2026人工智能浪潮及其对6G的影响研究报告-

AISURGEANDITSIMPLICATIONS

FOR6G

V1.0

AISURGEANDITS

IMPLICATIONSFOR6G

byNGMNAlliance

Version:1.0

Date:

19February2026

DocumentType:

Public

Programme:

6G

Approvedby/Date:

NGMNBoard,16February2026

Publicdocuments(P):©2026NextGenerationMobileNetworksAlliancee.V.Allrightsreserved.NopartofthisdocumentmaybereproducedortransmittedinanyformorbyanymeanswithoutpriorwrittenpermissionfromNGMNAlliancee.V.TheinformationcontainedinthisdocumentrepresentsthecurrentviewheldbyNGMNAlliancee.V.ontheissuesdiscussedasofthedateofpublication.Thisdocumentisprovided“asis”withnowarrantieswhatsoeverincludinganywarrantyofmerchantability,non-infringement,orfitnessforanyparticularpurpose.Allliability(includingliabilityforinfringementofanypropertyrights)relatingtotheuseofinformationinthisdocumentisdisclaimed.Nolicense,expressorimplied,toanyintellectualpropertyrightsaregrantedherein.Thisdocumentisdistributedforinformationalpurposesonlyandissubjecttochangewithoutnotice.Readersshouldnotdesignproductsbasedonthisdocument.

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CONTENTS

EXECUTIVESUMMARY 4

01

INTRODUCTION 6

02

IMPACTSOFAITRAFFICONNETWORKS 7

2.1TrafficGrowth 7

2.2ShiftinNetworkRequirements 8

03

NETWORKFORAI 9

3.1Performancevs.BusinessValue 9

3.2CapabilitiesBeyondConnectivity 9

04

AIFORNETWORKANDIMPLICATIONS

FOR6GARCHITECTUREEVOLUTION 11

4.1NetworkManagementLayer 11

4.2CoreNetwork 12

4.3RadioAccessNetwork 12

4.4KeyChallengesandConsiderations 12

4.5Implicationsfor6GNetworkArchitectureEvolution 13

05

CONCLUSION&STANDARDISATIONFOCUSAREAS 15

5.1Conclusion 15

5.2RecommendedStandardisationFocusAreas 15

06

LISTOFABBREVIATIONS 17

07

REFERENCES 18

08

FIGURES 19

ACKNOWLEDGEMENTS 20

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EXECUTIVESUMMARY

Thisisapivotalmomentinthetelecommunicationsindustry,propelledbytheunprecedentedAIsurgeandthebeginningof6Gstandardisation.AIisadvancingatarapidpaceandwillremainadominantforce,reshapingsocietyfarbeyondthe6Gera.

ThisdocumentconsolidatesNGMN’sperspectivesonhowAIwilllikelyimpact6Gstandardisation,providingguidanceforongoing6Gstudies.Thisstudyexaminesthreekeydimensions:(1)impactofAItrafficonnetworks,(2)networkforAI,and(3)AIfornetworkandimplicationsfor6Garchitectureevolution.

ImpactofAITrafficonNetworks

TherapidproliferationofAIapplications–particularlythosewithautonomous,task-drivencapabilities-introducessignificantuncertaintyintofuturenetworkdemand.WhilethepreciseimpactoftheseAI-drivenworkloadsontrafficpatternsisdifficulttopredict,severalfactorscouldmateriallyaltertoday’sassumptions:

•Multi-modalAIapplications:Servicesrequiringreal-timevideoexchangemaydrivesubstantialtrafficgrowthandshifttraditionaltrafficpatterns.

•AI-enableddevicesandusecases:Consumerapplications(e.g.ARglasses)andenterprisescenarios(e.g.autonomousvehicles)couldrequirefrequentuploadofimagesandvideoafterlocalprocessing,increasinguplinkdemandandchallengingcurrentdownlink-heavynetworkdesigns.

•Geographicanddevicedensity:AI-intensiveareasanddeviceclustersmayexperiencesharp,localisedsurges,creatingincreasinglyuneventrafficpatterns.

Giventheseuncertainties,networkdesignmustprioritiseflexibility.StandardsDevelopmentOrganisationsshouldexploremechanismsthatallowsemi-permanentadjustmentsinuplink/downlinkratiowithoutrequiringmajorstandard

revisions,aswellassolutionstoenhancetheuplinkcoverage.ThisadaptabilitywillbecriticaltoaccommodateevolvingAI-drivenrequirementsacrossdiversedevices,networksandregions.

