2024 全球6G技术大会 -10.0C 6G Wireless Intelligent Cloud Network

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TableofContents

1.Abstract 2

2.O-RANArchitecture 2

3.6GVisionanddesigntarget 3

4.Keytechnicalpillarsandconsiderations 4

4.1Networkarchitectureconsiderations 4

4.2ServicebasedRAN 6

4.3AI 8

4.3.1Cross-domainAIcollaboration 10

4.3.2LargeModel 11

4.4Spectrumsharing 12

4.5SustainabilityandEnergysaving 14

5.Forward-Looking 16

Reference 16

Abbreviation 17

Authors 18

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1.Abstract

O-RANALLIANCEhasbeenfoundedin2018byAT&T,ChinaMobile,DeutscheTelekom,NTTDOCOMOandOrange.Sincethen,O-RANALLIANCEhasbecomeaworld-widecommunityofmobilenetworkoperators,vendors,andresearch&academicinstitutionsoperatingintheRadioAccessNetwork(RAN)industry.Themissionistore-shapetheRANindustrytowardsmoreintelligent,open,virtualizedandfullyinteroperablemobilenetworks.O-RANleveragesmostofthephysicalfeaturesdefinedin3GPP,whichmaintainsaunifiedandhealthyecosystem.O-RANspecificationssplitsthenetworkentitiesanddefinestheinterfacestofacilitatethemulti-vendorjointlydevelopandinteroperatetesttheproducts.

ITUdefinedIMT-2030Frameworkandrelatedtimeline,andtheindustriesinitializedthe6Gstudyaccording.O-RANalsokickedoffthe6GstudyinnGRG,whichistoformulatethe6Grelatedviewsbeforestandard.Beyondtheadvancedfeatures,O-RAN’sflexiblearchitecturecouldprovidesomeuniqueadvantagesforfuture6Gnetworks,whichincludesprogramablearchitecturefornetworkintelligence,service-basedRANdesign,sufficientnetworkpoweroptimization,flexiblespectrumsharingandetc.

Thiswhitepaperbrieflyintroducessomekey6GtechnicalpillarsbasedonO-RANnGRGdiscussion.Tofacilitatethereadertounderstandthetechnicalissuesandconsiderations,theO-RANarchitectureisintroducedinsection2.WealsoprovideforwardlookingforO-RANin6Geraattheendofthewhitepaper.

2.O-RANArchitecture

BelowistheO-RANarchitectureoverviewdefinedbyO-RANalliance[1].O-RANleveragesthe3GPPdefinedinterfaceandalsodefinessomenewinterfacesasitsplitstheRANfunctionsintoO-CU,O-DU,andO-RU.

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Figure1O-RANArchitectureoverview[O-RAN]

Withinthearchitecture,RANIntelligentController(RIC)isthelogicalfunctionstoenablethecontrolsinanearrealtimeornon-realtimemanner.Thenetworkcontrolfunctionsaresplittedintothetwoentitiesbasedonrequiredtimescale.E2istheinterfaceconnectingtheNRRICandO-CU,andmostofthenetworkintelligentfunctionsareconnectedviaE2.O-DUandO-RUaretworemarkableentitiestorepresentthenetworkopenness.ThereareseveralsplitoptionsbasedonthesupportedfunctionsonO-RU.OperatorscouldprogramtheRICfunctionswithdifferentAPPs,andmultipleAPPscouldflexiblyenablethedifferentnetworkfunctions.

3.6GVisionanddesigntarget

ITU-RdefinestheIMT-2030Framework[2],whichincludestheusagescenariosandcapabilitiesof6G.Thisframeworkrecommendationisoneofthemostimportantguidancefor6Gandwouldbereferredasdesignguidancefor3GPPandotherSDOtospecifythe6Gstandard.

