دانلود کتاب Applications of Machine Learning in Wireless Communications (Telecommunications)

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Cover Contents Foreword 1 Introduction of machine learning 1.1 Supervised learning 1.1.1 k-Nearest neighbours method 1.1.2 Decision tree 1.1.2.1 Classification and regression tree 1.1.2.2 Random forest 1.1.2.3 Gradient boosting decision tree 1.1.3 Perceptron 1.1.3.1 Support vector machine 1.1.3.2 Logistic regression 1.1.3.3 Multilayer perceptron and deep learning 1.1.4 Summary of supervised learning 1.2 Unsupervised learning 1.2.1 k-Means 1.2.2 Density-based spatial clustering of applications with noise 1.2.3 Clustering by fast search and find of density peaks 1.2.4 Relative core merge clustering algorithm 1.2.5 Gaussian mixture models and EM algorithm 1.2.5.1 The EM algorithm 1.2.5.2 The EM algorithm for GMM 1.2.6 Principal component analysis 1.2.7 Autoencoder 1.2.8 Summary of unsupervised learning 1.3 Reinforcement learning 1.3.1 Markov decision process 1.3.2 Model-based methods 1.3.3 Model-free methods 1.3.3.1 Monte Carlo methods 1.3.3.2 Temporal-difference learning 1.3.4 Deep reinforcement learning 1.3.4.1 Value function approximation 1.3.4.2 Policy gradient methods 1.3.5 Summary of reinforcement learning 1.4 Summary Acknowledgement References 2 Machine-learning-enabled channel modeling 2.1 Introduction 2.2 Propagation scenarios classification 2.2.1 Design of input vector 2.2.2 Training and adjustment 2.3 Machine-learning-based MPC clustering 2.3.1 KPowerMeans-based clustering 2.3.1.1 Clustering 2.3.1.2 Validation 2.3.1.3 Cluster pruning—ShapePrune 2.3.1.4 Development 2.3.2 Sparsity-based clustering 2.3.3 Kernel-power-density-based clustering 2.3.4 Time-cluster-spatial-lobe ( TCSL)-based clustering 2.3.4.1 TC clustering 2.3.4.2 SL clustering 2.3.5 Target-recognition-based clustering 2.3.6 Improved subtraction for cluster-centroid initialization 2.3.7 MR-DMS clustering 2.3.7.1 Cluster the MPCs 2.3.7.2 Obtaining the optimum cluster number 2.4 Automatic MPC tracking algorithms 2.4.1 MCD-based tracking 2.4.2 Two-way matching tracking 2.4.3 Kalman filter-based tracking 2.4.4 Extended Kalman filter-based parameters estimation and tracking 2.4.5 Probability-based tracking 2.5 Deep learning-based channel modeling approach 2.5.1 BP-based neural network for amplitude modeling 2.5.2 Development of neural-network-based channel modeling 2.5.3 RBF-based neural network for wireless channel modeling 2.5.4 Algorithm improvement based on physical interpretation 2.6 Conclusion References 3 Channel prediction based on machine-learning algorithms 3.1 Introduction 3.2 Channel measurements 3.3 Learning-based reconstruction algorithms 3.3.1 Batch algorithms 3.3.1.1 Support vector machine 3.3.1.2 Neural networks 3.3.1.3 Matrix completion 3.3.2 Online algorithms 3.3.2.1 APSM-based algorithm 3.3.2.2 Multi-kernel algorithm 3.4 Optimized sampling 3.4.1 Active learning 3.4.1.1 Query by committee 3.4.1.2 Side information 3.4.2 Channel prediction results with path-loss measurements 3.5 Conclusion References 4 Machine-learning-based channel estimation 4.1 Channel model 4.1.1 Channel input and output 4.2 Channel estimation in point-to-point systems 4.2.1 Estimation of frequency-selective channels 4.3 Deep-learning-based channel estimation 4.3.1 History of deep learning 4.3.2 Deep-learning-based channel estimator for orthogonal frequency division multiplexing ( OFDM) systems 4.3.3 Deep learning for massive MIMO CSI feedback 4.4 EM-based channel estimator 4.4.1 Basic principles of EM algorithm 4.4.2 An example of channel estimation with EM algorithm 4.5 Conclusion and open problems References 5 Signal identification in cognitive radios using machine learning 5.1 Signal identification in cognitive radios 5.2 Modulation classification via machine learning 5.2.1 Modulation classification in multipath fading channels via expectation– maximization 5.2.1.1 Problem statement 5.2.1.2 Modulation classification via EM EM-based modulation classifier 5.2.1.3 Numerical results 5.2.2 Continuous phase modulation classification in fading channels via Baum– Welch