Artificial Intelligence for 6G (2022) [Kim] [9783030950408] PDF
Preview Artificial Intelligence for 6G (2022) [Kim] [9783030950408]
Haesik Kim Artificial Intelligence for 6G Artificial Intelligence for 6G Haesik Kim Artificial Intelligence for 6G HaesikKim VTTTechnicalResearchCentreofFinland Oulu,Finland ISBN978-3-030-95040-8 ISBN978-3-030-95041-5 (eBook) https://doi.org/10.1007/978-3-030-95041-5 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland TomywifeHyeeun, daughterNaul, sonHanul and motherHyungsuk. Preface Thecellularsystemshavebeenincrementallyevolved,andtheoldandnewnetwork equipmentco-existsforacertainperiod.Likewise,the4Gequipmentwillcontinu- ouslyrollout,adoptanewfeature,andevolveto5Gsystems.5Gsystemsaredeployed progressively. The transition to 5G may take longer time than 4G because many differentfeaturesshouldbeincluded.While5Gsystemsarenowdeployed,research groups of cellular communications and networks started investigating beyond 5G systemsandconceptualizing6Gsystems.6Gwillrevolutionizethewirelesscommu- nicationsandnetworksmoreintelligentlywithhigherrequirementsthan5Gsystems. Intheeraof6G,weneedagame-changingapproach.6Gsystemswillredefinethe communicationsandnetworksdependingontherequiredservices.6Gbusinesswill notplayagamebutchangeagame.Inordertosupportnewrequirementsandservices, anewbloodtechnologyisrequired.Artificialintelligence(AI)andmachinelearning (ML) will be one of key technologies for 6G. They are now matured technologies andimprovemanyotherresearchfieldssignificantly.AIandMLmakeourday-to- daylifeeasier.Theypervadeeveryaspectofourlife.Forexample,weusemobile phoneappssuchasmapsandnavigation,facialrecognition,autocorrecttext,search recommendation, and so on. In addition, Chabot, social media, social media, and Internet banking are widely used. They all are based on AI and ML technologies. Thus,manyexpertsandbusinessmenexpectthattheycandramaticallyimprovethe efficiencies of our workplaces as well as create new applications and services. AI willplayacriticalroleinwirelesscommunicationsandnetworksandchangehow wedesignandmanage6Gcommunicationsandnetworks.WeexpectthatAImakes communicationsandnetworksdesignandmanagementsmarterandsafer.Keyques- tionisnotaboutwhetherbutwhenandhowtoimplementAIin6Gcommunication systems. ThisbookintroducesAItechniquesforwirelesscommunicationsandnetworks and helps audiences find an optimal, sub-optimal, or trade-off solution for each communicationsandnetworksproblemusingAItechniques.Thetargetaudiencesare seniorundergraduatestudents,graduatestudents,andyoungresearcherswhohavea backgroundaboutfundamentalsofwirelesscommunicationsandnetworksandstart studyingAIandMLtechniques.Fromthisbook,audiencesunderstandhowtoobtain vii viii Preface asolutionunderspecificconditionsandrealizethelimitofthesolution.Thisbook introduces,inastep-by-stepmanner,AItechniquessuchasunsupervisedlearning, supervised learning, reinforcement learning, and deep learning and explains how theyareusedforwirelesscommunicationsandnetworkssystem.Theorganization ofthebookisasfollows:InPartI,AItechniquesareintroduced.Itwillprovideaudi- enceswithamathematicalbackgroundaboutAIalgorithms.Unsupervisedlearning includeshierarchicalclustering,partitionalclustering,associationrulemining,and dimensionalityreduction.Supervisedlearningcoversdecisiontree,K-nearestneigh- bouring,andsupportvectormachine.Linearregression,gradientdescentalgorithms, andlogisticregressionarediscussed.Inreinforcementlearning,bothmodel-based approachesandmodel-freeapproachesareinvestigated.Deeplearningisdiscussed fromaperceptrontoneuralnetworks,convolutionalneuralnetworks,andrecurrent neural networks. In Part II, 6G communication and network systems are designed andoptimizedusingbothwirelesscommunicationsandnetworkstechniquesandAI techniques.Inphysicallayer,datalinklayer,andnetworklayers,keyalgorithmsare selectedandintroduced.AItechniquesareadoptedinwirelesscommunicationsand networkssystems.WelookintohowAItechniqueshelpthemtoimprovetheperfor- mance.6Gsystemsarenowunderdiscussioninacademy,standardizationbody,and industry.6Gusecases,requirements,andkeyenablingtechniquesarediscussedas a preliminary. In physical layer, channel model, signal detection, channel estima- tion, error control coding and modulation, and MIMO are explained. In data link layer,wefocusonresourceallocationtechniquesasoneselectedresearchtopic.In networklayer,cellularsystemisintroduced.Wefocusonnetworktrafficprediction techniquesasoneofkeyAI-enablednetworklayertechniques. IampleasedtoacknowledgethesupportofVTTTechnicalResearchCentreof FinlandandSpringerandalsothevaluablediscussionofmycolleaguesandexperts inEUproject5G-HEART.Iamgratefulforthesupportofmyfamilyandfriends. Oulu,Finland HaesikKim Contents PartI ArtificialIntelligenceTechniques 1 HistoricalSketchofArtificialIntelligence ....................... 3 1.1 IntroductiontoArtificialIntelligence ........................ 3 1.2 HistoryofArtificialIntelligence ............................ 9 References .................................................... 14 2 ArtificialIntelligenceEcosystem,Techniques,andUseCases ...... 15 2.1 ArtificialIntelligenceEcosystem ........................... 15 2.2 HardwareandSoftwareofArtificialIntelligence .............. 18 2.3 ArtificialIntelligenceTechniquesandSelection ............... 27 2.4 ArtificialIntelligenceWorkflowandUseCases ............... 29 References .................................................... 33 3 UnsupervisedLearning ........................................ 