A comprehensive review to the theory, application and research of machine learning for future wireless communications

In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities.

Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource:

Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks
Covers a range of topics from architecture and optimization to adaptive resource allocations
Reviews state-of-the-art machine learning based solutions for network coverage
Includes an overview of the applications of machine learning algorithms in future wireless networks
Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing

Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

FA-LONG LUO, Ph.D, Silicon Valley, California, USA
Dr. Fa-Long Luo is an IEEE Fellow and an Affiliate Full Professor of Electrical & Computer Engineering Department at the University of Washington in Seattle. Having gained international high recognition, Dr. Luo has 36 years of research and industry experience in wireless communication, neural networks, signal processing, machine learning and broadcasting with real-time implementation, applications and standardization. Including his well-received book: Signal Processing for 5G: Algorithms and Implementations (2016, Wiley-IEEE), Dr. Luo has published 6 books and more than 100 technical papers in the related fields. Dr. Luo has also contributed 61 patents/inventions which have successfully resulted in a number of new or improved commercial products in mass production. He has served as the Chairman of IEEE Industry DSP Standing Committee and the Technical Board Member of Signal Processing Society. Dr. Luo was awarded the Fellowship by the Alexander von Humboldt Foundation of Germany.

List of Contributors xv

Preface xxi

Part I Spectrum Intelligence and Adaptive Resource Management 1

1 Machine Learning for Spectrum Access and Sharing 3
Kobi Cohen

1.1 Introduction 3

1.2 Online Learning Algorithms for Opportunistic Spectrum Access 4

1.2.1 The Network Model 4

1.2.2 Performance Measures of the Online Learning Algorithms 5

1.2.3 The Objective 6

1.2.4 Random and Deterministic Approaches 6

1.2.5 The Adaptive Sequencing Rules Approach 7

1.2.5.1 Structure of Transmission Epochs 7

1.2.5.2 Selection Rule under the ASR Algorithm 8

1.2.5.3 High-Level Pseudocode and Implementation Discussion 9

1.3 Learning Algorithms for Channel Allocation 9

1.3.1 The Network Model 10

1.3.2 Distributed Learning, Game-Theoretic, and Matching Approaches 11

1.3.3 Deep Reinforcement Learning for DSA 13

1.3.3.1 Background on Q-learning and Deep Reinforcement Learning (DRL): 13

1.3.4 Existing DRL-Based Methods for DSA 14

1.3.5 Deep Q-Learning for Spectrum Access (DQSA) Algorithm 15

1.3.5.1 Architecture of the DQN Used in the DQSA Algorithm 15

1.3.5.2 Training the DQN and Online Spectrum Access 16

1.3.5.3 Simulation Results 17

1.4 Conclusions 19

Acknowledgments 20

Bibliography 20

2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27
Andres Kwasinski, Wenbo Wang, and Fatemeh Shah Mohammadi

2.1 Use of Q-Learning for Cross-layer Resource Allocation 29

2.2 Deep Q-Learning and Resource Allocation 33

2.3 Cooperative Learning and Resource Allocation 36

2.4 Conclusions 42

Bibliography 43

3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 45
Hadi Ghauch, Hossein Shokri-Ghadikolaei, Gabor Fodor, Carlo Fischione, and Mikael Skoglund