UsagescenariosofIMT-2030areenvisagedtoexpandonthoseofIMT-2020(i.e.eMBB,URLLC,andmMTCintroducedinRecommendation

ITU-RM.2083

)intobroaderuserequiringevolvedandnewcapabilities.InadditiontoexpandedIMT-2020usagescenarios,IMT-2030isenvisagedtoenablenewusagescenariosarisingfromcapabilities,suchasartificialintelligenceandsensing,whichpreviousgenerationsofIMTwerenotdesignedtosupport.

TheusagescenariosofIMT-2030includeImmersiveCommunication,HyperReliableandLow-LatencyCommunication,MassiveCommunication,UbiquitousConnectivity,ArtificialIntelligenceandCommunication,andIntegratedSensingandCommunication.

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Figure2IMT-2030UsageScenarios[2]

O-RAN’snetworkarchitectureprovidesmostflexibilitybysplittingRANfunctionsanddefiningstandardinterface.Asthe6Gusagescenariosaredoubledcomparedto5G,theO-RAN’sflexibilitywouldbeagoodfoundationforfurtherinnovation.Insection3ofthepaper,wediscussedseveralhighlightedtechnicalpillarsfor6Gdesignandanalyzethechallengesandpotentialsolutions.

4.Keytechnicalpillarsandconsiderations

4.1Networkarchitectureconsiderations

6Gnetworkbridgesthephysicalanddigitalworlds.Anincreasingnumberoftrafficwilloccurontheedgeofthe6Gnetwork.Thepotentialfeaturesoffuture6Gnetworkareintelligence,programmabilityandresourcepooling.

Intelligenceisthekeyenablertechnologyfor6Garchitecture,andnativeAIhasarousedmoreattentionfromacademiaandindustry.InordertoachievethenativeAI,therelatedinterface(e.g.,E2)andprocedure(e.g.,AI/MLflow)shouldbeconsideredin6Garchitecture.Thesub-section3.3describesthenativeAIindetail.

Inthecontextof6G,theintegrationofnativeAIneedsanefficientandconvenientapproachtoincorporateAIelementsseamlessly.Inaddition,thedifference6Gservicerequiresdifferentnetworkresources.Therefore,programmabilityemergesasapromisingsolutiontodrivethedevelopmentof6Garchitecture.

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Programmabilityencompassesthreekeycomponents:Parameter,Data,andAlgorithm.Programmableparameterfacilitatestheseamlessadaptationofparametersof6Gnetworkthroughaprogrammableframeworkandgeneralinterface.ProgrammabledatainvolvestheconstructionofdatasetsforAIalgorithmtrainingandtheexplorationofdatarelationshipswithinthenetworkfunctions.Additionally,datacanbesecurelyprovidedtothirdpartiesthroughrelevantsecuredmethods.Theprogrammablealgorithmsdefinetheinputandoutputdataformatwithdifferentscenarios.ThenetworkfunctionsandthirdpartiescanembedorreplacetheAIalgorithmsviaaprogrammableframeworkunderthesubjectsoftheaforementionedinputandoutputandasecuritycheck.Tofacilitatethisprocess,aprogrammableframeworkisrequiredtodeployandmanagethesealgorithmseffectively.Theframeworkshouldencompassacomprehensivesetofprogrammableinterfacesandfunctionmodules,enablingseamlessintegrationandoperation.Additionally,theprogrammablealgorithmensuresthatthe6Gnetworkdynamicallyadaptstovariousscenarios'requirements.Forinstance,ifconsumersseekhighthroughputfromtheRAN,theAIalgorithmincorporatestheRANslice.Similarly,forconsumersprioritizingQualityofService(QoS),theAIalgorithmintegratestheQoSoptimization.

ToenabletheimplementationofprogrammableRAN,itisessentialtoprogressivelyopenthetraditionallyclosedprotocolstackwithintheRAN.Thisinvolvesenhancingthefunctionalityattheprotocolstacklevel,andstandardizingandgeneralizingthenewlyopenedinterfaces.InthecontextoftheongoingevolutionofnativeAI,programmableRANcatalyzesadvancementtowardamoreopenandintelligentRANvision.Byembracingprogrammability,theRANcaneffectivelyadapttodynamicnetworkrequirements,fosterinnovation,andleveragethefullpotentialofnativeAI.

