization,suchasadvancedSelf-OrganizingNetwork(SON),byenablingrapidanalysisandadaptationofnetworkresourcestochangingdemands,ensuringoptimalperformanceandreliabilityinRANenvironments.TheseattributesallowgRANyieldthemoredynamicandresponsivenetworkscrucialforsupportingemergingusecasessuchasGenerativeAI/LLMinferencing,augmentedreality(AR),virtualreality(VR),andotherdata-intensiveapplications.

4.2TheArchitectureofgRAN-basedAI-RAN

ThearchitectureofgRANconsistsofseveralkeycomponentsthatworktogethertocreateahighlyprogrammable,intelligent,andhighperformingnetworkenvironment.ThecoreelementsofgRANaretheRadioUnit(RU),DistributedUnit(DU),CentralizedUnit(CU),theintegrationofAIcapabilities,andamulti-tenantanddynamicorchestrator.

RadioUnit(RU):TheRUhandlestheradiofrequency(RF)signals,convertingthembetweenanaloganddigitalformats.Itisresponsibleforcommunicatingwithuserdevicesandservesasthemobileuser’sentrypointtotheRAN.

DistributedUnit(DU):TheDUisresponsibleforlower-layerprocessing,includingreal-timetaskslikescheduling,beamforming,and