6G原生AI无线网络与AI大模型6G（英）

Outline

Drivingforcesof6GnativeAI

6GnativeAIandkeyfeatures

6GandAILargeModel

2

DemandforUbiquitousIntelligence

AIhasbecomethecoredrivingforceforanewroundofindustrialtransformation.Theautomation,digitalizationandintelligenceoftheindustryrequireubiquitousintelligence.

NetworkautonomyneedsAI

CustomersneedAI

BusinessneedsAI

Operation&maintenance Emergencycommunication Voiceprintrecognition Machinetranslation MedicalRecognition Securitymonitor

Smartcoverage Customizednetwork SmartNavigation PersonalizedRecommend. Robotrescue SmartManufacturing

Theintegrationof6GandAIincludestwoaspects:"AIforNetwork"

and“NetworkforAI"

3

TheDrivingForceofAIforNetwork

Mobilecommunicationtechnologyfacesbottlenecks,requiringurgenttechnologicalinnovationandinterdisciplinaryintegration.AIisakeysolutionforenhancingnetworkperformance.

Traditionalcommunicationsystemsface

performancebottlenecks

Performance

Optimization

Conflictsbetweennetworkoperationefficiency,complexity,andcost

networkoperationandmaintenanceefficiency

contradictory

triangle

network cost

complexity

6Gposesmorechallengingdemand

metrics

Currenttechnologyfallsshortof

meeting6G'sneeds

Difficultyinestimatinglargerscale

MIMOchannels

Densebasestationdeploymentleads

toincreasedinterference

Morecomplexsystemdesignsleadto

increasedenergyconsumption

Complexroutinginheterogeneous

equipmentnetworks

Diversecommunicationscenario

requirementsarefragmented

AIenhancesnetworkperformance

airinterface

Moreaccuratechannel

information

Moreprecisepositioning

Enhancedinterference

cancellationcapabilities

Enhancedenergyefficiency,

spectralcapacity

network

Improvedsceneadaptation

speed

Morebalancedtraffic

scheduling

Fasternetworking

interferenceavoidance

Morerefinedbusiness

identification

Moreaccuratefaultlocation

4

TheDrivingForceofNetworkforAI

ITUextends6Gscenariostoubiquitousintelligence.AIneedstobetransformedintonewcapabilities

andservicesfor6GcommunicationnetworkstoachieveAIaaS

ITUextends6Gscenariostoubiquitousintelligence

GetAIanytime,anywhere

LowlatencyAI

inference/training

SupportmobileAI

AIservicequality

assurance

AIsecurityandprivacyprotection

6Gnetworkinherentlyprovides

AIservices

5G

communication communication

ability service

6G

communication+computing

ability

ability

AIservice

perceptionabilitydataabilityAImodelability

5

ChallengesintheIntegrationof5GNetworkandAI

Fulfilling6GandAIintegrationdemands,theuniversalityandefficiencyofexistingAIdesignmethodsdrivenbyscenariousecases,plugins,orgraftsneedtobeimproved.

Scenario-drivenAI ExternalorgraftingAI

AIfor

Networks

Networksfor

AI

DesignseparateAImodelsforspecificairinterfaceandnetworkoptimizationusecases

Massivetrafficdata

Intelligentdata

analysis

Changingchannel

Antennaweight

conditions

tuning

Reducedswitching

Usermovement

performance

prediction

Problem:AImodelshavelowgeneralization,long

developmentcycles,andhighcosts

DesigndifferentAIserviceprocessesfordifferentthird-partyAIscenarios

InternetofVehicles

High-speed

intelligentfollowing

smartfactory

Real-timemulti-agent

collaboration

XR/VR

Usermovement

prediction

AddAIserversorAI-relatednetworkfunctionstothenetwork,suchasNWDAF

AIservers CN NWDAF RAN UE

NetworkManagement

Problem：It‘schallengingtoguaranteereal-time,effective,andconsistentdata.CompletingtheentireAIprocessinvolveshightrialanderrorcosts.

CloudAIserviceprovidersprovidebest-effortAIservicesafteruserssubmitorders

SubmitAI

Network

ServiceOrder

Transmission

UE

Network

CloudAIServiceProvider

Problem:ThenetworkstrugglestorapidlydeployAI

Problem：Dataisonlyuploadedtothecloud,makingitdifficultto

efficientlyleveragetheubiquitousresourceswithinthenetwork,which

servicesfordiversescenarios

cannotguaranteethequalityandsecurityofAIservices

6

6GNativeAIDesignPrinciples

Toachieveubiquitousintelligence,6Gnetworkarchitecturerequires"fourtransformations"

Cloud Cloud

NWDAF CN

CN

AIworkflow1

5GExternalAI 6GNativeAI AIworkflow2

Commu

Comput

Data

AI

Fourelementscollaboration

nication

ing

Algorithm

CloudAI

providers

CommunicationQoS

CommunicationQoS

Trafficanalysis

Antennaadjustment

Movementprediction

...