Resourcepoolingplaysanimportantrolein6Garchitecture.Theresourceisstillheterogeneous,itconsistsofcommonanddedicatedresources.Generalresourcesarecommon,standardizedhardware(i.e.,industrialserversbasedonX86orARMCPUs),anddiversifiedhardwarechipswithscalability,includingaccelerationandclockresourcechips,andgraphicsprocessingunit(GPU)forAImodeltraining.ForRAN,high-speedprocessingandalargenumberofdedicatedresourcesarerequired,suchasFieldProgrammableGateArray(FPGA)forcodingandencoding.TheclockresourcesareappliedtofulfillsynchronizationaccuracyamongnetworkelementsandUEs.Dedicatedresources(e.g.,ASICchips)providespecializedservicesforasmallnumberoffacilitieswithlargecapacityandultra-high-performancerequirements.

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Figure3ProgrammableRAN

4.2ServicebasedRAN

Historically,RANarchitecturewasmainlydesignedtoguaranteetheconnectionservicefortraditionalToCbusiness,usingarelativelyclosedprotocolbutwiththeperformanceadvantagesofspecialization.Asmorescenariosandservicesintroducedin5Gand6G,andITtechnologiesisintegratedinmobilenetwork,RANarchitectureneedtoevolvetoprovidemoreflexible,adaptablenetwork.O-RANisdefiningamoreopenarchitecture,buildingaunifiedcloudplatformforRAN,standardizingmoreopeninterfaces,andintroducinganintelligentfunction.TheimplementationofcurrentcloudRANonlychangestherunningplatformforsoftwareinsteadofchangingsoftwarearchitectureoftheRAN.ThisRANarchitectureisnotcloud-friendlyandcannotmakefulluseoftheadvantagesofcloud-native.

Cloud-RANisthefirststep,RANsystemcanbefurtherevolved.SBAin5GCoreNetworkcanbeusedasreference.ThegoalofService-basedRANisachievingafullycloud-nativearchitecturebyrebuildingRANfunctionsintocombinableandreusablenetworkservicesandusingunifiedinterfacewithRANinternalservicesandCN.

Theadvantagesoftheservice-basedRANinclude:

1)Flexibleandelasticdeploymentofnetworkfunctions,rapidupgradingandexpansionofnetworkcapabilities,enablingmorebusinessscenarios;

2)Bringnewend-to-endnetworkinteractionwayswithoutreducingtheimpactof

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cross-domainnewfunctionsintroductiononexistingservices;

3)Moretimelyandmulti-dimensionalopeningofwirelessnetworkcapabilities;

4)IntegratedmanagementandorchestrationwithCNservices,reducingthecomplexityofnetworkoperationandmaintenance,improvingthenetworkofadaptabilitytonewbusinesses.

Figure4Theconceptofservice-basedRAN

Thedesignoftheservice-basedRANarchitectureneedstoconsiderthefollowingaspects:

ServiceGranularity

ThegranularityforRANserviceswhichisrebuiltformtheoriginallyRANfunctionsiscrucial.Thesmallerthegranularity,themoreflexibleitis,butitmaybringperformanceandefficiencyissues.Theimplementationofthe5Gcorenetworksservice-basedarchitectureincludestwolevels:NFandNFServices.NFscommunicatewitheachother,andinternalNFservicescansharedatabaseswhichreducescomplexityandisalsodifferentfromMicroservicesarchitecture.Consideringtheinternalfunctionalcorrelationsandcomplexity,RANcaninitiallyberebuiltinasimilarwaytotheCoreNetwork.

