Applications of Machine Learning in Wireless Communications

492 Pages·2019·25.412 MB·English

by  Ruisi He, Zhiguo Ding

IETTELECOMMUNICATIONSSERIES81
Applications of Machine
Learning in Wireless
Communications
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Applications of Machine
Learning in Wireless
Communications
Edited by
Ruisi He and Zhiguo Ding
The Institution of Engineering and Technology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
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Contents
Foreword xiii
1 Introductionofmachinelearning 1
Yangli-aoGeng,MingLiu,QingyongLi,andRuisiHe
1.1 Supervisedlearning 1
1.1.1 k-Nearestneighboursmethod 2
1.1.2 Decisiontree 4
1.1.3 Perceptron 9
1.1.4 Summaryofsupervisedlearning 19
1.2 Unsupervisedlearning 20
1.2.1 k-Means 21
1.2.2 Density-basedspatialclusteringofapplicationswithnoise 23
1.2.3 Clusteringbyfastsearchandfindofdensitypeaks 24
1.2.4 Relativecoremergeclusteringalgorithm 27
1.2.5 GaussianmixturemodelsandEMalgorithm 29
1.2.6 Principalcomponentanalysis 34
1.2.7 Autoencoder 37
1.2.8 Summaryofunsupervisedlearning 40
1.3 Reinforcementlearning 41
1.3.1 Markovdecisionprocess 42
1.3.2 Model-basedmethods 44
1.3.3 Model-freemethods 46
1.3.4 Deepreinforcementlearning 50
1.3.5 Summaryofreinforcementlearning 53
1.4 Summary 56
Acknowledgement 57
References 57
2 Machine-learning-enabledchannelmodeling 67
ChenHuang,RuisiHe,AndreasF.Molisch,ZhangduiZhong,andBoAi
2.1 Introduction 67
2.2 Propagationscenariosclassification 69
2.2.1 Designofinputvector 70
2.2.2 Trainingandadjustment 71
2.3 Machine-learning-basedMPCclustering 72
2.3.1 KPowerMeans-basedclustering 73
2.3.2 Sparsity-basedclustering 76
vi Applicationsofmachinelearninginwirelesscommunications
2.3.3 Kernel-power-density-basedclustering 78
2.3.4 Time-cluster-spatial-lobe(TCSL)-basedclustering 82
2.3.5 Target-recognition-basedclustering 82
2.3.6 Improvedsubtractionforcluster-centroidinitialization 84
2.3.7 MR-DMSclustering 86
2.4 AutomaticMPCtrackingalgorithms 89
2.4.1 MCD-basedtracking 89
2.4.2 Two-waymatchingtracking 90
2.4.3 Kalmanfilter-basedtracking 91
2.4.4 ExtendedKalmanfilter-basedparametersestimation
andtracking 92
2.4.5 Probability-basedtracking 93
2.5 Deeplearning-basedchannelmodelingapproach 95
2.5.1 BP-basedneuralnetworkforamplitudemodeling 96
2.5.2 Developmentofneural-network-basedchannelmodeling 96
2.5.3 RBF-basedneuralnetworkforwirelesschannelmodeling 99
2.5.4 Algorithmimprovementbasedonphysicalinterpretation 101
2.6 Conclusion 103
References 103
3 Channelpredictionbasedonmachine-learningalgorithms 109
XueJiangandZhimengZhong
3.1 Introduction 109
3.2 Channelmeasurements 110
3.3 Learning-basedreconstructionalgorithms 111
3.3.1 Batchalgorithms 111
3.3.2 Onlinealgorithms 124
3.4 Optimizedsampling 126
3.4.1 Activelearning 126
3.4.2 Channelpredictionresultswithpath-lossmeasurements 127
3.5 Conclusion 130
References 131
4 Machine-learning-basedchannelestimation 135
YueZhu,GongpuWang,andFeifeiGao
4.1 Channelmodel 137
4.1.1 Channelinputandoutput 138
4.2 Channelestimationinpoint-to-pointsystems 139
4.2.1 Estimationoffrequency-selectivechannels 139
4.3 Deep-learning-basedchannelestimation 140
4.3.1 Historyofdeeplearning 140
4.3.2 Deep-learning-based channel estimator for orthogonal
frequencydivisionmultiplexing(OFDM)systems 142
Contents vii
4.3.3 DeeplearningformassiveMIMOCSIfeedback 145
4.4 EM-basedchannelestimator 149
4.4.1 BasicprinciplesofEMalgorithm 149
4.4.2 AnexampleofchannelestimationwithEMalgorithm 152
4.5 Conclusionandopenproblems 156
References 157
5 Signalidentificationincognitiveradiosusingmachinelearning 159
JingwenZhangandFanggangWang
5.1 Signalidentificationincognitiveradios 159
5.2 Modulationclassificationviamachinelearning 161
5.2.1 Modulationclassificationinmultipathfadingchannelsvia
expectation–maximization 162
5.2.2 Continuousphasemodulationclassificationinfading
channelsviaBaum–Welchalgorithm 170
5.3 Specificemitteridentificationviamachinelearning 178
5.3.1 Systemmodel 179
5.3.2 Featureextraction 181