35 3.1 TypesandPerformanceMetricsofUnsupervisedLearning ..... 35 3.2 ClusteringAlgorithms .................................... 48 3.2.1 HierarchicalClustering ............................ 48 3.2.2 PartitionalClustering .............................. 55 3.3 AssociationRuleMining .................................. 70 3.4 DimensionalityReduction ................................. 77 3.5 Problems ................................................ 84 References .................................................... 85 4 SupervisedLearning .......................................... 87 4.1 SupervisedLearningWorkflow,Metrics,andEnsemble Methods ................................................ 87 4.2 ClassificationofSupervisedLearning ....................... 96 4.2.1 DecisionTree .................................... 97 4.2.2 K-NearestNeighbours ............................. 109 4.2.3 SupportVectorMachine ........................... 119 4.3 RegressionofSupervisedLearning ......................... 138 4.3.1 LinearRegression ................................. 139 ix x Contents 4.3.2 GradientDescentAlgorithms ....................... 149 4.3.3 LogisticRegression ............................... 164 4.4 Problems ................................................ 178 References .................................................... 181 5 ReinforcementLearning ....................................... 183 5.1 Introduction to Reinforcement Learning and Markov DecisionProcess ......................................... 184 5.2 Model-BasedApproaches ................................. 196 5.2.1 PolicyIteration ................................... 197 5.2.2 ValueIteration ................................... 206 5.3 Model-FreeApproaches ................................... 212 5.3.1 MonteCarloMethods ............................. 213 5.3.2 Temporaldifferencelearningmethods ............... 226 5.4 Problems ................................................ 244 References .................................................... 246 6 DeepLearning ................................................ 247 6.1 IntroductiontoDeepLearning ............................. 249 6.2 DeepNeuralNetwork ..................................... 252 6.3 ConvolutionalNeuralNetwork ............................. 280 6.4 RecurrentNeuralNetwork ................................. 288 6.5 Problems ................................................ 301 References .................................................... 303 PartII AI-EnabledCommunicationsandNetworksTechniques for6G 7 6GWirelessCommunicationsandNetworksSystems ............. 307 7.1 6GWirelessCommunicationsandNetworks ................. 308 7.1.1 6GUseCasesandRequirements .................... 309 7.1.2 6G Timeline, Technical Requirements, andTechnicalChallenges .......................... 313 7.1.3 6GKeyEnablingTechniques ....................... 317 7.2 AI-Enabled6GWirelessCommunicationsandNetworks ....... 331 7.2.1 AIandMLContributionstoPhysicalLayers .......... 332 7.2.2 AIandMLContributiontoDataLinkandNetwork LayersandOpenResearchChallenges ............... 335 7.3 Problems ................................................ 337 References .................................................... 338 8 AI-EnabledPhysicalLayer ..................................... 341 8.1 DesignApproachesofAI-EnabledPhysicalLayer ............ 342 8.2 End-To-EndPhysicalLayerRedesignwithAutoencoder ....... 350 8.3 WirelessChannelModels .................................. 359 8.4 SignalDetectionandModulation ........................... 361 8.5 ChannelEstimation ....................................... 367 Contents xi 8.6 ErrorControlCoding ..................................... 376 8.7 MIMO .................................................. 385 8.8 Problems ................................................ 396 References .................................................... 398 9 AI-EnabledDataLinkLayer ................................... 401 9.1 DesignApproachesofAI-EnabledDataLinkLayer ........... 402 9.2 RadioResourceAllocationandScheduling .................. 408 9.2.1 Resource Allocation Problems in Wireless NetworksandConvexOptimization ................. 410 9.2.2 ResourceAllocationModelsandPerformance Measure ......................................... 416 9.2.3 Utility Functions and Fairness of Resource Allocation ....................................... 429 9.2.4 ResourceAllocationUsingAITechniques ............ 441 9.3 HandoverUsingAITechniques ............................ 452 9.4 Problems ................................................ 455 References .................................................... 457 10 AI-EnabledNetworkLayer .................................... 461 10.1 DesignApproachesofAI-EnabledNetworkLayer ............ 461 10.2 CellularSystemsandNetworking ........................... 474 10.2.1 EvolutionofCellularNetworks ..................... 474 10.2.2 ConceptofCellularSystems ....................... 479 10.2.3 CellPlanning .................................... 493 10.3 NetworkTrafficPrediction ................................. 496 10.3.1 ClassicNetworkTrafficPrediction .................. 496 10.3.2 AI-EnabledNetworkTrafficPrediction .............. 511 10.4 Problems ................................................ 515 References .................................................... 517 Index ............................................................. 521
The list of books you might like
