软银：运营商AI - 格局、挑战及前行之路 Telco Al - Landscape Challenges and Path Forward

Contents

Introduction:WhyAIisCrucialforTelco 3

PartI:Landscape 3

1AIforTelcoOverview 3

1.1HumanInteractionAI/LLM(callcenter,Q&Aoperation) 4

1.1.1AIasaCo-PilotforNetworkOperationsCenters 4

1.1.2ImprovingKnowledgeManagement 4

1.1.3CustomerExperienceImprovements 5

1.2OperationalAutomation 5

1.2.1AutomatingRoutineTasks 5

1.2.2IntelligentTroubleshooting 6

1.2.3ProactiveNetworkManagement 6

1.2.4MaximizingResourceUtilizationbyNetworkTuning 6

1.3RadioEvolution:“AI-for-RAN” 7

1.3.1CurrentIssuesinWirelessNetworks 7

1.3.2SolutionstotheChallengesthroughAIandML 8

1.3.3ExpectationsforAIandML 8

1.4Lookingaheadto6G 8

1.4.1ImprovingEnergyE^iciency 9

1.4.2EnhancingSecurityandPrivacy 9

1.4.3IssuestoConsiderBeforePracticalImplementation 9

1.5CostBenefitforAIIntegration 10

2CurrentIndustryInitiatives 11

2.1AI-RANAlliance:AI-NativeRadioAccessNetworks 11

2.2O-RANAlliance:OpenandIntelligentRANEcosystems 12

2.33GPPandITU:StandardizingAIforTelecommunications 13

2.4GlobalTelcoAIAlliance 13

PartII:UseCases,Challenges,PathForward 14

3UseCasesClassification:HumanAIandMachineAI 14

3.1HumanAI:Example 15

3.2MachineAI:Example 16

3.2.1ULChannelInterpolation 16

3.2.2SRSPrediction 17

3.2.3MACScheduler 18

1

4AIPillars 19

4.1Data 19

4.2Infrastructure 20

4.3BaseModel 21

5CreatingAIforTelco 21

5.1OptionsforCreatingAIModelsforTelco 21

5.2End-EndCycleofCreatingTelcoAIModels 26

Conclusion 26

References 27

Acknowledgment 27

2

Introduction:WhyAIisCrucialforTelco

ArtificialIntelligence(AI)hasbecomeaforcedrivinginnovationwiththeemergenceofGenerativeArtificialIntelligence(GenAI)andLargeLanguageModels(LLMs),becomingindispensableforallindustriesandverticalsectors.Telecommunicationsproviders(Telco)arenoexceptions,asGenAIissettofundamentallytransformhowtheymanagenetworks,interactwithcustomers,andoptimizeworkflows.ThiscomesatatimewhentheTelcoindustryisalreadyfacingincreasingchallengeswith5Ginvestments,heavyoperationalexpendituresand6Gemergingtowardstheendofthisdecade.However,intheseearlydaysofGenAI,severalchallengesexistinbuildingTelcoAImodelsandimplementingthem.Nevertheless,AIpresentsauniqueopportunityforTelcostorethinkhowtheirnetworksarebuiltandoperated.

Forthecapitalexpenses(CAPEX)pointofview,wecanreducethenumberofsites,equipment,andnecessaryspectrumbyimprovingspectrumeficiency.AItechnologiessuchasmachinelearninganddeeplearningcanenablemoreefectiveallocationandmanagementofradiofrequencies,helpingmaximizespectrumusageandminimizewaste.Thisimprovedspectrumeficiencycantranslatedirectlyintocostsavings,astheneedtoacquireadditionalspectrumlicensesispotentiallyreduced.

Fromtheoperationalexpenses(OPEX)pointofview,havingfewersitesandequipmenttooperatehelpsreducerunningcosts,includingland,labor,andelectricityexpenses.ByutilizingAIsupport,wecanmanageourinfrastructuremoreeficiently.Furthermore,implementingAI-drivensolutionsforcustomersupport,training,troubleshooting,configurationsetupsupport,andotheroperationaltaskswillhelpdecreaseoveralloperationalcosts.

