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El. knyga: Machine Learning for Future Wireless Communications [Wiley Online]

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  • Formatas: 496 pages
  • Serija: IEEE Press
  • Išleidimo metai: 13-Feb-2020
  • Leidėjas: Wiley-IEEE Press
  • ISBN-10: 1119562309
  • ISBN-13: 9781119562306
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  • Wiley Online
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Machine Learning for Future Wireless CommunicationsDidesnis paveikslėlis
  • Formatas: 496 pages
  • Serija: IEEE Press
  • Išleidimo metai: 13-Feb-2020
  • Leidėjas: Wiley-IEEE Press
  • ISBN-10: 1119562309
  • ISBN-13: 9781119562306
Kitos knygos pagal šią temą:
Wiley Online E-booksMore info about Wiley Online
Partnerių interneto svetainė: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119562306

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.

 

List of Contributors
xv
Preface xxi
Part I Spectrum Intelligence and Adaptive Resource Management
1(60)
1 Machine Learning for Spectrum Access and Sharing
3(24)
Kobi Cohen
1.1 Introduction
3(1)
1.2 Online Learning Algorithms for Opportunistic Spectrum Access
4(5)
1.2.1 The Network Model
4(1)
1.2.2 Performance Measures of the Online Learning Algorithms
5(1)
1.2.3 The Objective
6(1)
1.2.4 Random and Deterministic Approaches
6(1)
1.2.5 The Adaptive Sequencing Rules Approach
7(1)
1.2.5.1 Structure of Transmission Epochs
7(1)
1.2.5.2 Selection Rule under the ASR Algorithm
8(1)
1.2.5.3 High-Level Pseudocode and Implementation Discussion
9(1)
1.3 Learning Algorithms for Channel Allocation
9(10)
1.3.1 The Network Model
10(1)
1.3.2 Distributed Learning, Game-Theoretic, and Matching Approaches
11(2)
1.3.3 Deep Reinforcement Learning for DSA
13(1)
1.3.3.1 Background on Q-learning and Deep Reinforcement Learning (DRL)
13(1)
1.3.4 Existing DRL-Based Methods for DSA
14(1)
1.3.5 Deep Q-Learning for Spectrum Access (DQSA) Algorithm
15(1)
1.3.5.1 Architecture of the DQN Used in the DQSA Algorithm
15(1)
1.3.5.2 Training the DQN and Online Spectrum Access
16(1)
1.3.5.3 Simulation Results
17(2)
1.4 Conclusions
19(8)
Acknowledgments
20(1)
Bibliography
20(7)
2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks
27(18)
Andres Kwasinski
Wenbo Wong
Fatemeh Shah Mohammadi
2.1 Use of Q-Learning for Cross-layer Resource Allocation
29(4)
2.2 Deep Q-Learning and Resource Allocation
33(3)
2.3 Cooperative Learning and Resource Allocation
36(6)
2.4 Conclusions
42(3)
Bibliography
43(2)
3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular
45(16)
Hadi Ghouch
Hossein Shokri-Ghadikolaei
Qobor Fodor
Carlo Fischione
Mikael Skoglund
3.1 Background and Motivation
45(4)
3.1.1 Review of Cellular Network Evolution
45(1)
3.1.2 Millimeter-Wave and Large-Scale Antenna Systems
46(1)
3.1.3 Review of Spectrum Sharing
47(1)
3.1.4 Model-Based vs. Data-Driven Approaches
48(1)
3.2 System Model and Problem Formulation
49(5)
3.2.1 Models
49(1)
3.2.1.1 Network Model
49(1)
3.2.1.2 Association Model
49(1)
3.2.1.3 Antenna and Channel Model
