中國移動(dòng)研究院陳天驕在全球6G大會(huì)上的演講：6G原生AI無線網(wǎng)絡(luò)與AI大模型 6G Native AI Wireless Network and AI Large Model

6GNativeAlwirelessNetworkand

AlLargemodel

Tianjiaochen

2023-12

1

2

Outline

Drivingforcesof6GnativeAI

6GnativeAIandkeyfeatures

36GandAILargeModel

2

BusinessneedsAI

CustomersneedAI

DemandforUbiquitousIntelligence

AIhasbecomethecoredrivingforceforanewroundofindustrialtransformation.Theautomation,digitalizationandintelligenceoftheindustryrequireubiquitousintelligence.

NetworkautonomyneedsAI

Operation&maintenance

MedicalRecognition

Securitymonitor

Machinetranslation

Voiceprintrecognition

Emergencycommunication

SmartNavigation

SmartManufacturing

Robotrescue

Customizednetwork

Smartcoverage

PersonalizedRecommend.

Theintegrationof6GandAIincludestwoaspects:"AIforNetwork"

3

and“NetworkforAI"

Performance

AIenhancesnetworkperformance

network

cost

networkoperationandmaintenanceefficiency

TheDrivingForceofAIforNetwork

Mobilecommunicationtechnologyfacesbottlenecks,requiringurgenttechnologicalinnovationand

interdisciplinaryintegration.AIisakeysolutionforenhancingnetworkperformance.

Traditionalcommunicationsystemsfaceperformancebottlenecks

Optimization

Conflictsbetweennetworkoperationefficiency,complexity,andcost

contradictory

triangle

complexity

6Gposesmorechallengingdemandmetrics

9cpbiitisnhancedbyM203

8abiitie8adddbyM

Currenttechnologyfallsshortofmeeting6G'sneeds

Difficultyinestimatinglargerscale

MIMOchannels

Densebasestationdeploymentleads

toincreasedinterference

Morecomplexsystemdesignsleadto

increasedenergyconsumption

Complexroutinginheterogeneous

equipmentnetworks

Diversecommunicationscenario

requirementsarefragmented

airinterface

Moreaccuratechannelinformation

Moreprecisepositioning

Enhancedinterferencecancellationcapabilities

Enhancedenergyefficiency,spectralcapacity

network

Improvedsceneadaptationspeed

Morebalancedtrafficscheduling

Fasternetworkinginterferenceavoidance

Morerefinedbusinessidentification

Moreaccuratefaultlocation

4

5

6GnetworkinherentlyprovidesAIservices

5G

communicationservice

communicationability

TheDrivingForceofNetworkforAI

ITUextends6Gscenariostoubiquitousintelligence.AIneedstobetransformedintonewcapabilitiesandservicesfor6GcommunicationnetworkstoachieveAIaaS

ITUextends6Gscenariostoubiquitousintelligence

GetAIanytime,anywhere

LowlatencyAIinference/training

SupportmobileAI

6G

communicationability

computingability

perceptionabilitydataabilityAImodelability

+

AIservicequality

AIservice

assurance

AIsecurityandprivacyprotection

Scenario-drivenAI

Intelligentdata

analysis

NWDAF

UE

CN

RAN

AIservers

AIforNetworks

Antennaweighttuning

Usermovementprediction

Problem：It‘schallengingtoguaranteereal-time,effective,and

consistentdata.CompletingtheentireAIprocessinvolveshightrialanderrorcosts.

High-speed

intelligentfollowing

NetworksforAI

ServiceOrderTransmission

Real-timemulti-agentcollaboration

Network

Usermovementprediction

CloudAIServiceProvider

UE

ChallengesintheIntegrationof5GNetworkandAI

Fulfilling6GandAIintegrationdemands,theuniversalityandefficiencyofexistingAIdesignmethods

drivenbyscenariousecases,plugins,orgraftsneedtobeimproved.

ExternalorgraftingAI

.AddAIserversorAI-relatednetworkfunctionstothenetwork,suchasNWDAF

.DesignseparateAImodelsforspecificairinterfaceandnetwork

optimizationusecases

Massivetrafficdata

Changingchannelconditions

NetworkManagement

Reducedswitchingperformance

Problem:AImodelshavelowgeneralization,long

developmentcycles,andhighcosts

.CloudAIserviceprovidersprovidebest-effortAIservicesafteruserssubmitorders

SubmitAINetwork

.DesigndifferentAIserviceprocessesfordifferentthird-partyAI

scenarios

InternetofVehicles

smartfactory

XR/VR

Problem:ThenetworkstrugglestorapidlydeployAIservicesfordiversescenarios

Problem：Dataisonlyuploadedtothecloud,makingitdifficultto

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efficientlyleveragetheubiquitousresourceswithinthenetwork,whichcannotguaranteethequalityandsecurityofAIservices

...

...