algorithm 5.2.2.1 Problem statement 5.2.2.2 Classification of CPM via BW 5.2.2.3 Numerical results 5.3 Specific emitter identification via machine learning 5.3.1 System model 5.3.1.1 Single-hop scenario 5.3.1.2 Relaying scenario 5.3.2 Feature extraction 5.3.2.1 Hilbert– Huang transform 5.3.2.2 Entropy and first-and second-order moments-based algorithm 5.3.2.3 Correlation-based algorithm 5.3.2.4 Fisher's discriminant ratio-based algorithm 5.3.3 Identification procedure via SVM 5.3.4 Numerical results 5.3.5 Conclusions References 6 Compressive sensing for wireless sensor networks 6.1 Sparse signal representation 6.1.1 Signal representation 6.1.2 Representation error 6.2 CS and signal recovery 6.2.1 CS model 6.2.2 Conditions for the equivalent sensing matrix 6.2.2.1 Null space property 6.2.2.2 Restricted isometry property 6.2.2.3 Mutual coherence 6.2.3 Numerical algorithms for sparse recovery 6.2.3.1 Convex optimization algorithms 6.2.3.2 Greedy pursuit algorithms 6.3 Optimized sensing matrix design for CS 6.3.1 Elad's method 6.3.2 Duarte-Carvajalino and Sapiro's method 6.3.3 Xu et al.' s method 6.3.4 Chen et al.' s method 6.4 CS-based WSNs 6.4.1 Robust data transmission 6.4.2 Compressive data gathering 6.4.2.1 WSNs with single hop communications 6.4.2.2 WSNs with multi-hop communications 6.4.3 Sparse events detection 6.4.4 Reduced-dimension multiple access 6.4.5 Localization 6.5 Summary References 7 Reinforcement learning-based channel sharing in wireless vehicular networks 7.1 Introduction 7.1.1 Motivation 7.1.2 Chapter organization 7.2 Connected vehicles architecture 7.2.1 Electronic control units 7.2.2 Automotive sensors 7.2.3 Intra-vehicle communications 7.2.4 Vehicular ad hoc networks 7.2.5 Network domains 7.2.6 Types of communication 7.3 Dedicated short range communication 7.3.1 IEEE 802.11p 7.3.2 WAVE Short Message Protocol 7.3.3 Control channel behaviour 7.3.4 Message types 7.4 The IEEE 802.11p medium access control 7.4.1 Distributed coordination function 7.4.2 Basic access mechanism 7.4.3 Binary exponential backoff 7.4.4 RTS/ CTS handshake 7.4.5 DCF for broadcasting 7.4.6 Enhanced distributed channel access 7.5 Network traffic congestion in wireless vehicular networks 7.5.1 Transmission power control 7.5.2 Transmission rate control 7.5.3 Adaptive backoff algorithms 7.6 Reinforcement learning-based channel access control 7.6.1 Review of learning channel access control protocols 7.6.2 Markov decision processes 7.6.3 Q-learning 7.7 Q-learning MAC protocol 7.7.1 The action selection dilemma 7.7.2 Convergence requirements 7.7.3 A priori approximate controller 7.7.4 Online controller augmentation 7.7.5 Implementation details 7.8 VANET simulation modelling 7.8.1 Network simulator 7.8.2 Mobility simulator 7.8.3 Implementation 7.9 Protocol performance 7.9.1 Simulation setup 7.9.2 Effect of increased network density 7.9.3 Effect of data rate 7.9.4 Effect of multi-hop 7.10 Conclusion References 8 Machine-learning-based perceptual video coding in wireless multimedia communications 8.1 Background 8.2 Literature review on perceptual video coding 8.2.1 Perceptual models 8.2.1.1 Manual identification 8.2.1.2 Automatic identification 8.2.2 Incorporation in video coding 8.2.2.1 Model-based approaches 8.2.2.2 Learning-based approaches 8.3 Minimizing perceptual distortion with the RTE method 8.3.1 Rate control implementation on HEVC-MSP 8.3.2 Optimization formulation on perceptual distortion 8.3.3 RTE method for solving the optimization formulation 8.3.4 Bit reallocation for maintaining optimization 8.4 Computational complexity analysis 8.4.1 Theoretical analysis 8.4.2 Numerical analysis 8.5 Experimental results on single image coding 8.5.1 Test and parameter settings 8.5.2 Assessment on rate–distortion performance 8.5.3 Assessment of BD-rate savings 8.5.4 Assessment of control accuracy 8.5.5 Generalization test 8.6 Experimental results on video coding 8.6.1 Experiment 8.6.1.1 Settings 8.6.1.2 Evaluation on R–D performance 8.6.1.3 Evaluation on RC accuracy 8.7 Conclusion References 9 Machine-learning-based saliency detection and its video decoding application in wireless