49(1)
3.2.1.4 Beamforming and Coordination Models
50(1)
3.2.1.5 Coordination Model
50(1)
3.2.2 Problem Formulation
51(1)
3.2.2.1 Rate Models
52(1)
3.2.3 Model-based Approach
52(1)
3.2.4 Data-driven Approach
53(1)
3.3 Hybrid Solution Approach
54(5)
3.3.1 Data-Driven Component
55(1)
3.3.2 Model-Based Component
56(2)
3.3.2.1 Illustrative Numerical Results
58(1)
3.3.3 Practical Considerations
58(1)
3.3.3.1 Implementing Training Frames
58(1)
3.3.3.2 Initializations
59(1)
3.3.3.3 Choice of the Penalty Matrix
59(1)
3.4 Conclusions and Discussions
59(2)
Appendix A Appendix for
Chapter 3
61(96)
A.1 Overview of Reinforcement Learning
61(2)
Bibliography
61(2)
4 Deep Learning-Based Coverage and Capacity Optimization
63(22)
Andrej Marinescu
Zhiyuan Jiang
Sheng Zhou
Luiz A. DaSilva
Zhlsheng Niu
4.1 Introduction
63(1)
4.2 Related Machine Learning Techniques for Autonomous Network Managements
64(3)
4.2.1 Reinforcement Learning and Neural Network
64(2)
4.2.2 Application to Mobile Network
66(1)
4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning
67(5)
4.3.1 Deep Reinforcement Learning Architecture
7(61)
4.3.2 Deep Q-Learning Preliminary
68(1)
4.3.3 Applications to BS Sleeping Control
68(1)
4.3.3.1 Action-Wise Experience Replay
69(1)
4.3.3.2 Adaptive Reward Scaling
70(1)
4.3.3.3 Environment Models and Dyna Integration
70(1)
4.3.3.4 DeepNap Algorithm Description
71(1)
4.3.4 Experiments
71(1)
4.3.4.1 Algorithm Comparisons
71(1)
4.3.5 Summary
72(1)
4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach
72(9)
4.4.1 Multi-Agent System Architecture
73(2)
4.4.1.1 Cell Agent Architecture
75(1)
4.4.2 Application to Fractional Frequency Reuse
75(1)
4.4.3 Scenario Implementation
76(1)
4.4.3.1 Cell Agent Neural Network
76(2)
4.4.4 Evaluation
78(1)
4.4.4.1 Neural Network Performance
78(1)
4.4.4.2 Multi-Agent System Performance
79(2)
4.4.5 Summary
81(1)
4.5 Conclusions
81(4)
Bibliography
82(3)
5 Machine Learning for Optimal Resource Allocation
85(20)
Marius Pesavento
Florian Bahlke
5.1 Introduction and Motivation
85(3)
5.1.1 Network Capacity and Densification
86(1)
5.1.2 Decentralized Resource Minimization
87(1)
5.1.3 Overview
88(1)
5.2 System Model
88(2)
5.2.1 Heterogeneous Wireless Networks
88(1)
5.2.2 Load Balancing
89(1)
5.3 Resource Minimization Approaches
90(6)
5.3.1 Optimized Allocation
91(1)
5.3.2 Feature Selection and Training
91(2)
5.3.3 Range Expansion Optimization
93(1)
5.3.4 Range Expansion Classifier Training
94(1)
5.3.5 Multi-Class Classification
94(2)
5.4 Numerical Results
96(3)
5.5 Concluding Remarks
99(6)
Bibliography
100(5)
6 Machine Learning in Energy Efficiency Optimization
105(14)
Muhammad Ali Imran
Ana Flavia dos Reis
Glauber Brante
Paulo Valente Klaine
Richard Demo Souza
6.1 Self-Organizing Wireless Networks
106(4)
6.2 Traffic Prediction and Machine Learning
110(1)
6.3 Cognitive Radio and Machine Learning
111(1)
6.4 Future Trends and Challenges
112(2)
6.4.1 Deep Learning
112(1)
6.4.2 Positioning of Unmanned Aerial Vehicles
113(1)
6.4.3 Learn-to-Optimize Approaches
113(1)