3.1 Background and Motivation 45

3.1.1 Review of Cellular Network Evolution 45

3.1.2 Millimeter-Wave and Large-Scale Antenna Systems 46

3.1.3 Review of Spectrum Sharing 47

3.1.4 Model-Based vs. Data-Driven Approaches 48

3.2 System Model and Problem Formulation 49

3.2.1 Models 49

3.2.1.1 Network Model 49

3.2.1.2 Association Model 49

3.2.1.3 Antenna and Channel Model 49

3.2.1.4 Beamforming and Coordination Models 50

3.2.1.5 Coordination Model 50

3.2.2 Problem Formulation 51

3.2.2.1 Rate Models 52

3.2.3 Model-based Approach 52

3.2.4 Data-driven Approach 53

3.3 Hybrid Solution Approach 54

3.3.1 Data-Driven Component 55

3.3.2 Model-Based Component 56

3.3.2.1 Illustrative Numerical Results 58

3.3.3 Practical Considerations 58

3.3.3.1 Implementing Training Frames 58

3.3.3.2 Initializations 59

3.3.3.3 Choice of the Penalty Matrix 59

3.4 Conclusions and Discussions 59

Appendix A Appendix for Chapter 3 61

A.1 Overview of Reinforcement Learning 61

Bibliography 61

4 Deep Learning–Based Coverage and Capacity Optimization 63
Andrei Marinescu, Zhiyuan Jiang, Sheng Zhou, Luiz A. DaSilva, and Zhisheng Niu

4.1 Introduction 63

4.2 Related Machine Learning Techniques for Autonomous Network Management 64

4.2.1 Reinforcement Learning and Neural Networks 64

4.2.2 Application to Mobile Networks 66

4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning 67

4.3.1 Deep Reinforcement Learning Architecture 67

4.3.2 Deep Q-Learning Preliminary 68

4.3.3 Applications to BS Sleeping Control 68

4.3.3.1 Action-Wise Experience Replay 69

4.3.3.2 Adaptive Reward Scaling 70

4.3.3.3 Environment Models and Dyna Integration 70

4.3.3.4 DeepNap Algorithm Description 71

4.3.4 Experiments 71

4.3.4.1 Algorithm Comparisons 71

4.3.5 Summary 72

4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach 72

4.4.1 Multi-Agent System Architecture 73

4.4.1.1 Cell Agent Architecture 75

4.4.2 Application to Fractional Frequency Reuse 75

4.4.3 Scenario Implementation 76

4.4.3.1 Cell Agent Neural Network 76

4.4.4 Evaluation 78

4.4.4.1 Neural Network Performance 78

4.4.4.2 Multi-Agent System Performance 79

4.4.5 Summary 81

4.5 Conclusions 81

Bibliography 82

5 Machine Learning for Optimal Resource Allocation 85
Marius Pesavento and Florian Bahlke

5.1 Introduction and Motivation 85

5.1.1 Network Capacity and Densification 86

5.1.2 Decentralized Resource Minimization 87

5.1.3 Overview 88

5.2 System Model 88

5.2.1 Heterogeneous Wireless Networks 88

5.2.2 Load Balancing 89

5.3 Resource Minimization Approaches 90

5.3.1 Optimized Allocation 91

5.3.2 Feature Selection and Training 91

5.3.3 Range Expansion Optimization 93

5.3.4 Range Expansion Classifier Training 94

5.3.5 Multi-Class Classification 94

5.4 Numerical Results 96

5.5 Concluding Remarks 99

Bibliography 100

6 Machine Learning in Energy Efficiency Optimization 105
Muhammad Ali Imran, Ana Flávia dos Reis, Glauber Brante, Paulo Valente Klaine, and Richard Demo Souza

6.1 Self-Organizing Wireless Networks 106

6.2 Traffic Prediction and Machine Learning 110

6.3 Cognitive Radio and Machine Learning 111

6.4 Future Trends and Challenges 112

6.4.1 Deep Learning 112

6.4.2 Positioning of Unmanned Aerial Vehicles 113

6.4.3 Learn-to-Optimize Approaches 113

6.4.4 Some Challenges 114

6.5 Conclusions 114

Bibliography 114

7 Deep Learning Based Traffic and Mobility Prediction 119
Honggang Zhang, Yuxiu Hua, Chujie Wang, Rongpeng Li, and Zhifeng Zhao