NetworkforAI

6GshouldgobeyondprovidingconnectivityservicestodelivernewAIenabledservicesandcapabilities(e.g.newdataexposure),bydesigningnetworksthataremoreintelligent,flexible,andtrustworthy.

Keydesignenablersinclude:

•Flexible(e.g.token-based)chargingmodelsreflectingrealresourceuse.

•DynamicandintelligentnetworkingforAIagentscollaboration.

•SupportforexplicitQoSandcomputingdemandfromanAI-basedapplication,tofacilitatemeetingtherequiredQoSatminimumcostandenvironmentalimpact.

•EnhancedQoSandadaptivepolicycontroltosupporttrafficroutingachievingseamlessconnectivity.

•Unifieddataandmodelframeworksacrossdevicesanddomains.

•Securetrust,authenticationandauthorisationmechanismsforAIagents’digitalidentity.

AIforNetworkandImplicationsfor6GArchitectureEvolution

AIisexpectedtobeanimportantnetworkcapabilityfor6Gnetworks,enablingmoreefficientusageofnetworkresources,networkautomation,intent-basedmanagementandintelligentorchestration.AIcouldbeapplicabletoalldomainsanddifferentlayersofthenetwork,includingtheoperationandmaintenance.

NGMNexpectsthat6GwillbeAI-ready,andthe5GService-BasedArchitecture(SBA)willbeconsideredasthestartingpointtowards6Garchitecture.

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ChallengesandconsiderationsforadoptingAI:

•AdoptionofAIcapabilitiesshouldallowagentsandlargelanguagemodels(LLM)tobedeployedinawaythatavoidsunnecessaryimpactontheexistingarchitecture.ThisshouldnotrestrictthepossibleintegrationofAI-relatedfeaturesembeddedwithinnetworkfunctions(NFs).

•AIinterfaces(e.g.,A2A,MCP)willcomplementexistingandfutureAPIs,ensuringreadinessforthetrafficvolumesandcapabilitiesrequiredbyemergingAIservices.

•Multi-vendorinteroperabilityframeworksareneededtoensuresecure,scalable,andopenecosystems.

•Deploymentstrategiesmustalignwithcostandsustainabilitygoals,andvalidationofreal-worldperformancegainsisessential.

•Continuedsupportfornon-AIalternativesifthesealternativesarenecessarytoensurereliability,flexibilityandopenness.

•CoordinatedUE–networkoperationisneeded,i.e.,toefficientlyexecuteAImodelsinbothtwo-sidedandone-sidedmodels.

RecommendedStandardisationFocusAreas

•Standardisedarchitecture,protocols,andinterfacesenablingefficientend-to-endsupportofAIfunctionalities,integratedacrossalldomains(RAN,Core,Transport)andallnetworklayers,includingdevices.

•StandardsthatsupportexplicitnetworkQoSandcomputingdemandfromanAI-basedapplication,tofacilitatemeetingtherequiredQoSatminimumcostandenvironmentalimpact.

•Standardsthatallowadaptabilitytosupportchangingtrafficpatterns,accommodatinguncertaintyintheimpactofevolvingAIusecases.

•Evolutionoftheexisting(5GSBA)networkarchitectureshouldbejustifiedbyvaluedrivenAIusecasesandservicescenarios,ensuringalignmentwithsocietalandbusinessneeds.

•6Gstandardsthatsupportagent-to-agentandagent-to-networkcommunications.

•FunctionalandperformancerequirementsforAIcapabilitiesacrossthe6Gsystem.

•Establishmentofinteroperabilityandtrustframeworkstoenablesecure,multi-vendor,andmulti-agentdeploymentsandoperations(includingmodelsretraining,finetuning).

•Emphasisonthereuse,adoption,orenhancementof“AIinterface”fromtelcoandnon-telcoworldswhereappropriateandmainstream.(e.g.(A2A)Agent-to-Agentor(MCP)ModelContextProtocol).

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01INTRODUCTION

Therapidevolutionoflarge-scaleAImodelsisdrivingaparadigmshifttowardan“AI-native”era.Theproliferationoflargelanguageandmulti-modalmodelsisenablingtheemergenceofAIagents—autonomous,collaborative,andself-learningentitiesthatmayoutnumberhumanusersinupcomingyears.Thisshifttowardpervasive,agent-drivenecosystemswillfundamentallyreshapeindustries,services,andeverydaylife.