Service-basedRANfunctions

5GRANcanbefunctionallydividedintocontrolplaneanduserplane,andthereisalsotheconceptofseparationofcontrolplaneanduserplane,butinthedeploymentlayer,itstilladoptsasinglemode.Thecontrolplanemainlyincludesfunctionssuchasconnectionmanagement,sessionmanagement,mobility,andmeasurement,andtheuserplaneincludestheprocessingofdatapackets.Therearedifferentconsiderationsforondifferentfunctionalplanes.

Forthecontrolplane,theservitizationcanrebuildtheexistingcontrolplanefunctionsintofinergrainedservicesaccordingtothedegreeofcoupling,anddifferentservicescanbecombinedandflexiblydeployedindifferentscenariosandregionsondemand.Forexample,inthescenariooftheInternetofvehicles,themobilitymanagementserviceissuitableforcentralizeddeploymenttooptimizethemobilityexperience.Atthesametime,theservice-basedfunctionsofthecontrolplanecanrealizedirectaccesstotheCoreNetworkcontrolplane,reduceunnecessarysignalingforwarding,andtheinteractionwithothercorenetworkservicescanbechangedfromserialinteractiontoparallelinteraction,optimizingthesignalingprocessofthecontrolplane.The

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optimizationofsignalingprocesseshelpsimprovenetworkperformance,suchasdelayandefficiency.Besides,forextremerequirementsofspecificservices,italsohelpsRANandCNintegratesattheedge,simplifyingdeploymentcomplexityandimprovingperformance.Finally,forthemorecomplexfunctionalconfigurationandparameterconfigurationofthefuturenetwork,theservice-basedcontrolplanecanbeexecutedandupdatedatasmallergranularitywithoutaffectingtheoperationofotherservices.

Fortheuserplane,thetraditionalmobilecommunicationprotocolsallfollowtheOSIhierarchicalprotocoldesignconcept.Eachlayerreceivesspecificservicesprovidedbythelowerlayerandisresponsiblefortheupperlayer.Theupperandlowerlayersinteractwitheachotheraccordingtotheinterfaceagreement,andthesamelayerinteractswitheachotheraccordingtotheprotocolagreement.Theproblemofthislayereddesignconceptisthattheprotocolandservicemodelarefixed,andflexiblecross-layersignalinginteractionandcross-layerfunctioncombinationcannotberealized.Thediversifiedcharacteristicsoffutureapplicationswillbringmoredifferencesindatapacketprocessing,suchassmalldatapacketsforindustrialcontrol,whichrequirehigherreliabilityandneedtoutilizethePDCPreplicationfunctioninuserplane;ImmersiveinteractiveapplicationshavedifferentprocessingrequirementsforI-frame,P-frame.TheuserplaneneedsfunctioncombinationandarrangementforthenewQoSguarantee.Inadditiontothecurrenttypesofexistingapplications,sensing,AIandothernewapplicationshavealsobroughtnewdatapacketmodels,requiringtheuserplanetobeabletomatchtheprocessingofdifferentdatapackets,aswellasforwarding.Theservice-baseduserplanehasadvantagesinflexiblecombination,deployment,andrapidupdate.Forscenarioswithdifferentuserdatapacketprocessingrequirements,theservice-baseduserplanecanbepreferred.

Besides,thenewservicessuchasAI,computing,sensingwillbeprovidedbythefuturewirelesssystem,ontheonehand,thiscanenabletheenhancementoftheexistingfunctionalplane,suchasintroducingcontrolfunctionsforsensingandcomputingpowerandintroducingnewuserpacketprocessingmodeinuserplane.Ontheotherhand,RANmayalsointroducenewfunctionalplanes,suchasdataplane,bringingnewfunctionalinteractiveways,thatwillraisemoredemandsonnetworkflexibilityandrapidupdate.Service-basedarchitecturehascertainadvantagesintheseaspects.