6.4.4 Some Challenges
114(1)
6.5 Conclusions
114(5)
Bibliography
114(5)
7 Deep Learning Based Traffic and Mobility Prediction
119(18)
Honggang Zhang
Yuxiu Hua
Chujie Wang
Rongpeng Li
Zhifeng Zhao
7.1 Introduction
119(1)
7.2 Related Work
120(2)
7.2.1 Traffic Prediction
120(1)
7.2.2 Mobility Prediction
121(1)
7.3 Mathematical Background
122(2)
7.4 ANN-Based Models for Traffic and Mobility Prediction
124(9)
7.4.1 ANN for Traffic Prediction
124(1)
7.4.1.1 Long Short-Term Memory Network Solution
124(1)
7.4.1.2 Random Connectivity Long Short-Term Memory Network Solution
125(3)
7.4.2 ANN for Mobility Prediction
128(1)
7.4.2.1 Basic LSTM Network for Mobility Prediction
128(2)
7.4.2.2 Spatial-Information-Assisted LSTM-Based Framework of Individual Mobility Prediction
130(1)
7.4.2.3 Spatial-Information-Assisted LSTM-Based Framework of Group Mobility Prediction
131(2)
7.5 Conclusion
133(4)
Bibliography
134(3)
8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing
137(20)
Benjamin Sliwa
Robert Falkenberg
Christian Wietfeld
8.1 Mobile Crowdsensing
137(3)
8.1.1 Applications and Requirements
138(1)
8.1.2 Anticipatory Data Transmission
139(1)
8.2 ML-Based Context-Aware Data Transmission
140(8)
8.2.1 Groundwork: Channel-aware Transmission
140(2)
8.2.2 Groundwork: Predictive CAT
142(2)
8.2.3 ML-based CAT
144(2)
8.2.4 ML-based pCAT
146(2)
8.3 Methodology for Real-World Performance Evaluation
148(1)
8.3.1 Evaluation Scenario
148(1)
8.3.2 Power Consumption Analysis
148(1)
8.4 Results of the Real-World Performance Evaluation
149(3)
8.4.1 Statistical Properties of the Network Quality Indicators
149(1)
8.4.2 Comparison of the Transmission Schemes
149(2)
8.4.3 Summary
151(1)
8.5 Conclusion
152(5)
Acknowledgments
154(1)
Bibliography
154(3)
Part II Transmission Intelligence and Adaptive Baseband Processing
157(170)
9 Machine Learning-Based Adaptive Modulation and Coding Desian
1(180)
Lin Zhang
Zhiqiang Wu
9.1 Introduction and Motivation
159(3)
9.1.1 Overview of ML-Assisted AMC
160(1)
9.1.2 MCS Schemes Specified by IEEE 802.11n
161(1)
9.2 SL-Assisted AMC
162(10)
9.2.1 k-NN-Assisted AMC
162(1)
9.2.1.1 Algorithm for A-NN-Assisted AMC
163(1)
9.2.2 Performance Analysis of A-NN-Assisted AMC System
164(2)
9.2.3 SVM-Assisted AMC
166(1)
9.2.3.1 SVM Algorithm
166(4)
9.2.3.2 Simulation and Results
170(2)
9.3 RL-Assisted AMC
172(6)
9.3.1 Markov Decision Process
172(1)
9.3.2 Solution for the Markov Decision
173(1)
9.3.3 Actions, States, and Rewards
174(1)
9.3.4 Performance Analysis and Simulations
175(3)
9.4 Further Discussion and Conclusions
178(3)
Bibliography
178(3)
10 Machine Learning-Based Nonlinear MIMO Detector
181(16)
Song-Nam Hong
Seonho Kim
10.1 Introduction
181(1)
10.2 A Multihop MIMO Channel Model
182(2)
10.3 Supervised-Learning-based MIMO Detector
184(604)
10.3.1 Non-Parametric Learning
184(1)
10.3.2 Parametric Learning
185(3)
10.4 Low-Complexity SL (LCSL) Detector
188(3)
10.5 Numerical Results
191(2)
10.6 Conclusions
193(4)
Bibliography
193(4)
11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach
197(16)