7.1 Introduction 119

7.2 Related Work 120

7.2.1 Traffic Prediction 120

7.2.2 Mobility Prediction 121

7.3 Mathematical Background 122

7.4 ANN-Based Models for Traffic and Mobility Prediction 124

7.4.1 ANN for Traffic Prediction 124

7.4.1.1 Long Short-Term Memory Network Solution 124

7.4.1.2 Random Connectivity Long Short-Term Memory Network Solution 125

7.4.2 ANN for Mobility Prediction 128

7.4.2.1 Basic LSTM Network for Mobility Prediction 128

7.4.2.2 Spatial-Information-Assisted LSTM-Based Framework of Individual Mobility Prediction 130

7.4.2.3 Spatial-Information-Assisted LSTM-Based Framework of Group Mobility Prediction 131

7.5 Conclusion 133

Bibliography 134

8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137
Benjamin Sliwa, Robert Falkenberg, and Christian Wietfeld

8.1 Mobile Crowdsensing 137

8.1.1 Applications and Requirements 138

8.1.2 Anticipatory Data Transmission 139

8.2 ML-Based Context-Aware Data Transmission 140

8.2.1 Groundwork: Channel-aware Transmission 140

8.2.2 Groundwork: Predictive CAT 142

8.2.3 ML-based CAT 144

8.2.4 ML-based pCAT 146

8.3 Methodology for Real-World Performance Evaluation 148

8.3.1 Evaluation Scenario 148

8.3.2 Power Consumption Analysis 148

8.4 Results of the Real-World Performance Evaluation 149

8.4.1 Statistical Properties of the Network Quality Indicators 149

8.4.2 Comparison of the Transmission Schemes 149

8.4.3 Summary 151

8.5 Conclusion 152

Acknowledgments 154

Bibliography 154

Part II Transmission Intelligence and Adaptive Baseband Processing 157

9 Machine Learning–Based Adaptive Modulation and Coding Design 159
Lin Zhang and Zhiqiang Wu

9.1 Introduction and Motivation 159

9.1.1 Overview of ML-Assisted AMC 160

9.1.2 MCS Schemes Specified by IEEE 802.11n 161

9.2 SL-Assisted AMC 162

9.2.1 k-NN-Assisted AMC 162

9.2.1.1 Algorithm for k-NN-Assisted AMC 163

9.2.2 Performance Analysis of k-NN-Assisted AMC System 164

9.2.3 SVM-Assisted AMC 166

9.2.3.1 SVM Algorithm 166

9.2.3.2 Simulation and Results 170

9.3 RL-Assisted AMC 172

9.3.1 Markov Decision Process 172

9.3.2 Solution for the Markov Decision 173

9.3.3 Actions, States, and Rewards 174

9.3.4 Performance Analysis and Simulations 175

9.4 Further Discussion and Conclusions 178

Bibliography 178

10 Machine Learning–Based Nonlinear MIMO Detector 181
Song-Nam Hong and Seonho Kim

10.1 Introduction 181

10.2 A Multihop MIMO Channel Model 182

10.3 Supervised-Learning-based MIMO Detector 184

10.3.1 Non-Parametric Learning 184

10.3.2 Parametric Learning 185

10.4 Low-Complexity SL (LCSL) Detector 188

10.5 Numerical Results 191

10.6 Conclusions 193

Bibliography 193

11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach 197
Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahario Yukawa, and Slawomir Stanczak

11.1 Introduction 197

11.2 Preliminaries 198

11.2.1 Reproducing Kernel Hilbert Spaces 198

11.2.2 Sum Spaces of Reproducing Kernel Hilbert Spaces 199

11.3 System Model 200

11.3.1 Symbol Detection in Multiuser Environments 201

11.3.2 Detection of Complex-Valued Symbols in Real Hilbert Spaces 202

11.4 The Proposed Learning Algorithm 203

11.4.1 The Canonical Iteration 203

11.4.2 Practical Issues 204

11.4.3 Online Dictionary Learning 205

11.4.3.1 Dictionary for the Linear Component 206

11.4.3.2 Dictionary for the Gaussian Component 206

11.4.4 The Online Learning Algorithm 206

11.5 Simulation 207

11.6 Conclusion 208

Appendix A Derivation of the Sparsification Metric and the Projections onto the Subspace Spanned by the Nonlinear Dictionary 210