Service-basedinterface

Atpresent,theRANandtheCoreNetworkinteractthroughthepoint-to-pointN2interface.Forservice-basedRAN,aservice-basedN2interfacecanbeconsidered,andtheRANisstillanindependentwhole,RANservicescaninteractwitheachotherthroughaninternalefficientinterface.Thisapproachisrelativelyeasytoimplementandcanbeadoptedduringtheinitialphaseofserviceorientation.TheotherwayistouseaconsistentinterfacebetweenRANinternalservicesandthecorenetwork,RANservicesandcorenetworkservicesareinapeerpositionandcanachievedirectinteraction,thisapproachhasmoreadvantages,butatthesametimewillbringmoreissuesrelatedtonetworksecurity,ecologicalchange.

4.3AI

ArtificialIntelligence(AI)hasbeenproposedasoneofthemostpowerfultechnologiesthat

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improvessystemperformanceandenablesnewfeaturesinthewirelesscommunicationnetwork,byanalyzingthedatacollectedandautonomouslyprocessedthatcanyieldfurtherinsights.

3GPPintroducedanewlogicalfunctionentity,namedNWDAF,tothe5GCtoprovidemultipletypesofnetworkdataanalyticservices.Thenetworkdataanalyticservicesinclude:

ObservedServiceExperiencerelatednetworkdataanalytics,toprovideaverageofobservedServiceMoSand/orvarianceofobservedServiceMoSindicatingserviceMOSdistributionforservicessuchasaudio-visualstreamingaswellasservicesthatare

notaudio-visualstreamingsuchasV2XandWebBrowsingservices;

NFLoadAnalytics,toprovidetheaverageloadoftheNFinstance;

NetworkPerformanceAnalytics,toprovidethebasestationstatusinformation,resource

usage,communicationperformanceandmobilityperformanceinanareaofinterest;

UErelatedanalytics,toprovidetheUEmobilityanalytics,UEcommunicationanalytics,expectedUEbehavioralparametersrelatednetworkdataanalyticsandabnormalbehaviorrelatednetworkdataanalytics;

UserDataCongestionAnalytics,toprovidecongestionexperiencedwhiletransferringuserdataoverthecontrolplaneoruserplaneorboth;

QoSSustainabilityAnalytics,toprovidetheQoSchangestatisticsorlikelihoodofaQoSchangeforananalyticstargetperiodinacertainarea.

InRAN,3GPPalsoconductedseveralstudiesontheAI-enablednetwork.InRelease17,3GPPconductedastudyonAI-enabledRANintelligence,whichdefinedareferencefunctionalframeworkandidentifiedasetofhigh-levelprinciplestoguidethestandardswork.ThestudyonAI-enabledRANintelligencefocusedonthreeusecases:

NetworkEnergySaving,tooptimizetheenergysavingdecisions(e.g.,cellactivation/deactivation)bypredictingtheenergyefficiencyandloadstateofthenextperiod;

LoadBalancing,toprovidehigherqualityuserexperienceandtoimprovesystemcapacitybybasedoncollectionofvariousmeasurementsandfeedbacksfromUEsandnetworknodes;

MobilityOptimization,toreducetheprobabilityofunintendedeventsassociatedwithmobility,topredictUElocation,mobilityandperformance,andtosteertraffictoachieveefficientresourcehandling.

InRelease18,3GPPconductedastudyonAIforNRairinterface,toexplorethe3GPPframeworkforAIforair-interfacecorrespondingtoeachtargetusecaseregardingaspectssuchasperformance,complexity,andpotentialspecificationimpact.ThestudyonAIforNRairinterfacealsoadoptedausecasecentricapproach,focusingonthreeselectiveusecases,namelyCSIfeedbackenhancement,beammanagementandpositioningaccuracyenhancement.InRelease19,3GPPconductedastudyonAIformobility,toimprovehandoverand/orRRMperformancebypredictingcelllevelmeasurement,handoverfailure/radiolinkfailure,andmeasurementevents.

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IntheO-RANarchitecture,theintroductionoftheRIChasbeenanimportantdevelopment,makingitpossibletointroduceAIbasedsolutionstoawidelyusecases.EnablingAIdrivennetworkingrequiresaparadigmshiftinthearchitecturalblueprint.Inthe6G,therearethreeimportantfeaturesofAIneedtobeconsidered,namely,nativeAI,crossdomainAI,andnetworklargemodel.

