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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
1
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
:/solutions-and-impact/connectivity-for-good/mobile-economy/wp-
content/uploads/2024/02/260224-The-Mobile-Economy-2024.pdf
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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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AI-RAN:TelecomInfrastructurefortheAgeofAI
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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AI-RAN:TelecomInfrastructurefortheAgeofAI
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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AI-RAN:TelecomInfrastructurefortheAgeofAI
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
2
.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
/wp-content/uploads/2024/12/AI-
RAN_Alliance_Whitepaper.pdf
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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
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