Bibliography 211

12 Machine Learning for Joint Channel Equalization and Signal Detection 213
Lin Zhang and Lie-Liang Yang

12.1 Introduction 213

12.2 Overview of Neural Network-Based Channel Equalization 214

12.2.1 Multilayer Perceptron-Based Equalizers 215

12.2.2 Functional Link Artificial Neutral Network-Based Equalizers 215

12.2.3 Radial Basis Function-Based Equalizers 216

12.2.4 Recurrent Neural Networks-Based Equalizers 216

12.2.5 Self-Constructing Recurrent Fuzzy Neural Network-Based Equalizers 217

12.2.6 Deep-Learning-Based Equalizers 217

12.2.7 Extreme Learning Machine–Based Equalizers 218

12.2.8 SVM- and GPR-Based Equalizers 218

12.3 Principles of Equalization and Detection 219

12.4 NN-Based Equalization and Detection 223

12.4.1 Multilayer Perceptron Model 223

12.4.1.1 Generalized Multilayer Perceptron Structure 224

12.4.1.2 Gradient Descent Algorithm 225

12.4.1.3 Forward and Backward Propagation 226

12.4.2 Deep-Learning Neural Network-Based Equalizers 227

12.4.2.1 System Model and Network Structure 227

12.4.2.2 Network Training 228

12.4.3 Convolutional Neural Network-Based Equalizers 229

12.4.4 Recurrent Neural Network-Based Equalizers 231

12.5 Performance of OFDM Systems With Neural Network-Based Equalization 232

12.5.1 System Model and Network Structure 232

12.5.2 DNN and CNN Network Structure 233

12.5.3 Offline Training and Online Deployment 234

12.5.4 Simulation Results and Analyses 235

12.6 Conclusions and Discussion 236

Bibliography 237

13 Neural Networks for Signal Intelligence: Theory and Practice 243
Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, and Tommaso Melodia

13.1 Introduction 243

13.2 Overview of Artificial Neural Networks 244

13.2.1 Feedforward Neural Networks 244

13.2.2 Convolutional Neural Networks 247

13.3 Neural Networks for Signal Intelligence 248

13.3.1 Modulation Classification 249

13.3.2 Wireless Interference Classification 252

13.4 Neural Networks for Spectrum Sensing 255

13.4.1 Existing Work 256

13.4.2 Background on System-on-Chip Computer Architecture 256

13.4.3 A Design Framework for Real-Time RF Deep Learning 257

13.4.3.1 High-Level Synthesis 257

13.4.3.2 Design Steps 258

13.5 Open Problems 259

13.5.1 Lack of Large-Scale Wireless Signal Datasets 259

13.5.2 Choice of I/Q Data Representation Format 259

13.5.3 Choice of Learning Model and Architecture 260

13.6 Conclusion 260

Bibliography 260

14 Channel Coding with Deep Learning: An Overview 265
Shugong Xu

14.1 Overview of Channel Coding and Deep Learning 265

14.1.1 Channel Coding 265

14.1.2 Deep Learning 266

14.2 DNNs for Channel Coding 268

14.2.1 Using DNNs to Decode Directly 269

14.2.2 Scaling DL Method 271

14.2.3 DNNs for Joint Equalization and Channel Decoding 272

14.2.4 A Unified Method to Decode Multiple Codes 274

14.2.5 Summary 276

14.3 CNNs for Decoding 277

14.3.1 Decoding by Eliminating Correlated Channel Noise 277

14.3.1.1 BP-CNN Reduces Decoding BER 279

14.3.1.2 Multiple Iterations Between CNN and BP Further Improve Performance 279

14.3.2 Summary 279

14.4 RNNs for Decoding 279

14.4.1 Using RNNs to Decode Sequential Codes 279

14.4.2 Improving the Standard BP Algorithm with RNNs 281

14.4.3 Summary 283

14.5 Conclusions 283

Bibliography 283

15 Deep Learning Techniques for Decoding Polar Codes 287
Warren J. Gross, Nghia Doan, Elie Ngomseu Mambou, and Seyyed Ali Hashemi