5.3.3 IdentificationprocedureviaSVM 185
5.3.4 Numericalresults 189
5.3.5 Conclusions 194
References 195
6 Compressivesensingforwirelesssensornetworks 197
WeiChen
6.1 Sparsesignalrepresentation 198
6.1.1 Signalrepresentation 198
6.1.2 Representationerror 199
6.2 CSandsignalrecovery 200
6.2.1 CSmodel 200
6.2.2 Conditionsfortheequivalentsensingmatrix 202
6.2.3 Numericalalgorithmsforsparserecovery 204
6.3 OptimizedsensingmatrixdesignforCS 206
6.3.1 Elad’smethod 206
6.3.2 Duarte-CarvajalinoandSapiro’smethod 208
6.3.3 Xuetal.’smethod 209
6.3.4 Chenetal.’smethod 210
6.4 CS-basedWSNs 211
6.4.1 Robustdatatransmission 211
6.4.2 Compressivedatagathering 213
6.4.3 Sparseeventsdetection 214
6.4.4 Reduced-dimensionmultipleaccess 216
6.4.5 Localization 217
6.5 Summary 218
References 218
viii Applicationsofmachinelearninginwirelesscommunications
7 Reinforcementlearning-basedchannelsharinginwireless
vehicularnetworks 225
AndreasPressas,ZhengguoSheng,andFalahAli
7.1 Introduction 225
7.1.1 Motivation 226
7.1.2 Chapterorganization 227
7.2 Connectedvehiclesarchitecture 227
7.2.1 Electroniccontrolunits 227
7.2.2 Automotivesensors 228
7.2.3 Intra-vehiclecommunications 228
7.2.4 Vehicularadhocnetworks 228
7.2.5 Networkdomains 229
7.2.6 Typesofcommunication 229
7.3 Dedicatedshortrangecommunication 231
7.3.1 IEEE802.11p 231
7.3.2 WAVEShortMessageProtocol 232
7.3.3 Controlchannelbehaviour 233
7.3.4 Messagetypes 234
7.4 TheIEEE802.11pmediumaccesscontrol 234
7.4.1 Distributedcoordinationfunction 234
7.4.2 Basicaccessmechanism 235
7.4.3 Binaryexponentialbackoff 236
7.4.4 RTS/CTShandshake 237
7.4.5 DCFforbroadcasting 238
7.4.6 Enhanceddistributedchannelaccess 238
7.5 Networktrafficcongestioninwirelessvehicularnetworks 239
7.5.1 Transmissionpowercontrol 240
7.5.2 Transmissionratecontrol 240
7.5.3 Adaptivebackoffalgorithms 240
7.6 Reinforcementlearning-basedchannelaccesscontrol 241
7.6.1 Reviewoflearningchannelaccesscontrolprotocols 241
7.6.2 Markovdecisionprocesses 242
7.6.3 Q-learning 242
7.7 Q-learningMACprotocol 243
7.7.1 Theactionselectiondilemma 243
7.7.2 Convergencerequirements 244
7.7.3 Aprioriapproximatecontroller 244
7.7.4 Onlinecontrolleraugmentation 246
7.7.5 Implementationdetails 247
7.8 VANETsimulationmodelling 248
7.8.1 Networksimulator 248
7.8.2 Mobilitysimulator 249
7.8.3 Implementation 249
7.9 Protocolperformance 251
7.9.1 Simulationsetup 251
Contents ix
7.9.2 Effectofincreasednetworkdensity 252
7.9.3 Effectofdatarate 254
7.9.4 Effectofmulti-hop 255
7.10 Conclusion 256
References 256
8 Machine-learning-basedperceptualvideocodinginwireless
multimediacommunications 261
ShengxiLi,MaiXu,YufanLiu,andZhiguoDing
8.1 Background 261
8.2 Literaturereviewonperceptualvideocoding 264
8.2.1 Perceptualmodels 264
8.2.2 Incorporationinvideocoding 265
8.3 MinimizingperceptualdistortionwiththeRTEmethod 267
8.3.1 RatecontrolimplementationonHEVC-MSP 267
8.3.2 Optimizationformulationonperceptualdistortion 269
8.3.3 RTEmethodforsolvingtheoptimizationformulation 270
8.3.4 Bitreallocationformaintainingoptimization 274
8.4 Computationalcomplexityanalysis 275
8.4.1 Theoreticalanalysis 276
8.4.2 Numericalanalysis 278
8.5 Experimentalresultsonsingleimagecoding 279
8.5.1 Testandparametersettings 279
8.5.2 Assessmentonrate–distortionperformance 281
8.5.3 AssessmentofBD-ratesavings 287
8.5.4 Assessmentofcontrolaccuracy 289
8.5.5 Generalizationtest 290
8.6 Experimentalresultsonvideocoding 292
8.6.1 Experiment 296
8.7 Conclusion 300
References 302
9 Machine-learning-basedsaliencydetectionanditsvideodecoding
applicationinwirelessmultimediacommunications 307
MaiXu,LaiJiang,andZhiguoDing
9.1 Introduction 307
9.2 Relatedworkonvideo-saliencydetection 310
9.2.1 Heuristicvideo-saliencydetection 310
9.2.2 Data-drivenvideo-saliencydetection 311
9.3 Databaseandanalysis 312
9.3.1 Databaseofeyetrackingonrawvideos 312
9.3.2 Analysisonoureye-trackingdatabase 313
9.3.3 Observationsfromoureye-trackingdatabase 315
9.4 HEVCfeaturesforsaliencydetection 317
9.4.1 BasicHEVCfeatures 317