Trafficanalysis

Antennaadjustment

Movementprediction

...

7

Outline

Drivingforcesof6GnativeAI

6GnativeAIandkeyfeatures

6GandAILargeModel

8

6GNativeAInetwork

Challenge:AsthethreefundamentalcomponentsofAI(data,algorithmsandcomputing)havegainedsignificanceonparwithnetworkconnections,thedesignofthecorrespondingarchitecture,interfaces,andprotocolsshouldspantheentireAIlifecycle.

Resourcelayer:

provideunderlyingresources

Networkfunctionlayer:

providespecificnetworkfunction/

networkservicecapabilities

Applicationandservicelayer:

providecorrespondingsupportfor

customers'businessneeds.

Dataplane:

managesnetworkdataandprovidesdataservices

Computingplane:

managescomputingandprovidescomputingservices

Intelligentplane:

providestheoperatingenvironmentforfulllife-cycleofnativeAI.

Method

Unlike5Gnetwork,newdataplane,smartplane,andcomputingplanewillbedefinedin6Gnetwork,andtraditionalcontrolplaneanduserplaneareexpectedtobeextendedaswell.

9

KeyFeature1:AIServiceQuality(QoAIS)

TraditionalQoSsystemsprimarilyemphasizesessionandconnectionperformance,lackingcomprehensivesupportfordiverserequirements；TheQoAISindicatorsystemincorporatessecurity,privacy,autonomy,andresourceoverheadasnewevaluationdimensionstoformastandardizedAIservicequalityevaluationsystem.

QoAISGuaranteeMechanism

SmartCity

Smart

SmartLife

Smart

Smart

Entertainment

Industry

Community

PlatformizedServiceNetwork

Management

AIService

ServiceQoS

&

Orchestration

AITask

TaskQoS

Task

Algorithm

Data

Management

ResourceQoS

Computin

Connectio

g

n

TaskControl

UnifiedIPcomputing-networkbase

OTN/OXC OTN/OXC OTN/OXC

Allopticalbase

Computing-NetworkInfrastructure

KeyFeature2:DeepintegrationofAIcomputingandcommunication

DesigninganativeAIprotocolthatintegratescomputingandcommunicationisnecessarytomeetAI‘sconnectivityanddistributedcomputingserviceneeds.

Itisachievedthroughthreedimensions:ManagementPlane,ControlPlaneandUserPlane

Computingrequirements

for6GnativeAI

Highcomputationalefficiency

Lowenergyconsumptionandlatency

MeetthedifferentiatedQoAISneeds

ControlPlane:ThreeModesofDeepConvergenceof

ComputingandCommunication

Mode1

Mode2

Mode3

Coordination

xNB

xNB

Connection

Computing

Connection

Computing

Converged

control

control

control

control

control

CCB CEB CCB CEB CCB CEB

ComputingTaskDataTransmission&Execution

Task1

CEB

CEB

CCB

CEB

CCB

Task3

CCB

CCB

CEB

CS

Task2

CEB

CEB

ManagementPlane

Functionalarrangement

QoSanalysis

UserPlane

collaborativedesignofcomputingandcommunicationprotocol

CEB：ComputingExecutionBearer

CCB：ComputingConnectionBearer

CS：ComputingSession=CEB+CCB

KeyFeature3:DataGenerationandReliableAI

ThemassivetrainingdatademandandhighriskoftrialanderrorforAIinthenetworkrequirenetworkdigitaltwinstoachieveon-demanddatagenerationandreliableAIandverification

NetworkDigitalTwin

Datagenerationand

optimization

Networkstateprediction

Networkvirtual

scene

Pre-validation

Iterative

optimization

1.Reducethecostofdatacollection

andtransmission;

2.Solveproblemssuchasdifficultyin

obtainingtraditionalrealdata;

3.Technology:DataAugmentationin

NetworkAI

Digitaltwinmodeling

Digitaltwin

AI

Requirements

services

requirements

entity

Externaldemand

Auto-generated

Requirementsfor

Dataon-demand

Processed

requirements

data

datacollection

collectionand

andgeneration

generation

Network

Radio

CU

DU

AAU

decision

physical

Virtualization

networkCoreNetwork

CloudbasedRadioAccessNetwork

GANs;

PrevalidationofAI

Intendedtocompleteperformanceprevalidationwithoutaffectingnetworkoperations;