Tosupportthistransformation,networksmayneedtoprogressivelyintroduceAIfeaturesforintent-drivenprogrammability,autonomousoperation,anddynamiccomputedistributionacrosscentralandedgedomains.Thisevolutionaimstodeliverdifferentiatedconnectivity,highreliability,energyefficiency,andsimplifiedoperation,positioning6GasthebestnetworkforAIandafoundationforAI-basedapplications,management,andinnovation.

As6Gstandardisationentersacriticalphase,thegrowthofAIandAIagentspresentsbothopportunitiesandchallengesformobilenetworkoperators(MNOs).NGMNhasoutlinedkey6Gobjectivesandarchitecturaldesignprinciplesemphasisinginnovationacrossnetworks,AI,computing,sensing,modularity,operationalsimplicity,sustainability,trustworthiness,cloudnativeness,network-as-a-service,automation,smoothmigration,andadisaggregatedmulti-vendorapproach.Theseprinciplesaimtoguidetheevolutionofnetworksthatareefficient,sustainable,cost-effective,andsociallybeneficial[1][2][3][4][5][6][7].

ToaddresstheimplicationsofAIonfuturenetworkdesignandensurealignmentwithNGMN’sobjectives,thisdocumentexaminesthreedimensionsfromanoperator’sperspectiveandhighlightsrecommendedstandardisationfocusareastosupportindustryalignment:

•ImpactofAItrafficonnetworks

•NetworkforAI

•AIfornetworkandimplicationsfornetworkarchitectureevolution

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02IMPACTSOFAITRAFFICONNETWORKS

2.1TRAFFICGROWTH

Today,mobiledataconsumptionisdominatedbyvideoapplications,accountingfor70-75%oftotaltraffic[8][9].Ahandfulofsocialmediaandstreamingservicescontributemorethan50%ofthisdemand.

AlthoughAIapplicationshavegrownexponentially,theircurrentimpactonmobilenetworktrafficremainsmodest–withprimaryinteractionsbeingtext-based[10].ThiscouldchangeasAIservicesproliferate,butpredictingthescaleofimpactremainshighlyspeculativeduetoseveralfactors:

•OptimisationofAIModels

AImodelsarebeingoptimisedusingtechniquessuchasquantisation,pruningandreducedtokensizestoenableefficienthigh-performanceinferencedirectlyondevice.[11]

•LocalProcessing

MorecomplexAImodelsareexpectedtorunnativelyondeviceaschipsetsevolvewithlargerandmorecapableNeuralProcessingUnits(NPU),fasteron-chipmemoryandcache,increasedRAMallocationforAIworkloadsandtighterintegrationofhardwarewithAIframeworksandruntimeengines.

•UnclearAdoptionCurve

End-useradoptioncurve:itremainsuncertainwhichnewservicesprovidetrueadditionalvalueforend-users,impactingservicesadoption,trafficcurvesandcommercialmodels.

•RegulatoryandPrivacyConstraints

Severalchallengeswouldneedtoberesolved,fordata-heavyAIfeatures,suchasautomaticimageorvideocaptureviaARglasses.

Againstthisuncertainty,thepotentialimpactofAIontrafficgrowthneedstobeconsideredinthefollowingaspects:

•SubstitutionofCurrentDemand

Multi-modalAIapplicationsarelikelytoproliferatecapturingmoreuserattention,withsmartphoneslikelyremainingaprimaryinterface.However,itisexpectedthatmostvideotrafficfromtheseapplicationswillreplaceexistinguserbehaviour–suchaswatchingsocialmediavideofeeds–ratherthancreatingtrulyincrementaldemand.

•PotentialRiseofWearables

ARglassesandsimilarinterfacescoulddramaticallyincreasetrafficiftheycontinuouslyinteractwithcloud-basedAIapplicationsusingvideoorimages.Thistrafficwouldbeconsideredincremental,ratherthansubstitutional,butadoptionhingesonovercomingprivacyandsecurityconcernsasdiscussedabove.

•EnterpriseandOtherApplications

Autonomousdrones,connectedcars,humanoidrobots/cobotsandindustrialAIusecasescouldaddsignificanttraffic—providedtechnologicalandregulatoryhurdlesarecleared.