AtSoftBank,wehaveembracedAIasatechnologycriticalforthefutureofmobilenetworks.Along-withourkeypartners,wehavebeenengagedinbuildinganddevelopingplatformsreadyforRadioAccessNetwork(RAN)stackandAIapplicationsontheedge.WehavealsobeenactivelydevelopingAI-basedmodelsforTelcostack(andoperations)andcontributingtobodiessuchastheAI-RANAlliance.Itisourintenttoshareourperspectivesonthistopicwiththebroaderindustry.

TherestofthisWhitePaperisorganizedasfollows:InthenextsectioninPartI,webrieflyelucidatetheimportanceofAIforTelcoacrosshumaninteractions,operations,andradionetworkimprovements.Inthefollowingsection,weprovideanoverviewofon-goingindustryinitiatives.InPartII,wefocusonourgeneralizedapproachtoAIforTelcoswithusecasesandTelco-specificchallenges.Wethenwalk-throughtheprocessofcreatingAIforTelcobyleveragingtheavailableAItoolkits.Finally,weconcludewithasummaryandapeekintoouron-goingworkonAIforTelco.ReaderswhoarefamiliarwithTelcoAIcanreadPartIandPartIIfairlyindependently.

PartI:Landscape

1AIforTelcoOverview

Inthissection,wedetailthepotentialbenefitsAIandLLMspresenttoTelcos.WedorecognizethatmanyTelcosarealreadyengagedinoneormoreoftheapplicationsweoutlinebelow,sowedonotintendtobecomprehensivebutprovideasnapshotofthepossibilities.ThissectionfocusesondescribingwhatAIcando.WewilldiscussourapproachtorealizingconceptssuchastheseinPartII.

3

1.1HumanInteractionAI/LLM(callcenter,Q&Aoperation)

ThetelecommunicationsindustryisincreasinglyleveragingAIandLLMsespeciallyinareasinvolvinghuman-operatorinteractions.Below,weexplorehowAIandLLMsaretransformingTelcooperations,theirimpactonproductivity,andtheirroleinautomatingmundaneworkflows.

1.1.1AIasaCo-PilotforNetworkOperationsCenters

AIandLLMsactasintelligentassistants,providingreal-timesupporttohumanoperatorsinnetworkoperationscenters(NOCs).ThefollowingitemsareexamplesofhowAIandLLMcanassistnetwork

operators.

lAnalyzeNetworkLogs:LLMscanprocessvastamountsofnetworktelemetrydatatoidentifypatterns,anomalies,andtrendsthatmightindicateunderlyingissues.Byrapidlyanalyzingthisdata,LLMshelpoperatorsquicklydiagnoseandresolvecustomerconcernsrelatedtonetworkperformance,connectivity,andotherservicedisruptions.Thisapproachnotonlyimprovesthespeedofissueresolution,butalsominimizespotentialdowntimeandenhancesoverallnetworkreliability.

lGenerateSummariesandRecommendations:NetworkoperatorscanrelyonLLMstodistillcomplexdatasetsintoconciseandactionablesummaries.TheseAIsystemsextractcriticalinsightsfromlargevolumesofdata,recommendspecificactionsbasedontheseinsights,andprovidestep-by-stepguidanceforresolvingidentifiedissues.Thiscomprehensivesupportallowsoperatorstomakeinformeddecisionsswiftly,ensuringeSicientnetworkmanagementandoptimalservicedelivery.