Daniyal Amir Awan
Renato Luis Garrido Cavalcante
Masahario Yukawa
Slawomir Stanczak
11.1 Introduction
197(1)
11.2 Preliminaries
198(2)
11.2.1 Reproducing Kernel Hilbert Spaces
198(1)
11.2.2 Sum Spaces of Reproducing Kernel Hilbert Spaces
199(1)
11.3 System Model
200(3)
11.3.1 Symbol Detection in Multiuser Environments
201(1)
11.3.2 Detection of Complex-Valued Symbols in Real Hilbert Spaces
202(1)
11.4 The Proposed Learning Algorithm
203(4)
11.4.1 The Canonical Iteration
203(1)
11.4.2 Practical Issues
204(1)
11.4.3 Online Dictionary Learning
205(1)
11.4.3.1 Dictionary for the Linear Component
206(1)
11.4.3.2 Dictionary for the Gaussian Component
206(1)
11.4.4 The Online Learning Algorithm
206(1)
11.5 Simulation
207(1)
11.6 Conclusion
208(5)
Appendix A Derivation of the Sparsification Metric and the Projections onto the Subspace Spanned by the Nonlinear Dictionary
210(1)
Bibliography
211(2)
12 Machine Learning for Joint Channel Equalization and Signal Detection
213(30)
Lin Zhang
Lie-Liang Yang
12.1 Introduction
213(1)
12.2 Overview of Neural Network-Based Channel Equalization
214(5)
12.2.1 Multilayer Perceptron-Based Equalizers
215(1)
12.2.2 Functional Link Artificial Neutral Network-Based Equalizers
215(1)
12.2.3 Radial Basis Function-Based Equalizers
216(1)
12.2.4 Recurrent Neural Networks-Based Equalizers
216(1)
12.2.5 Self-Constructing Recurrent Fuzzy Neural Network-Based Equalizers
217(1)
12.2.6 Deep-Learning-Based Equalizers
217(1)
12.2.7 Extreme Learning Machine-Based Equalizers
218(1)
12.2.8 SVM- and GPR-Based Equalizers
218(1)
12.3 Principles of Equalization and Detection
219(4)
12.4 NN-Based Equalization and Detection
223(9)
12.4.1 Multilayer Perceptron Model
223(1)
12.4.1.1 Generalized Multilayer Perceptron Structure
224(1)
12.4.1.2 Gradient Descent Algorithm
225(1)
12.4.1.3 Forward and Backward Propagation
226(1)
12.4.2 Deep-Learning Neural Network-Based Equalizers
227(1)
12.4.2.1 System Model and Network Structure
227(1)
12.4.2.2 Network Training
228(1)
12.4.3 Convolutional Neural Network-Based Equalizers
229(2)
12.4.4 Recurrent Neural Network-Based Equalizers
231(1)
12.5 Performance of OFDM Systems With Neural Network-Based Equalization
232(4)
12.5.1 System Model and Network Structure
232(1)
12.5.2 DNN and CNN Network Structure
233(1)
12.5.3 Offline Training and Online Deployment
234(1)
12.5.4 Simulation Results and Analyses
235(1)
12.6 Conclusions and Discussion
236(7)
Bibliography
237(6)
13 Neural Networks for Signal Intelligence: Theory and Practice
243(22)
Jithin Jagannath
Nicholas Polosky
Anu Jagannath
Francesco Restuccia
Tommaso Melodia
13.1 Introduction
243(1)
13.2 Overview of Artificial Neural Networks
244(4)
13.2.1 Feedforward Neural Networks
244(3)
13.2.2 Convolutional Neural Networks
247(1)
13.3 Neural Networks for Signal Intelligence
248(7)
13.3.1 Modulation Classification
249(3)
13.3.2 Wireless Interference Classification
252(3)
13.4 Neural Networks for Spectrum Sensing
255(4)
13.4.1 Existing Work
256(1)
13.4.2 Background on System-on-Chip Computer Architecture
256(1)