NativeAIreferstoembeddingAIintofunctionalitiessupportedbyvariousnodes/endpointsandinterfaceswithinanetworkarchitecture[8].ConsideringthefourkeycomponentsofAI,i.e.,computingpower,data,AIalgorithmsandfunctionalities,anativeAInetworkshouldbewithahybridcentralized/distributedAIarchitecture.ThecentralizedAIentitiesrunfororchestration,managing,deployingandcontrollingallthedistributedAIentities,e.g.,ontheSMOplatformthatinteractswithotherdomain-specificAIentities.ThedistributedAIentitiesrunforservingfunctionsofthelocalnetworkandreceivingcommandsfromthecentralizedAIentities,e.g.,ontheCN,TN,BSandUErespectively.

The6GwirelessnetworkwillnativelyintegratecommunicationcapabilitieswithAI.Ontheonehand,end-to-endAImayleveragemassiveamountsofdataproducedbyairinterfacesandnetworkstooptimize6Gnetworksandofferconsumerscustomizednetworkservices.Ontheotherhand,asthecomputingpowerofinfrastructureandterminaldevicesenhances,futurenetworkswillbeabletoofferadistributeddeploymentenvironmentforAI,deliveringmoreflexibleandreal-timeAIservicesatthenetworkedgeforusers.Firstly,itisessentialthatsupportforAIbetakenintoconsiderationfromthebeginningwhendesigningnetworkarchitecture.Thisconsiderationmustensuretheseamlessintegrationoftraditionalcommunicationinteractions,whilethemetricsfortrainingandinferenceofAIareconvergedintothecontrolanddataflow.CollaborationwithinAIisalsoacriticalfactortotakeintoaccount.ThisincludescooperationbetweencentralizedanddistributedAIdeployment,cooperationbetweenlargenetworkmodelsandotherspecializedmodels,andthecross-domainAIcollaborationamongRAN,CN,andmanagementsystems.Hence,itisimperativetodesignefficientAIcollaborationmechanismsfromtheperspectivesofAIorchestrationandmanagement,datainteraction,distributedlearningalgorithms,andcomputingpowerscheduling.Lastbutnotleast,theproblemofAIsecurityhascontinuouslypresentedamajorobstacletotheuseofAItechnologies,requiringprotectionsintrustworthyAI,datasecurity,andprivacytoguaranteethedependabilityandsecurityof6GAIapplications.

4.3.1Cross-domainAIcollaboration

CrossdomainAIreferstocollaborationandintegrationofAI-enabledfunctionalitiesacrossdifferentdomains,wherethedomainscanmaptonetworksdomains(e.g.,RAN,CN,TN,networkapplications,networkdigitaltwins)orotherdomains[8].InordertoenablecoordinatedAIcapabilitiesacrossdifferentnetworkdomains,thecentralizedAIentities(e.g.,ontheSMOplatform)shouldhandletheend-to-endAImanagementandorchestrationcapability,suchascross-domaindataarrangementandmapping,AItaskidentificationanddecomposition,mappingAItaskswithcomputingresources.

Figure3providesapotentialarchitecturefornativeandcross-domainAI,wherethecentralizedAIentityislocatedinthemanagementdomain.ForE2Eintelligentscenario,across-domainAImanagementfunctionshouldbeaddedintheSMOasacentralizedAIentityto

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coordinatetheAIcapabilitiesfromotherdomains.ThismoduleisrequiredtohandleAIserviceorchestration,networkcomputingresourcemanagement,modelstorageandmanagement,cross-domainAIlifecyclemanagement,andotherrelatedfunctions.

Figure5Nativeandcross-domainAInetworkarchitecture.