Cloud

CloudAIproviders

CN

CN

Trafficanalysis

Trafficanalysis

Antennaadjustment

Antennaadjustment

Movementprediction

Movementprediction

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6GNativeAIDesignPrinciples

Toachieveubiquitousintelligence,6Gnetworkarchitecturerequires"fourtransformations"

Fromexternaltonativeandintegrated

Frombest-efforttoAlservicequalityguarantee

Cloud

NWDAF

CommunicationQoS

(·)

AIworkflow1

5GExternalAI

6G

NativeAIAIworkflow2

CommunicationQoS

Fromscenariodriventocapabilitydriven

Frommultielementsdecouplingtocollaboration

Fourelementscollaboration

AI

Algorithm

Computing

Communication

()

Data

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2

Outline

Drivingforcesof6GnativeAI

6GnativeAIandkeyfeatures

36GandAILargeModel

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Method

6GNativeAInetwork

Challenge:AsthethreefundamentalcomponentsofAI(data,algorithmsandcomputing)havegained

significanceonparwithnetworkconnections,thedesignofthecorrespondingarchitecture,interfaces,andprotocolsshouldspantheentireAIlifecycle.

Dataplane:

Resourcelayer:

provideunderlyingresources

Networkfunctionlayer:

providespecificnetworkfunction/networkservicecapabilities

Applicationandservicelayer:

providecorrespondingsupportfor

customers'businessneeds.

managesnetworkdataandprovidesdataservices

Computingplane:

managescomputingandprovidescomputingservices

Intelligentplane:

providestheoperatingenvironmentforfulllife-cycleofnativeAI.

Unlike5Gnetwork,newdataplane,smartplane,andcomputingplanewillbedefinedin6Gnetwork,andtraditionalcontrolplaneanduserplaneareexpectedtobeextendedaswell.

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PlatformizedServiceNetwork

Management

&

Orchestration

Task

Management

Computin

g

Connection

UnifiedIPcomputing-

networkbase

TaskControl

OTN/OXC

OTN/OXC

KeyFeature1:AIServiceQuality(QoAIS)

TraditionalQoSsystemsprimarilyemphasizesessionandconnectionperformance,lackingcomprehensivesupport

fordiverserequirements；TheQoAISindicatorsystemincorporatessecurity,privacy,autonomy,andresource

overheadasnewevaluationdimensionstoformastandardizedAIservicequalityevaluationsystem.

QoAISGuaranteeMechanism

Smart

Entertainment

SmartLife

SmartCity

Smart

Community

Smart

Industry

AIService

ServiceQoS

TaskQoS

Data

ResourceQoS

AITask

Algorithm

OTN/OXC

Allopticalbase

Computing-NetworkInfrastructure

AppropriateData

collectionandprocessing

closedLbopFeedback

xNB

ControlPlane:ThreeModesofDeepConvergenceofComputingandCommunication

Mode1

Mode2

Mode3

Coordination

xNB

Connectioncontrol

Computingcontrol

Connectioncontrol

Computingcontrol

Convergedcontrol

CCB

CCB

CCB

CEB

CEB

ComputingTaskDataTransmission&Execution

Task1

CEB

CS

CEB

CCB

CEB

CCB

Task3

CCB

CCB

CEB

CEB

Task2

CEB

KeyFeature2:DeepintegrationofAIcomputingandcommunication

DesigninganativeAIprotocolthatintegratescomputingandcommunicationisnecessarytomeetAI‘sconnectivity

anddistributedcomputingserviceneeds.

Itisachievedthroughthreedimensions:ManagementPlane,ControlPlaneandUserPlane

Computingrequirementsfor6GnativeAI

Highcomputationalefficiency

Lowenergy

consumptionand

latency

MeetthedifferentiatedQoAISneeds

CEB：ComputingExecutionBearer

CCB：ComputingConnectionBearer

CS：ComputingSession=CEB+CCB

ManagementPlane

Functionalarrangement

QoSanalysis

CEB

UserPlane

collaborativedesignof

computingandcommunication

protocol

Datagenerationandoptimization

1.Reducethecostofdatacollectionandtransmission;

2.Solveproblemssuchasdifficultyinobtainingtraditionalrealdata;

3.Technology:DataAugmentationinGANs;

PrevalidationofAI

1.Intendedtocompleteperformanceprevalidationwithoutaffecting

networkoperations;

2.Reducepotentialrisksthatdecisionsmayleadto,suchasdeterioratingnetworkperformance;

KeyFeature3:DataGenerationandReliableAI

ThemassivetrainingdatademandandhighriskoftrialanderrorforAIinthenetworkrequirenetworkdigitaltwinstoachieveon-demanddatagenerationandreliableAIandverification