•UplinkTrends

Currentuplinkdemandismoderate,butfutureusecasessuchasAIagentscouldreversethistrend[10].

AIagentswithadvancedperceptionandreasoningcapabilitiesmayresideonsmartphonesorwearables,continuouslygatheringdataandinteractingautonomously-potentiallygeneratingfarmoredatathanhumans,subjecttobatterycapacityandcomputationalpowerofthedevice.

However,thisshiftisuncertain,asmanyAIagentscouldinsteadoperateinthecloud,performinginferenceanddeliveringrecommendationstotheuser.

Futurescenariosdiffergreatlyinbothlikelihoodandscaleofimpact.Usecasesthatdrivetrulyincrementalvideotrafficbeyondtoday’sdemandwillexertthegreatestpressureonnetworks.Whilesomescenarios

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presentsignificantpotentialforincreaseddemand,theymustbeweighedagainsttheirlikelihoodwhensettingprioritiesfornetworkevolution.

Thisuncertaintymakesflexibilityacornerstoneof6Gstandardisation–ensuringthenetworkcanadaptseamlesslytodiverseandunpredictablerequirements.

2.2SHIFTINNETWORKREQUIREMENTS

TheriseofAImayintroducefundamentalchangesinboththeformanddirectionoftraffic:

•Machine-orientedMedia

Traditionalnetworksprimarilycarryhuman-perceivablecontent(text,images,audio,video).Incontrast,agent-to-agentcommunicationmayinvolveexchangingmodels,featurevectors,latentrepresentations,andotherformsofinformationoptimisedformachinesratherthanhumans.

•Uplink-heavyBehaviour

Whiletoday’strafficismostlydownlink-dominated,manyAI-enabledusecasesareassumedtoreversethispattern.Forinstance,ARglasseswithAImayrequirecontinuousuplinktransmissionofenvironmentalimages,andAI-inferencedautonomousvehiclesmayuploadreal-timevideoandsensordatamoreoften,incontrasttotraditionalconnectivitypatterns.

6Gnetworksshouldsupporttheseusecaseswithsufficientflexibilitytoincreaseuplinktrafficasamajordesigndriverfor6Gnetworks.Forexample,increaseduplink(UL)slotoccurrencesthatmaximisetheULtransmissionopportunitiestomanagetheincreasedULtrafficexpectedwithnewservices.

Someoftheproposalsthatarebeingdiscussedinindustryandunderreviewin3GPParearoundthedefinitionofflexibleanddynamicdownlink(DL)/ULpatterns,forexample,Full-DuplexorSub-bandFullDuplexoperation.EnhancingULcoverageisalsoadesirablefeature.

•RegionalandSectoralVariability

TheimpactofAItrafficwilldifferacrossregionsandindustries.Urbancentersarelikelytoexperience

moreAItrafficsurgesthanruralorremote

areas.Certainindustriessuchasmanufacturing,transportation,healthcare,andsmartcitiesmaygeneratehighervolumesofAItraffic.AI-intensiveareasanddeviceclustersmayexperiencesharp,localisedsurges,creatinguneventrafficpatterns.

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03NETWORKFORAI

AI-drivenapplicationsimposenewrequirementson6Gnetworks,encompassingnotonlyimprovedconnectivityperformancebutalsonewcapabilitiesbeyondconnectivity.

3.1PERFORMANCEVS.BUSINESSVALUE

Forperformanceimprovementsrelatedtotraditionalconnectivity,itisessentialtovalidatethenecessityofanynetworkenhancementsfromabusinessvalueperspectiveinordertoavoidunnecessaryinvestmentandresourcewaste.Whilenetworkoptimisationcanimproveuserexperiencetosomeextent,notallscenariosrequireextremeperformancegains,astheexistingservicesofferedmaynotbedirectlyimpactedbythesenetworkenhancements.Forexample,humansgenerallyhavearelativelyhightoleranceforlatencyinaudioandvideoconversationalinteractionsthroughInternet—comparedwithface-to-facecommunication,userscantypicallyacceptanadditionaldelayoftheorderofafewmillisecondswithoutasignificantimpactonexperience.