2.Reducepotentialrisksthatdecisionsmayleadto,suchasdeterioratingnetworkperformance;

Outline

Drivingforcesof6GnativeAI

6GnativeAIandkeyfeatures

6GandAILargeModel

13

TheConvergenceof6GandAILargeModel

AsAIenterstheeraofgeneralintelligence,theemergenceofFoundationModelspromisesaprofoundtransformationintheintegrationof6GandAI

NetworkforAILarge

AILargeModelfor

Model

Network

ThenetworkservesasaplatformtosupportorprovideAILargeModelservices

ProviderichenvironmentaldataforAILargeModel

Offerintent-basedservicestousers

Achieveglobalcollaborativecontrolofintelligentterminals

AILargeModelwillenhancemobilenetworkservicesinaspectssuchasoperations,execution,andverification

Domains

Requirements

Impacton

Networks

Network

Multi-modalMachine

Small

Operations

Learning,Language

Understanding,Text

Generation

Network

Non-standardData

Medium

Maintenance

Governance,Data

Alignment,Natural

LanguageUnderstanding,

CodeGeneration

Network

Non-standardData

Large

Running

Governance,Image

Generation,Video

Generation

DetectingFailuresandGeneratingSolutions

OrchestratingandSchedulingTaskWorkflows

PlayingaVitalRoleintheValidationPhase

14

NetworksforAILargeModel

6GnativeAIfacilitatesthetrainingofAIlargemodelbyprovidinglinksanddataservicesduringthetrainingprocess,andsupportstheinferenceprocesswithlinks,computation,andmodeldecomposition/distributionservices

AItrainingservices

6G

AIinferenceservices

Processeddata

Massivedata

Dataprocessing

collection

Inferencerequests

Processeddata

Features

Services

Potential

gains

Futureissues

UE 6GNetwork CloudAIproviders

AILargeModeltrainingoftenneedshigh-speedfiberopticconnectionsindatacenters,makingradionetworkdeploymentchallenging.

Collectinguserandnetworkdata,preprocessingit,andmanagingtraffictosupportmodeltraining

6Gnetworksprocessdataefficiently,reducingdatatransmissionandimprovingcloudAItrainingformodels

Therequiredspecialdataanalysistechniques?Howtoefficientlyscheduledatainadistributed?

AIinference

UE 6GNetwork CloudAIproviders

AILargeModelrequiresignificantstoragespaceandpowerfulAIinferencechips,whichcannotbemetbyasinglebasestation.

Withpropermodelsegmentation,modelscanbedeployedinwirelessnetworkstoofferAIinferenceservices.

In6Gnetworks,deployingmodelsclosertouserscanreducelatency

Howtobalanceincreasedinferencelatencywithreducedtransmissionlatencyin6Gnetworks?Aretechniqueslikemodelsegmentation,compression,andaccelerationfeasiblefor

models?databeeffectivelyscheduledbetweennodes?

15

AILargeModelforNetwork

AILargeModelforNetworkfacesignificantchallengesduetotheabundanceofstructureddataandunclearcommonalitiesamongdifferentnetworkproblems,unlikeChatGPT

Exploringinphases,beginningwiththeexplorationofnetworkoperationsaigeneralmodels

Progressingfromsmall-scaletolarge-scaleandfromofflinetoreal-time,ultimatelyinvestigatingthe

feasibilityofunification

Small-scale

Offline Scenario-based

operationmodel

large-scale unified

Operationuniversal

model

smallmodel1

？

Service-level

smallmodel2

runningmodel

…

Network-level

smallmodelN

runningmodel

Realtime

Single-system

runningmodel

Multi-scenario？universalrunning

model

？NetworkAILargeModel

16

TheChallengesofNetworkAILargeModel-Data

Networkoperationandmaintenancedataismainlyavailableatminute/hourintervalsfromaconsistentsource,whilenetworkoperationaldataismorecomplexduetovaryingtimeintervals,standardization,anddatasources,makingithardertoacquire.

Dataopennessandstandardization

Difficultdata Poordata

acquisition quality

Industry-widecollaborativedataopenness

6GANAcollaborateswithmultipleorganizations,includingtheNineHeavensplatform,toreleasefourmajordatasets,creatinganindustrydatasharingecosystemtosupportnetworkAIresearch!

Dataopenness

Continuouslycuratingandaccumulatingintelligentnetworkdatasets,opentothepublic,tobuildaseriesofinnovativesmartnetworkecosystems,andsupportresearch

standardization

Collaboratewiththeindustrytojointlyformulatenewdatacollectionstandardsanddevelopadynamicdatacollectiongranularityschemetailoredtospecificneeds