1.1.2ImprovingKnowledgeManagement

LLMscanenhanceinternalknowledgemanagement.Examplesofsuchsupportarelistedbelow.

lProvidingContextualAnswers:OperatorscanqueryLLMsforspecificinformationrelatedtonetworkconfigurations,troubleshootingprocedures,oroperationalprotocols.TheseAIsystemswillretrieveandpresentrelevantinformationquickly,eliminatingtheneedtomanuallysearchthroughextensivedocumentation.Thisnotonlysavestime,butalsoensuresthatoperatorshaveaccesstoaccurateandup-to-dateinformation.

lTrainingandOnboarding:NewoperatorscanuseLLM-poweredsystemstolearnaboutnetworkoperationsthroughinteractive,scenario-basedtrainingmodules.TheseAI-driventrainingprogramssimulatereal-worldnetworkissuesandguidenewhiresthroughtheresolutionprocess,acceleratingthelearningcurveandensuringtheyarewell-preparedforliveoperations.

lBridgingLanguageandExpertiseGaps:MultilingualLLMssupportoperatorsindiverseregionsbyprovidinglocalizedinsightsandinstructionsinmultiplelanguages.Thiscapabilityensuresconsistentoperationalstandardsandqualityofserviceacrossglobalteams,regardlessoflanguagebarriersorregionaldiSerencesinexpertise.Bybridgingthesegaps,LLMsenhancecommunicationandcollaborationwithininternationalTelcooperations.

4

1.1.3CustomerExperienceImprovements

CallCenterautomationusingAIandLLMsforinteractionwithcustomersisoneofthelow-hangingareasforimprovement.WiththeamazinginteractioncapabilitiesofLLMchatbots,theTelcoscansignificantlyreducetheircallcenterexpenseswhileleveragingvastamountsofsubscriberdata.ExamplesoftheAI-drivensystemsthatenhancecustomerexperienceareshownbelow.

lReducingServiceDisruptions:AI-drivenautomationcanquicklyaddresscommoncustomerissuesandqueries,reducingthelikelihoodofservicedisruption.Byprovidingimmediateandaccurateresponses,thesesystemsminimizetheneedforescalationtohumanagents,ensuringsmootherandmoreconsistentservice.

lImprovingResponseTime:TheabilityofLLMstorapidlyprocessandunderstandcustomerinquiriesleadstofasterresponsetime.Thispromptnessnotonlyresolvescustomerconcernsmoreeficiently,butalsoenhancesoverallservicequality.

lEnhancingCustomerSatisfactionandLoyalty:ByleveragingAItodelivertimelyandefectivecustomersupport,Telcoscansignificantlyimprovecustomersatisfaction.Satisfiedcustomersaremorelikelytoremainloyaltotheserviceprovider,reducingchurnratesandfosteringlong-termcustomerrelationships.Additionally,theinsightsgainedfromanalyzingcustomerinteractionscanbeusedtocontinuallyrefineandimprovethecustomersupportexperience.

1.2OperationalAutomation

OperationalautomationintelecommunicationsleveragesAIandLLMstostreamlineandenhancevariousaspectsofnetworkmanagementandservicedelivery.Theseadvancedtechnologiesplayapivotalroleinreducingthemanualworkloadonhumanoperators,optimizingnetworkperformance,andenablingproactivemaintenance.Byautomatingroutinetasks,facilitatingintelligenttroubleshooting,andsupportingproactivenetworkmanagement,AIandLLMscontributetosignificantimprovementsinoperationaleficiencyandcost-efectiveness.Below,weexplorespecificexamplesofhowthesetechnologiesarebeingutilizedinthetelecommunicationsindustry.

1.2.1AutomatingRoutineTasks

LLMsandAI-drivensystemsautomateroutinetasks,therebyfreeingupoperatorstoconcentrateonmorestrategicandvalue-addedactivities.Herearesomespecificexamplesofhowthesetechnologiesareutilized.

lPredictiveMaintenance:Byanalyzingsensordatacollectedfromnetworkequipment,AIsystemscanpredictwhencertaincomponentsarelikelytofail.Thisallowsmaintenanceteamstoaddresspotentialissuesproactively,schedulingrepairsorreplacementsbeforeanyactualfailuresoccur.Thispreemptiveapproachhelpspreventservicedisruption,enhancesnetworkreliability,andreducesunexpecteddowntime.

lDynamicNetworkConfigurations:AIcanoptimizenetworkconfigurationsdynamicallybyadjustingsettingsinreal-timetomanagebandwidth,frequency,and/orradioresourceblocksusagemoreeficiently.Duringpeaktimes,theAIsystemcanredistributenetworktrafictoavoidcongestion

5

andmaintainoptimalperformance.Thisensuresthatcustomersexperienceseamlessconnectivityevenduringhigh-demandperiods,thusimprovingoverallservicequality.