15.1 Motivation and Background 287

15.2 Decoding of Polar Codes: An Overview 289

15.2.1 Problem Formulation of Polar Codes 289

15.2.2 Successive-Cancellation Decoding 290

15.2.3 Successive-Cancellation List Decoding 291

15.2.4 Belief Propagation Decoding 291

15.3 DL-Based Decoding for Polar Codes 292

15.3.1 Off-the-Shelf DL Decoders for Polar Codes 292

15.3.2 DL-Aided Decoders for Polar Codes 293

15.3.2.1 Neural Belief Propagation Decoders 293

15.3.2.2 Joint Decoder and Noise Estimator 295

15.3.3 Evaluation 296

15.4 Conclusions 299

Bibliography 299

16 Neural Network–Based Wireless Channel Prediction 303
Wei Jiang, Hans Dieter Schotten, and Ji-ying Xiang

16.1 Introduction 303

16.2 Adaptive Transmission Systems 305

16.2.1 Transmit Antenna Selection 305

16.2.2 Opportunistic Relaying 306

16.3 The Impact of Outdated CSI 307

16.3.1 Modeling Outdated CSI 307

16.3.2 Performance Impact 308

16.4 Classical Channel Prediction 309

16.4.1 Autoregressive Models 310

16.4.2 Parametric Models 311

16.5 NN-Based Prediction Schemes 313

16.5.1 The RNN Architecture 313

16.5.2 Flat-Fading SISO Prediction 314

16.5.2.1 Channel Gain Prediction with a Complex-Valued RNN 314

16.5.2.2 Channel Gain Prediction with a Real-Valued RNN 315

16.5.2.3 Channel Envelope Prediction 315

16.5.2.4 Multi-Step Prediction 316

16.5.3 Flat-Fading MIMO Prediction 316

16.5.3.1 Channel Gain Prediction 317

16.5.3.2 Channel Envelope Prediction 317

16.5.4 Frequency-Selective MIMO Prediction 317

16.5.5 Prediction-Assisted MIMO-OFDM 319

16.5.6 Performance and Complexity 320

16.5.6.1 Computational Complexity 320

16.5.6.2 Performance 321

16.6 Summary 323

Bibliography 323

Part III Network Intelligence and Adaptive System Optimization 327

17 Machine Learning for Digital Front-End: a Comprehensive Overview 329
Pere L. Gilabert, David López-Bueno, Thi Quynh Anh Pham, and Gabriel Montoro