6Gaimstoenhancenetworkperformance,especiallyinrelationtonetworkcapacityandlatencyduetotheemergenceofnewservices,suchasAIapplications.However,intoday’stext-basedconversationalgenerativeAIservices,thedominantfactoraffectingresponsetimeisnotthenetworklatencybuttheprocessingdelayofcomputationallyintensiveAImodelsthatrequirespecialisedandhigh-performanceinfrastructure.Inthiscase,thebottleneckliesintheAIservicesandcomputinginfrastructureratherthanthenetwork.Assumingthesebottleneckswillberesolvedinthefuture,someserviceswillrequiretighternetworkperformancecontrol.Forinstance,inthecaseofconversationalAIservicesforreal-timeimmersiveexperiencethroughXRdevices(AR,VRandothers)thenetworkthroughputandlatencyrequirementswillbecomemorestringent,sothatthenetworkQualityofService(QoS)willneedtobetightlymanagedtoensuregooduserexperience.

Ingeneral,itisthereforeimportanttoidentifytheimpactofnetworkperformanceontheuserexperienceofanAIservice.

Beyondensuringadequateconnectivityperformance,thetruevalueof6GforAI-basedservicesliesindeliveringtherequiredcapabilitiestoefficientlysupportthesenewservices.

3.2CAPABILITIESBEYONDCONNECTIVITY

TosupportAIandAIagentseffectively,6Gshouldintegratecapabilitiessuchasdynamicnetworking,advancedQoS,distributedcomputing,trustmanagement,andintelligentorchestration.

•NewChargingModels

ChargingrulesformobileAIservicesandapplicationsshouldreflecttheirspecificdemandsonnetworkresources.Forinstance,atoken-basedchargingmodelcouldbeinvestigated,wheretokenscorrespondtofine-grainedunitsofresourceconsumption,suchasbandwidth,latencyguarantees,oredgecomputingcapacity.Thisapproachfacilitatesflexible,transparent,andscalabletransactionsamongusers,AIagents,serviceproviders,andnetworkoperators,promotingfaircostallocationwhileincentivisingefficientresourceusage.

•DynamicandIntelligentNetworking

FuturenetworksareexpectedtosupportdynamicandintelligentcollaborationamongphysicalAIagentsbyenablingtheon-demandcreationofintent-drivenprivatenetworks.Thesenetworksmaybeshort-livedandmission-specific,supportingscenariossuchascollaborativehumanoids/robots,droneswarms,roboticdogswarms,autonomousvehiclefleets,andindustrialembodiedAIagents.Comparedwithstaticgroupingmodels,suchephemeralnetworkgroupsareexpectedtosupportdynamicjoiningandleavingofagents,adapttochangingservicerequirementsand

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environmentalconditions,andminimisemanualprovisioningandoperationaloverhead,whiledynamicallyadjustingmembership,connectivity,andperformancecharacteristicsbasedontaskobjectives,agentmobilityandproximity,real-timeservicerequirements,andtrustandauthorisationpolicies.

•EnhancedQoSMechanisms

AIservicesareexpandingbeyondtexttobecomemulti-modalanditisexpectedthatthecommunicationofdifferenttypesofAI-relatedcontentwillneeddifferenttreatment.5GandpreviousgenerationshavesupportedQoSandnetworkslicingmechanismstosupporttrafficdifferentiation,butthereisnomeanstohaveclearenduserandnetwork-basedpoliciesthatenabletheroutingoftrafficintothemostsuitableconnection(e.g.intothecorrespondingsliceorQoSflow).6GshouldtargetamuchbetteruseofexistingQoSand/orslicingmechanismsandenableadvancedpolicycontrolwithfinergranularityforpriorityhandlingandmulti-modalinformationhandlingandsynchronisation.Toachievethis,improvingcollaborationwithOver-the-Top(OTT)applicationsanddevicemanufacturersisneededtofacethefuturedemandinthebestway.Furthermore,incasethenetworkinvolvesitselfinAIservicetasksinadditiontoofferingconnectivity,methodswillbeneededtoassuretheperformanceoftheAItasksundertaken,throughtasklevelmonitoring,measurementandprediction.

•EdgeComputing

AIandAIagentservicesdependheavilyoncomputingspeed,particularlyforlow-latencyinference.Edgedevices/anduserequipmentarelimitedincomputingpower,whilecentralisedcloudprocessingintroduceslatencyandbottlenecks,whichrequirescarefulassessmentsdependingongeo-location.6Gshouldsupportdistributededgecomputingtoenablereal-timeprocessing,

collaborativeintelligenceamongagents,andefficientresourceutilisationclosetothedatasource.