1.2.2IntelligentTroubleshooting

AIsystemsequippedwithLLMsarehighlyeSectiveinthetroubleshootingprocessastheyoSerseverallayersofsupporttoresolvenetworkissuesmoreeSiciently.

lIdentifyRootCauses:Thesesystemscananalyzelogsandtelemetrydatatoidentifytheunderlyingcausesofnetworkproblems.Byexaminingpatternsandanomalies,AIcanpinpointexactissuesthatmaybeaSectingnetworkperformance,allowingforquickerdiagnosisandtargetedinterventions.

lSuggestFixes:Onceaproblemisidentified,LLMscanprovideactionablerecommendationsforresolvingproblems.Thesesuggestionscanincludedetailedstepsformanualresolutionbyoperatorsorautomatedscriptsthatcanbeexecutedimmediately.Thisguidancesignificantlyreducesthetimerequiredtoaddressandrectifyproblems.

lAutomateResolutions:Insomecases,AIsystemscanexecutefixesautonomouslywithouthumanintervention.Forexample,ifacommonnetworkissueisdetected,theAIcanexecuteapredefinedsetofcorrectiveactionstoresolvetheproblemswiftly.Thiscapabilitynotonlyspeedsuptheresolutionprocess,butalsominimizestheburdenonhumanoperators.

1.2.3ProactiveNetworkManagement

AItechnologiesfacilitateproactivemanagementofnetworkinfrastructures,whichiscrucialformaintainingoptimalperformanceandpreventingissuesfromescalating.Here’showAImakesthispossible.

lMonitoringNetworkHealth:AIsystemscontinuouslyanalyzevariousmetricsrelatedtonetworkperformance,suchaslatency,throughput,anderrorrates.Bykeepingconstantvigilance,thesesystemscandetectpotentialissuesearly,enablingoperatorstoaddressthembeforetheyimpactservicequality.Thisongoingmonitoringensureshighnetworkreliabilityandcustomersatisfaction.

lCoreandTransportNetworkPlanning:AIcandynamicallyrecommendnetworkdesignsandpoliciesasnewdeploymentsoccur.Forinstance,whenexpandingnetworkcoverageorintroducingnewservices,AIcansuggestoptimalconfigurationsandresourceallocations.ThisensureseSicientuseoftheexistinginfrastructureandhelpsinmaintainingconsistentservicelevelsacrossthenetwork.

1.2.4MaximizingResourceUtilizationbyNetworkTuning

TheAIorchestratorleveragesadvancedAItechnologiestomaximizetheutilizationofnetworkassetsbyidentifyingineSicienciesandimplementingcorrectivemeasures.ThisoptimizationprocessensuresthatnetworkresourcesareusedeSectively,whichiscrucialformaintaininghighperformanceandreducingoperationalcostsinthetelecommunicationsindustry.

lRemovalofUnusedSites:AIalgorithmsanalyzetraSicpatternsandusagemetricstoidentifyunderutilizedorredundantsiteswithinthenetwork.Bydecommissioningthesesites,operatorscansignificantlyreduceoperationaloverhead,includingmaintenancecosts,energyconsumption,and

6

stafingrequirements.Thistargetedapproachensuresthatresourcesareallocatedtoareaswheretheyareneededmost,thusoptimizingnetworkeficiency.

lNight-TimeShutdowns:AIsystemscanintelligentlyschedulesiteshutdownsduringof-peakhours,suchasatnight,whennetworktraficisminimal.Thisstrategicshutdownconservesenergyandlowersoperationalcostswithoutimpactingservicequality.TheAIensuresthatessentialservicesremainavailablewhilenon-criticalsitesaretemporarilytakenoflinetosaveresources.