17.1 Motivation and Background 329

17.2 Overview of CFR and DPD 331

17.2.1 Crest Factor Reduction Techniques 331

17.2.2 Power Amplifier Behavioral Modeling 334

17.2.3 Closed-Loop Digital Predistortion Linearization 335

17.2.4 Regularization 337

17.2.4.1 Ridge Regression or Tikhonov 𝓁₂ Regularization 338

17.2.4.2 LASSO or 𝓁₁ Regularization 339

17.2.4.3 Elastic Net 340

17.3 Dimensionality Reduction and ML 341

17.3.1 Introduction 341

17.3.2 Dimensionality Reduction Applied to DPD Linearization 343

17.3.3 Greedy Feature-Selection Algorithm: OMP 345

17.3.4 Principal Component Analysis 345

17.3.5 Partial Least Squares 348

17.4 Nonlinear Neural Network Approaches 350

17.4.1 Introduction to ANN Topologies 350

17.4.2 Design Considerations for Digital Linearization and RF Impairment Correction 353

17.4.2.1 ANN Architectures for Single-Antenna DPD 354

17.4.2.2 ANN Architectures for MIMO DPD, I/Q Imbalances, and DC Offset Correction 355

17.4.2.3 ANN Training and Parameter Extraction Procedure 357

17.4.2.4 Validation Methodologies and Key Performance Index 361

17.4.3 ANN for CFR: Design and Key Performance Index 364

17.4.3.1 SLM and PTS 364

17.4.3.2 Tone Injection 365

17.4.3.3 ACE 366

17.4.3.4 Clipping and Filtering 368

17.5 Support Vector Regression Approaches 368

17.6 Further Discussion and Conclusions 373

Bibliography 374

18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 383
Alexios Balatsoukas-Stimming

18.1 Nonlinear Self-Interference Models 384

18.1.1 Nonlinear Self-Interference Model 385

18.2 Digital Self-Interference Cancellation 386

18.2.1 Linear Cancellation 386

18.2.2 Polynomial Nonlinear Cancellation 387

18.2.3 Neural Network Nonlinear Cancellation 387

18.2.4 Computational Complexity 389

18.2.4.1 Linear Cancellation 389

18.2.4.2 Polynomial Nonlinear Cancellation 390

18.2.4.3 Neural Network Nonlinear Cancellation 390

18.3 Experimental Results 391

18.3.1 Experimental Setup 391

18.3.2 Self-Interference Cancellation Results 391

18.3.3 Computational Complexity 392

18.4 Conclusions 393

18.4.1 Open Problems 394

Bibliography 395

19 Machine Learning for Context-Aware Cross-Layer Optimization 397
Yang Yang, Zening Liu, Shuang Zhao, Ziyu Shao, and Kunlun Wang

19.1 Introduction 397

19.2 System Model 399

19.3 Problem Formulation and Analytical Framework 402

19.3.1 Fog-Enabled Multi-Tier Operations Scheduling (FEMOS) Algorithm 403

19.3.2 Theoretical and Numerical Analysis 405

19.3.2.1 Theoretical Analysis 405

19.3.2.2 Numerical Analysis 406

19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409

19.4.1 System Model 409

19.4.2 Theoretical Analysis 411

19.4.3 Numerical Analysis 413

19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413

19.5.1 System Model and Problem Formulation 413

19.5.2 COUS Algorithm 416

19.5.3 Performance Evaluation 418

19.6 Conclusion 420

Bibliography 421

20 Physical-Layer Location Verification by Machine Learning 425
Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto

20.1 IRLV by Wireless Channel Features 427

20.1.1 Optimal Test 428

20.2 ML Classification for IRLV 428

20.2.1 Neural Networks 429

20.2.2 Support Vector Machines 430

20.2.3 ML Classification Optimality 431

20.3 Learning Phase Convergence 431

20.3.1 Fundamental Learning Theorem 431

20.3.2 Simulation Results 432

20.4 Experimental Results 433

20.5 Conclusions 437

Bibliography 437

21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439
M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar

21.1 Introduction 439

21.2 System Model 441

21.2.1 Multi-Cell Network Model 441

21.2.2 Single-Cell Network Model with D2D Communication 442

21.2.3 Action Space 443

21.3 Problem Formulation 443

21.3.1 Cache Hit Rate 443

21.3.2 Transmission Delay 444

21.4 Deep Actor-Critic Framework for Content Caching 446

21.5 Application to the Multi-Cell Network 448

21.5.1 Experimental Settings 448

21.5.2 Simulation Setup 448

21.5.3 Simulation Results 449

21.5.3.1 Cache Hit Rate 449

21.5.3.2 Transmission Delay 450

21.5.3.3 Time-Varying Scenario 451

21.6 Application to the Single-Cell Network with D2D Communications 452

21.6.1 Experimental Settings 452

21.6.2 Simulation Setup 452

21.6.3 Simulation Results 453

21.6.3.1 Cache Hit Rate 453

21.6.3.2 Transmission Delay 454

21.7 Conclusion 454

Bibliography 455

Index 459

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