•UnifiedandDistributedDataFramework

Achieving“Intelligenceeverywhere”requiresbothdataandcomputeresourcestobeavailableubiquitously.Thisimpliestransparentdatasharingacrossdifferentdomains,which

requiresarchitectureenhancementssupportedbynewprotocolsand/orinterfaces.AIagentsandapplicationsneedtosharedata,models,inferences,andintermediateresultsacrossheterogeneousdevices.Withoutaunifiedframework,datamayremainsiloedandinconsistent.6Gshouldintroduceanend-to-enddataframeworktoenableefficientandflexibledata,model,andinferencesharing,management,processing,andstorageacrossUE,RAN,CoreNetworkfunctions(NFs)andapplicationfunctions(AFs).

•TrustandAuthentication

AIagentsactingonbehalfofcustomersrequiremutualauthenticationwithnetworks.Strongencryptionandintegritychecksareessentialforsensitivepromptsandpersonaldata.Trustframeworksarenecessaryforagent-to-agentcommunicationtoidentifyandblockmaliciousAIcontent.Complianceandlawfulinterceptioncapabilitiesmustbeinplacetomeetregulatoryobligations.

•DynamicandIntelligentResourceAllocation

AdaptiveschedulingisneededtohandleburstyAItraffic,prioritisinglatency-sensitivepromptsandinferenceswhileefficientlyutilisingsharedresources.OrchestrationbetweenedgeandcloudAImodelsenablesdynamicworkloaddistribution,optimisingperformance,scalability,andresourceefficiency,whiledynamicscalingofnetworkfunctionscanhelpimproveenergyefficiency.

•ResilienceandReliability

Mission-criticalAIapplications—suchasthoseinhealthcareorautonomouscontrol—requirecontinuousavailabilityandfailovermechanismstomaintainusertrust.

•AITrafficOptimisationandAIAgentInteraction

6Gshouldsupportfine-grainedtrafficanalyticstodistinguishbetweenmodelupdates,inferencerequests,andagentcommunications,enablingoptimisedmanagement.ThenetworkshouldalsosupportanAIagentsinteractionframeworkthatfacilitatesseamlessinteractionbetweenAIagents,thenetworkandthird-partyapplications.

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04AIFORNETWORKAND

IMPLICATIONSFOR6G

ARCHITECTUREEVOLUTION

AIisnotonlyaconsumerofnetworkresourcesbutalsoacoreenablerofnetworkevolution.AIwillnotjusthelptoimproveperformanceofnewnetworksbutalsotoenablenewservicesandusecasesthatwerenotpossiblewithpreviousgenerations,suchasdigitaltwinsandsensing.

However,6GisnotjustaboutAI.Someimportantlessonslearnedfrom5Gshowthatnetworkevolutionshouldfocusalsoonaspectssuchasnetworksimplificationandenergyefficiency.ThesetwoaspectsmaycontradictAIrequirementstosomeextent.AIrequirestheintroductionofnewnetworkentitiesandinterfaceswhichleadtoarchitecturalchanges,addingcomplexitytonetworkevolution.Additionally,AIenginestypicallyrequiremorecomputationalresources,leadingtosomeincreaseinenergyconsumption.

Therefore,withregardto6GdeploymentsitisimportanttorecognizethatAIworkloadsshouldbedeployedwheretheyaremostefficient—acrossnetworkdomains,layers,andphysicalsitesfromcentralcloudstotheedgeandevenenddevices–andwhenevertheyaddsomevalueintermsofnetworkperformanceanduserexperience,hencelookingforagoodtrade-offbetweenbusinessvalue,networkcomplexity,energyconsumptionandcost.

In6Gnetworks,AIisproposedtobedeeplyintegratedintothevariouslayersanddomainsofnetwork:RAN,transport,core,andmanagementandorchestration.Dependingonthelevelofintegration,AIcouldbringmorebenefitsorcouldposemorechallenges,henceitneedsacarefulevaluationofwhattherequirementsareateachdomain.

4.1NETWORKMANAGEMENTLAYER

Networkmanagementisapredominantlayerresponsibleforoverseeingallnetworkassets,andtheactionsittakescansignificantlyimprovenetworkperformanceandoperationalefficiencygiventhatitcontrolstheentirenetwork,makingitpossibletoadapttoservicerequirementsandscenarioconstraints.Forthisreason,inthis