Networkingtuningisoftenacomplexandtime-consumingprocessforoperators.Traditionalmethodsrelyheavilyonmanualintervention,whichcanleadtosuboptimalconfigurationsandmissedopportunitiesforperformanceimprovement.AIsimplifiesthisprocessbycontinuouslyanalyzingnetworkbehaviorandrecommendingadjustmentsthatalignwithcurrentdemandandfutureprojections.Propertuningnotonlyimprovesresourceutilizationbutalsoenhancesoverallnetworkperformance.Bydynamicallyadjustingparameterssuchasbandwidthallocation,routingpaths,andloadbalancing,AIensuresbettercoverageandcapacity.

Aswecanseefromtheaboveexamples,theintegrationofAIandLLMsintelecommunicationsoperationsisrevolutionizinghuman-operatorinteractions.Thesetechnologiesnotonlyenhanceeficiencyandaccuracy,butalsohavethepotentialtosignificantlyreduceoperationalcosts.Byautomatingroutinetasks,enablingintelligenttroubleshooting,andsupportingproactivenetworkmanagement,AIandLLMsarevitaltoolsinthemodernTelcolandscape,drivingbothoperationalexcellenceandsuperiorcustomerexperiences.

1.3RadioEvolution:“AI-for-RAN”

1.3.1CurrentIssuesinWirelessNetworks

Eachgenerationofwirelessnetworks,fromLTEto5Gandfrom5Gto6G,isdefinedbythedemandsforfasterspeeds,greaterdatacapacity,andhigherperformance.Thedevicesandnetworkequipmentthatimplementthesestandardsarecontinuouslyevolvingtohandlemoretrafic.However,asmobiledatatraficcontinuestoincreasedaily,thereisapressingneedtofurtherexpandnetworkcapacity.Traditionally,thishasbeenaddressedbyaddingnewcellsitestoincreasenetworkcapacity.However,thisapproachencountersseveralchallenges,suchasthehardwareintroductioncycle,thescarcityofspacefornewcellsiteinstallationsinurbanareas,andtheperformancelimitsoftransportnetworks,makingitdificulttorapidlyincreasecapacity.

Anotherchallengeisthatmobilesystemscomprisenumerouscellsandfrequencybands,managedusinginter-cellcoordinationtechnologies.Inter-cellcoordinationacrossdiferentfrequenciesincludespracticessuchasCarrierAggregationandDualConnectivity.CoordinationwithinthesamefrequencyinvolvestechniqueslikeCoMP(CoordinatedMulti-Point),SFN(SingleFrequencyNetwork),andD-MIMO(DistributedMIMO).Whiletheseinter-cellcoordinationtechniquesenhanceeficiency,theyalsocontributetotheincreaseinnetworkcomplexity.

7

1.3.2SolutionstotheChallengesthroughAIandML

Inurbanareaswhereitischallengingtodeploynewcellsites,andinenvironmentswhereolderdevicesarestillinuse,AIandMLfunctionalitiesareexpectedtoenhancewirelessnetworkperformance.Forexample,animprovementinSINR(SignaltoInterferenceplusNoiseRatio)throughAI/MLcouldleadtothroughputincreases.Inareaswhereusertraficpatternsremainconsistent,thisenhancementinaveragethroughputcouldefectivelyequatetoanincreaseinnetworkcapacity.

1.3.3ExpectationsforAIandML

Wirelesspropagationiscomplexandvariable,andtherearelimitationstoimprovementsachievablethroughmathematicalmodelingalone.Therefore,usingAI/ML(ArtificialIntelligence/MachineLearning)tosolvethesecomplexissuesandenhancewirelessperformancehasbecomeacurrenttrend.Forexample,since3GPPRelease18,inter-cellcoordinationtechnologyhasgainedattention.Simplycombiningcellslogicallyasonecanleadtoadecreaseincapacity.Therefore,theschedulernotonlyallocatesPRBs(PhysicalResourceBlocks)asiftheywereinasinglecellbutalsoindependentlyallocatesPRBsasseparatecellsdependingonthesituation.Asthenumberofcoordinatingcellsincreases,thecomplexityalsoincreases.Tosolvethiscomplexproblem,itisnecessarytoappropriatelyprocessvariousdata,suchastheuser'smobility,datacommunicationstatus,radioquality,andthecongestionstatusofeachcoordinatingcell.Sincethisdataneedstobeaggregatedandanalyzed,aconfigurationlikeC-RAN(CentralizedRadioAccessNetwork)isdesirable.Usingtheaggregateddata,appropriateresourceallocationrequiresthecollection,computation,anddecision-makingofvariousdata.ThisisanareawhereAIexcels.Mobilesystemsoftenrequirecomplexprocessing,andthesechallengesexistatvariouslayers.Therefore,performanceimprovementsusingAIacrossalllayersarehighlyanticipated.

