Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Machine learning for future wireless communications / / edited by Fa-Long Luo
| Machine learning for future wireless communications / / edited by Fa-Long Luo |
| Autore | Luo Fa-Long |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley-IEEE, , 2020 |
| Descrizione fisica | 1 online resource (493 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning |
| ISBN |
1-119-56231-7
1-119-56227-9 1-119-56230-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 LearninǵôBased 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 LearninǵôBased 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 LearninǵôBased 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 MachinéôBased 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 NetworḱôBased 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 ]]>̧ô 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. |
| Record Nr. | UNINA-9910555156703321 |
Luo Fa-Long
|
||
| Hoboken, New Jersey : , : Wiley-IEEE, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning for future wireless communications / / edited by Fa-Long Luo
| Machine learning for future wireless communications / / edited by Fa-Long Luo |
| Autore | Luo Fa-Long |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley-IEEE, , 2020 |
| Descrizione fisica | 1 online resource (493 pages) |
| Disciplina | 006.31 |
| Soggetto topico | Machine learning |
| ISBN |
1-119-56231-7
1-119-56227-9 1-119-56230-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 LearninǵôBased 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 LearninǵôBased 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 LearninǵôBased 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 MachinéôBased 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 NetworḱôBased 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 ]]>̧ô 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. |
| Record Nr. | UNINA-9910813591503321 |
|
Luo Fa-Long
|
||
| Hoboken, New Jersey : , : Wiley-IEEE, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||