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Machine Learning for Future Wireless Communications

Machine Learning for Future Wireless Communications

Autorzy
Wydawnictwo John Wiley and Sons Ltd
Data wydania 13/02/2020
Liczba stron 496
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
ISBN 9781119562252
Kategorie WAP Technologia bezprzewodowa, Machine learning
597.00 PLN (z VAT)
$148.35 / €134.87 / £121.06 /
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Opis książki

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.

Machine Learning for Future Wireless Communications

Spis treści

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 Flavia 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 Lopez-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 ?2 Regularization 338


17.2.4.2 LASSO or ?1 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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