Figure1:ExpectationforAI/MLforwirelessperformanceenhancement

1.4Lookingaheadto6G

Mobiletechnologiesareevolving,shiftingfrom5Gto6G.TheITU-Risworkingon6Gstandardswithplanstocompletethemby2030.The3GPPwilldiscuss6Gin2026,aimingtofinishin2028.

8

Asignificantdiferencebetween6Gand5Gisthechangeintheunderlyingarchitecture.RANistraditionallycomposedofdedicatedhardwareandthisisreflectedin3GPPstandards.However,startingfromthe5Gera,significantchangeshavebeguntoappearintheRAN,suchasthepartialintroductionofsoftware-basedfront-haulsignalprocessingandstandardizationactivitiesinO-RAN.

AlthoughAIissaidtobethemainportioninthe6Gera,mostofthecurrent5GbasestationswerenotdesignedtosupportAI.Torealize6G,itisnecessarytotransitiontoanAI-nativenetworkarchitecture,assuggestedbythephrase'AIforNetwork,andNetworkforAI.'ThistransitionisexpectedtoenablemoreeficientimplementationofpreviouslydiscussedAI-drivenoperationsautomation,networkoptimization,andRANoptimizationusingAI/ML.

1.4.1ImprovingEnergyEOiciency

Improvingenergyeficiencyisacrucialchallengeinthenext-generationinfrastructure.Asignificantportionofthecurrentnetwork'stotalpowerconsumptionisattributabletoradiounits.Sincevirtualizingthehardwarepartofradiounitsthatactuallytransmitradiowavesisdificult,powerconsumptionincreaseswithgreaternumbersoffrequencybands.

However,notallradiounitsoperateatfullcapacity24/7.Therearetimeswhenusageislow,especiallyinruralareas,andthereareperiodswhentheyarehardlyusedatall.Turningthemofentirelyprecludesimmediateconnectivity.

ByusingAItopredictdemand,itbecomespossibletoreduceunnecessaryradiotransmissionsandoptimizepowerconsumptionwithoutcompromisingconnectivityperformance.

EnhancingSecurityandPrivacy

Inadditiontothecontinuousevolutionofconventionalcomputers,thedevelopmentofquantumcomputersisadvancing.Itissaidthatby2030,thecurrentencryptionmethodsusedincommunicationswillbecomevulnerable,andthestandardizationofnewencryptionmethods,termedPost-QuantumCryptography(PQC),isunderway.

Thesecuritychallengeisnotlimitedtoend-usersbutisalsoaconcernwithinnetworkinfrastructure,andtheneedforadvancedsecurityextendsbeyondthecommunicationlayer.Newsecuritytechnologiesarealsorequiredfordatastorageandauthentication.

Unfortunately,complexencryptionmethodsconsumeevenmorecomputingresources.Toaddressthisissue,researchisbeingconductedonutilizingAIinauthenticationandsecuritytechnologies.

1.4.2

IssuestoConsiderBeforePracticalImplementation

Torealizethefunctionalitiesdescribedherein,thereareseveralissuestobeaddressed,includinghowtooperatethesesystems.Belowaresomeexamples,thoughtheseareonlyafew,andadditionalchallengesareexpectedtoariseinthefuture.