Pre-validation

Iterativeoptimization

Networkstateprediction

AI

vices

NetworkAIRequirements

Digitaltwinmodelingrequirements

ser

Externaldemand

Auto-generatedrequirements

Dataon-demand

collectionand

Requirementsfor

generation

Digitaltwinentity

NetworkDigitalTwin

Networkvirtual

scene

Processeddata

datacollection

andgeneration

Networkdecision

CU

DUAAU

VirtualizationCoreNetwork

CloudbasedRadioAccessNetwork

Radio

physical

network

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2

Outline

Drivingforcesof6GnativeAI

6GnativeAIandkeyfeatures

36GandAILargeModel

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AILargeModelforNetwork

TheConvergenceof6GandAILargeModel

AsAIenterstheeraofgeneralintelligence,theemergenceofFoundationModelspromisesaprofoundtransformationintheintegrationof6GandAI

NetworkforAILargeModel

ThenetworkservesasaplatformtosupportorprovideAILargeModelservices

?ProviderichenvironmentaldataforAILargeModel

?Offerintent-basedservicestousers

?Achieveglobalcollaborativecontrolofintelligentterminals

AILargeModelwillenhancemobilenetwork

servicesinaspectssuchasoperations,execution,andverification

Domains

Requirements

ImpactonNetworks

NetworkOperations

Multi-modalMachine

Learning,Language

Understanding,Text

Generation

Small

Network

Maintenance

Non-standardData

Governance,Data

Alignment,Natural

LanguageUnderstanding,

CodeGeneration

Medium

Network

Running

Non-standardData

Governance,Image

Generation,Video

Generation

Large

?DetectingFailuresandGeneratingSolutions

?OrchestratingandSchedulingTaskWorkflows

?PlayingaVitalRoleintheValidationPhase

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NetworksforAILargeModel

6GnativeAIfacilitatesthetrainingofAIlargemodelbyprovidinglinksanddataservicesduringthetrainingprocess,andsupportstheinferenceprocesswithlinks,computation,andmodeldecomposition/distributionservices

/Processeddata

6GNetworkCloudAIproviders

AILargeModeltrainingoftenneedshigh-speedfiber

opticconnectionsindatacenters,makingradionetworkdeploymentchallenging.

Collectinguserandnetworkdata,preprocessingit,andmanagingtraffictosupportmodeltraining

6Gnetworksprocessdataefficiently,reducingdata

transmissionandimprovingcloudAItrainingformodels

Therequiredspecialdataanalysistechniques?Howtoefficientlyscheduledatainadistributed?

Potential

gains

Futureissues

Features

Services

Dataprocessing

AItrainingservices

Massivedatacollection

UE

Processeddata

Inference

requests

AIinference

CloudAIproviders

AILargeModelrequiresignificantstoragespaceandpowerfulAIinferencechips,whichcannotbemetbyasinglebasestation.

Withpropermodelsegmentation,modelscanbedeployedinwirelessnetworkstoofferAIinferenceservices.

In6Gnetworks,deployingmodelsclosertouserscanreducelatency

Howtobalanceincreasedinferencelatencywithreduced

transmissionlatencyin6Gnetworks?Aretechniqueslikemodelsegmentation,compression,andaccelerationfeasiblefor

models?databeeffectivelyscheduledbetweennodes?15

AIinferenceservices

6GNetwork

UE

?

?

AILargeModelforNetwork

AILargeModelforNetworkfacesignificantchallengesduetotheabundanceofstructureddataandunclearcommonalitiesamongdifferentnetworkproblems,unlikeChatGPT

Exploringinphases,beginningwiththeexplorationofnetworkoperationsaigeneralmodels

Progressingfromsmall-scaletolarge-scaleandfromofflinetoreal-time,ultimatelyinvestigatingthe

feasibilityofunification

Small-scalelarge-scaleunified

Offline

Realtime

smallmodel1

smallmodel2

…

smallmodelN

Scenario-basedoperationmodel

Service-levelrunningmodel

Network-level

runningmodelSingle-systemrunningmodel

?

Operationuniversalmodel

Multi-scenario

universalrunning

model

NetworkAILargeModel

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Industry-widecollaborativedataopenness

6GANAcollaborateswithmultipleorganizations,includingtheNine

Heavensplatform,toreleasefourmajordatasets,creatinganindustrydata

sharingecosystemtosupportnetworkAIresearch!

Poordata

quality

TheChallengesofNetworkAILargeModel-Data

Networkoperationandmaintenancedataismainlyavailableatminute/hourintervalsfromaconsistentsource,

whilenetworkoperationaldataismorecomplexduetovaryingtimeintervals,standardization,anddatasources,

makingithardertoacquire.

Dataopennessandstandardization

Difficultdataacquisition

IntelligentRANSlicingDataset

CSICompressionFeedbackDataset

Dataopenness

Continuouslycuratingandaccumulatingintelligentnetwork

datasets,opentothepublic,tobuildaseriesofinnovative

smartnetworkecosystems,andsupportresearch

standardization

NetworkAIScheduling

TechnologyResearchDataset

RadioResourceSchedulingDataset

Collaboratewiththeindustrytojointlyformulatenewdata

collectionstandardsan