13.4.3 A Design Framework for Real-Time RF Deep Learning
257(1)
13.4.3.1 High-Level Synthesis
257(1)
13.4.3.2 Design Steps
258(1)
13.5 Open Problems
259(1)
13.5.1 Lack of Large-Scale Wireless Signal Datasets
259(1)
13.5.2 Choice of I/Q Data Representation Format
259(1)
13.5.3 Choice of Learning Model and Architecture
260(1)
13.6 Conclusion
260(5)
Bibliography
260(5)
14 Channel Coding with Deep Learning: An Overview
265(22)
Shugong Xu
14.1 Overview of Channel Coding and Deep Learning
265(3)
14.1.1 Channel Coding
265(1)
14.1.2 Deep Learning
266(2)
14.2 DNNs for Channel Coding
268(9)
14.2.1 Using DNNs to Decode Directly
269(2)
14.2.2 Scaling DL Method
271(1)
14.2.3 DNNs for Joint Equalization and Channel Decoding
272(2)
14.2.4 A Unified Method to Decode Multiple Codes
274(2)
14.2.5 Summary
276(1)
14.3 CNNs for Decoding
277(2)
14.3.1 Decoding by Eliminating Correlated Channel Noise
277(2)
14.3.1.1 BP-CNN Reduces Decoding BER
279(1)
14.3.1.2 Multiple Iterations Between CNN and BP Further Improve Performance
279(1)
14.3.2 Summary
279(1)
14.4 RNNs for Decoding
279(4)
14.4.1 Using RNNs to Decode Sequential Codes
279(2)
14.4.2 Improving the Standard BP Algorithm with RNNs
281(2)
14.4.3 Summary
283(1)
14.5 Conclusions
283(4)
Bibliography
283(4)
15 Deep Learning Techniques for Decoding Polar Codes
287(16)
Warren J. Gross
Nghia Doan
Elie Ngomseu Mambou
Seyyed Ali Hashemi
15.1 Motivation and Background
287(2)
15.2 Decoding of Polar Codes: An Overview
289(3)
15.2.1 Problem Formulation of Polar Codes
289(1)
15.2.2 Successive-Cancellation Decoding
290(1)
15.2.3 Successive-Cancellation List Decoding
291(1)
15.2.4 Belief Propagation Decoding
291(1)
15.3 DL-Based Decoding for Polar Codes
292(7)
15.3.1 Off-the-Shelf DL Decoders for Polar Codes
292(1)
15.3.2 DL-Aided Decoders for Polar Codes
293(1)
15.3.2.1 Neural Belief Propagation Decoders
293(2)
15.3.2.2 Joint Decoder and Noise Estimator
295(1)
15.3.3 Evaluation
296(3)
15.4 Conclusions
299(4)
Bibliography
299(4)
16 Neural Network-Based Wireless Channel Prediction
303(24)
Wei Jiang
Hans Dieter Schorten
Ji-ying Xiang
16.1 Introduction
303(2)
16.2 Adaptive Transmission Systems
305(2)
16.2.1 Transmit Antenna Selection
305(1)
16.2.2 Opportunistic Relaying
306(1)
16.3 The Impact of Outdated CSI
307(2)
16.3.1 Modeling Outdated CSI
307(1)
16.3.2 Performance Impact
308(1)
16.4 Classical Channel Prediction
309(4)
16.4.1 Autoregressive Models
310(1)
16.4.2 Parametric Models
311(2)
16.5 NN-Based Prediction Schemes
313(10)
16.5.1 The RNN Architecture
313(1)
16.5.2 Flat-Fading SISO Prediction
314(1)
16.5.2.1 Channel Gain Prediction with a Complex-Valued RNN
314(1)
16.5.2.2 Channel Gain Prediction with a Real-Valued RNN
315(1)
16.5.2.3 Channel Envelope Prediction
315(1)
16.5.2.4 Multi-Step Prediction
316(1)
16.5.3 Flat-Fading MIMO Prediction
316(1)
16.5.3.1 Channel Gain Prediction
317(1)
16.5.3.2 Channel Envelope Prediction
317(1)
16.5.4 Frequency-Selective MIMO Prediction
317(2)
16.5.5 Prediction-Assisted MIMO-OFDM
319(1)
16.5.6 Performance and Complexity
320(1)