multimedia communications 9.1 Introduction 9.2 Related work on video-saliency detection 9.2.1 Heuristic video-saliency detection 9.2.2 Data-driven video-saliency detection 9.3 Database and analysis 9.3.1 Database of eye tracking on raw videos 9.3.2 Analysis on our eye-tracking database 9.3.3 Observations from our eye-tracking database 9.4 HEVC features for saliency detection 9.4.1 Basic HEVC features 9.4.2 Temporal difference features in HEVC domain 9.4.3 Spatial difference features in HEVC domain 9.5 Machine-learning-based video-saliency detection 9.5.1 Training algorithm 9.5.2 Saliency detection 9.6 Experimental results 9.6.1 Setting on encoding and training 9.6.2 Analysis on parameter selection 9.6.3 Evaluation on our database 9.6.4 Evaluation on other databases 9.6.5 Evaluation on other work conditions 9.6.6 Effectiveness of single features and learning algorithm 9.7 Conclusion References 10 Deep learning for indoor localization based on bimodal CSI data 10.1 Introduction 10.2 Deep learning for indoor localization 10.2.1 Autoencoder neural network 10.2.2 Convolutional neural network 10.2.3 Long short-term memory 10.3 Preliminaries and hypotheses 10.3.1 Channel state information preliminaries 10.3.2 Distribution of amplitude and phase 10.3.3 Hypotheses 10.3.3.1 Hypothesis 1 10.3.3.2 Hypothesis 2 10.3.3.3 Hypothesis 3 10.4 The BiLoc system 10.4.1 BiLoc system architecture 10.4.2 Off-line training for bimodal fingerprint database 10.4.3 Online data fusion for position estimation 10.5 Experimental study 10.5.1 Test configuration 10.5.2 Accuracy of location estimation 10.5.3 2.4 versus 5 GHz 10.5.4 Impact of parameter 10.6 Future directions and challenges 10.6.1 New deep-learning methods for indoor localization 10.6.2 Sensor fusion for indoor localization using deep learning 10.6.3 Secure indoor localization using deep learning 10.7 Conclusions Acknowledgments References 11 Reinforcement-learning-based wireless resource allocation 11.1 Basics of stochastic approximation 11.1.1 Iterative algorithm 11.1.2 Stochastic fixed-point problem 11.2 Markov decision process: basic theory and applications 11.2.1 Basic components of MDP 11.2.2 Finite-horizon MDP 11.2.2.1 Case study: multi-carrier power allocation via finite-horizon MDP 11.2.3 Infinite-horizon MDP with discounted cost 11.2.3.1 Case study: multi-carrier power allocation with random packet arrival 11.2.4 Infinite-horizon MDP with average cost 11.2.4.1 Case study: multi-carrier power allocation with average cost 11.3 Reinforcement learning 11.3.1 Online solution via stochastic approximation 11.3.1.1 Case study: multi-carrier power allocation without channel statistics 11.3.2 Q-learning 11.3.2.1 Case study: multi-carrier power allocation via Q-learning 11.4 Summary and discussion References 12 learning-based power control in small-cell networks 12.1 Introduction 12.2 System model 12.2.1 System description 12.2.2 Effective capacity 12.2.3 Problem formulation 12.3 Noncooperative game theoretic solution 12.4 Q-learning algorithm 12.4 Q-learning algorithm 12.4.1 Stackelberg game framework 12.4.2 Q-learning 12.4.3 Q-learning procedure 12.4.3.1 Sparsely deployed scenario 12.4.3.2 Densely deployed scenario 12.4.3.3 Distributed Q-learning algorithm 12.4.4 The proposed BDb-WFQA based on NPCG 12.5 Simulation and analysis 12.5.1 Simulation for Q-learning based on Stackelberg game 12.5.2 Simulation for BDb-WFQA algorithm 12.6 Conclusion References 13 Data-driven vehicular mobility modeling and prediction 13.1 Introduction 13.2 Related work 13.3 Model 13.3.1 Data sets and preprocessing 13.3.2 Model motivation 13.3.3 Queue modeling 13.4 Performance derivation 13.4.1 Vehicular distribution 13.4.2 Average sojourn time 13.4.3 Average mobility length 13.5 Model validation 13.5.1 Time selection and area partition 13.5.1.1 Area partition 13.5.1.2 Observation period selection 13.5.2 Arrival rate validation 13.5.3 Vehicular distribution 13.5.4 Average sojourn time and mobility length 13.6 Applications of networking 13.6.1 RSU capacity decision 13.6.2 V2I and V2V combined performance analysis 13.7 Conclusions References Index Back Cover