9

lDataCollectionMethodsforAI/MLTraining:ToproduceAImodels,adatasetfortrainingisobviouslyrequired.InRANsystems,thebigchallengeismethodofdatacollectionfortraining.Forexample,whenconsideringtheapplicationofAItothelayersuchasL1,newdefinitionsandimplementationsfordatacollectionmethodsincommercialenvironmentsarenecessary.Statisticaldata,whichserveastypicalKPIs,areunsuitablefortraining,ascontinuousdataareneeded,butthevolumeofdatabecomesenormous.Therefore,eficientdatacollectionandtransfermethodsmustbeconsidered.

lApplicationMethodsofNewAIModelstoRAN:ForlowerlayerssuchasL1/L2,tomeetlatencyrequirements,itmaybenecessarytoplaceAIwithintheRANsoftware.AImodelswillundergofine-tuningandmaybereplacedwithnewalgorithms.ThisimpliesthattheAImodelswithintheRANwillneedtobeswappedout,buthowtodosowithoutimpactingserviceisacriticalchallengethatneedstobeaddressedforcommercialdeployment.

lManagementandMonitoringMethodsforAIModels:Evenwithinasingleusecase,multipleAImodelsmaybeinoperation.Forinstance,fine-tuningmightbecarriedouttosuitthecharacteristicsofurbanandruralareas.Therefore,AImodelswillvarybylayer,usecase,andcharacteristic.Incommercialoperations,therewillbediverseAImodels,eachofwhichneedstobeaccuratelymanagedandmonitoredcontinuously.Howtoachievethisisatopicthatneedsfurtherconsideration.

lDetectionandResolutionMethodsforAIModelErrors:IntheoperationofAI/ML,giventhatmobilesystemsrequirehighavailabilityandmustfunctionwithincomplexwirelessenvironments,theriskofperformancedegradationduetotheuseofAImustbeconsidered.Incommercialoperations,tominimizetheadverseefectsonusers,itisnecessarytodetectandresolveissuesearly.HowtodetectandsolvetheseAI-relatedproblemsatanearlystagemustbeexplored.

1.5CostBenefitforAIIntegration

Withtheincreasingtraficdrivenbyrichcontent,utilizingAItoimprovefrequencyutilizationeficiencyacrossthenetworkallowsformaintainingwirelessnetworkqualitywithfewercells.Thiscandelayorpreventtheneedforcellsplitting,whichnotonlyreducesthecapitalexpendituresassociatedwithcellinstallationbutalsolowersthelong-termoperationalcosts.

Inurbanareas,wherepopulationdensityandtrafictendtobehigh,Telcosfaceseveralchallengessuchasalackofavailablesitesforcellinstallationandhighcostsforprimelocations.Insuchurbanenvironments,AIintegrationofersevengreaterpotentialforcostsavings.Thepotentialcost-savingareasinclude:

lCellSiteConstruction:Theseincludeexpensesrelatedtoantennas,radiounits,transmissiondevices,theirinstallation,integration,andassociatedsoftwarerequiredforcellsplitting.

lOperationalCosts:Theseincludeexpensesrelatedtositerental,electricity,fiber,andpreventativemaintenancecoststiedtotheiroperation.Furthermore,bydynamicallyoptimizingnetworkequipmentusagebasedontraficdemands,excessivepowerconsumptioncanbemitigated,reducingelectricityexpenses.

10

lNetworkMonitoringandRepairCosts:Earlydetectionofsignsindicatingequipmentfailureallowsforpreventivemeasuressuchasscheduledmaintenanceandequipmentreplacementduringplannedinspections.Thishelpsreducecostsassociatedwithurgent,short-termrepairvisits.

lCellDesign:Comparedtomanualcelldesignbyspecialists,automatingtheidentificationofareaswithqualityissuesandsubsequentautomatedareadesigncanreducelaborcosts.

2CurrentIndustryInitiatives

Thetelecommunicationsindustryisundergoingatransformativeshiftwiththeintegrationofartificialintelligence(AI)intoitsoperations.Severalglobalalliances,includingtheAI-RANAlliance[1],O-RANAlliance[2],3GPP[3],InternationalTelecommunicationUnion(