Forthenativeandcross-domainAInetworkarchitecture,thecollaborationcontrolbetweendifferentnetworkdomainsisanewchallenge[9].Firstly,thecollaborationcontrolfunctionlocatedinthecentralizedAIentitywilldecomposeintentsintoservicerequirementsonevolvednetworkdomains,wheretheservicerequirementswillaffectconnectionrequirements,AIalgorithmrequirements,datarequirementsandcomputingrequirements.Basedontheservicerequirements,thedistributedAIentitylocatedinthenetworkdomainwilldecomposetheservicerequirementsintothenetworkfunctionrequirements,connectionrequirementsandresourcerequirements.Secondly,toprovideamorereal-timemanagementcapabilities,theserviceswithhighreal-timerequirementsandlowcomplexitywillbeprocessedbythedistributedAIentities,whiletheserviceswithlowreal-timerequirements,largeareasandhighcomplexitywillbeprocessedbythecentralizedAIentity.Therefore,thecollaborativecontrolmethodsshouldbeconsidered,e.g.,federatedlearning,splitlearningandtransferlearning.

4.3.2LargeModel

AsabreakthroughdevelopmentofAItechniques,NetworkLargeModel(NetLM)haveattractedattentionfromboththescientificcommunityandindustryalike.ComparedwithtraditionalAImodelsthatoptimizenetworksunderpredefinedoperations,theNetLMleveragesgenerativeAIalgorithms,e.g.,generativeadversarialnetworkandtransformer,toautomaticallyandcreativelygeneratecustomizednetworksolutions.Forexample,forthejointcommunicationandsensing,theNetLMcouldhelpingeneratingrelevantrays(e.g.,generatingtherightdistributionofAzimuthandElevationanglesoftheradiofrequencybeamtransmittedoutofthenode)tocaptureandsensethesurrounding.AnotherexampleistheNetLMfordigitaltwin,where

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theNetLMisabletolearnthetailbehaviorofthetraindatasetdistributionwithonlyafewsamples,andgeneratethenewbehaviorbasedonwhatwaslearnedthatisconsistentwithreality.

SinceNetLMusuallyconsistsofbillionsofparameters,itisdifficulttodeploytheNetLMontheedgedirectlyduetothelimitedcomputing,communicationandstorageresources.Inaddition,thedeploymentoftheNetLMinthecorenetworkwillalsocausetremendoustransmissionlatencyduetothehugeanddistributeddatatobecollectedandtrainedinthecloud.Therefore,thedeploymentoftheNetLMwillemphasizethecollaborationbetweenNetLMwithvariousscales,includingthecollaborativetrainingandinference.Astandardizedcollaborationmechanismneedstobedefined,suchasnetworkarchitecturewithnewnetworkelement,large-scaledatadistributedstorageandreal-timeprovisionmechanism,andmodel-basedcollaborativeinterface.

4.4Spectrumsharing

From4Gera,mobileoperatorsjointlydeploytheRANnetworkstoextendthecoverage.ThiscouldlargelyreducetheCAPEXandbecomesthemajortrendswhen5Gcomes.InChinaandotherregions/countries,operatorssharethefrequenciesandcooperateontheRANnetworkconstruction.CMCC&CBNjointlydeploythe5GNRnetworkonbandn28,CT&CUjointlydeploythe5GNRnetworkonbandn79andotherfrequencies.

3GPPspecifiedtheRANsharingmechanism,whichisknownasMOCN,standingforMulti-OperatorCoreNetwork.InaMOCNset-up,oneradioaccessnetworkprovidesaccesstothenetworkofmultipleoperators.Eachoperatorrunsherowncorenetwork,buttheradioaccessnetwork,includingcarriersignals,isthesameforallpartnersinacertainregion.OnedrawbackisonlytheoperatorwhoownstheRANnetworkcouldoptimizethescheduler&configurationbasedonservicecharacters,andotheroperatorswouldnothavesuchflexibility.Iftheotheroperators’newusecases(e.g.XR)aredifferentthantheoriginaloptimization,theyhave