16.5.6.1 Computational Complexity
320(1)
16.5.6.2 Performance
321(2)
16.6 Summary
323(4)
Bibliography
323(4)
Part III Network Intelligence and Adaptive System Optimization
327(132)
17 Machine Learning for Digital Front-End: a Comprehensive Overview
329(54)
Pere L. Gilabert
David Lopez-Bueno
Thi Quynh Anh Pham
Gabriel Montoro
17.1 Motivation and Background
329(2)
17.2 Overview of CFR and DPD
331(10)
17.2.1 Crest Factor Reduction Techniques
331(3)
17.2.2 Power Amplifier Behavioral Modeling
334(1)
17.2.3 Closed-Loop Digital Predistortion Linearization
335(2)
17.2.4 Regularization
337(1)
17.2.4.1 Ridge Regression or Tikhonov 2 Regularization
338(1)
17.2.4.2 LASSO or 1x Regularization
339(1)
17.2.4.3 Elastic Net
340(1)
17.3 Dimensionality Reduction and ML
341(9)
17.3.1 Introduction
341(2)
17.3.2 Dimensionality Reduction Applied to DPD Linearization
343(2)
17.3.3 Greedy Feature-Selection Algorithm: OMP
345(1)
17.3.4 Principal Component Analysis
345(3)
17.3.5 Partial Least Squares
348(2)
17.4 Nonlinear Neural Network Approaches
350(18)
17.4.1 Introduction to ANN Topologies
350(3)
17.4.2 Design Considerations for Digital Linearization and RF Impairment Correction
353(1)
17.4.2.1 ANN Architectures for Single-Antenna DPD
354(1)
17.4.2.2 ANN Architectures for MIMO DPD, I/Q Imbalances, and DC Offset Correction
355(2)
17.4.2.3 ANN Training and Parameter Extraction Procedure
357(4)
17.4.2.4 Validation Methodologies and Key Performance Index
361(3)
17.4.3 ANN for CFR: Design and Key Performance Index
364(1)
17.4.3.1 SLM and PTS
364(1)
17.4.3.2 Tone Injection
365(1)
17.4.3.3 ACE
366(2)
17.4.3.4 Clipping and Filtering
368(1)
17.5 Support Vector Regression Approaches
368(5)
17.6 Further Discussion and Conclusions
373(10)
Bibliography
374(9)
18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation
383(14)
Alexios Balatsoukas-Stimming
18.1 Nonlinear Self-Interference Models
384(2)
18.1.1 Nonlinear Self-Interference Model
385(1)
18.2 Digital Self-Interference Cancellation
386(5)
18.2.1 Linear Cancellation
386(1)
18.2.2 Polynomial Nonlinear Cancellation
387(1)
18.2.3 Neural Network Nonlinear Cancellation
387(2)
18.2.4 Computational Complexity
389(1)
18.2.4.1 Linear Cancellation
389(1)
18.2.4.2 Polynomial Nonlinear Cancellation
390(1)
18.2.4.3 Neural Network Nonlinear Cancellation
390(1)
18.3 Experimental Results
391(2)
18.3.1 Experimental Setup
391(1)
18.3.2 Self-Interference Cancellation Results
391(1)
18.3.3 Computational Complexity
392(1)
18.4 Conclusions
393(4)
18.4.1 Open Problems
394(1)
Bibliography
395(2)
19 Machine Learning for Context-Aware Cross-Layer Optimization
397(28)
Yang Yang
Zening Liu
Shuang Zhao
Ziyu Shao
Kunlun Wang
19.1 Introduction
397(2)
19.2 System Model
399(3)
19.3 Problem Formulation and Analytical Framework
402(7)
19.3.1 Fog-Enabled Multi-Tier Operations Scheduling (FEMOS) Algorithm
403(2)
19.3.2 Theoretical and Numerical Analysis
405(1)
19.3.2.1 Theoretical Analysis
405(1)
19.3.2.2 Numerical Analysis
406(3)
19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm
409(64)
19.4.1 System Model
409(2)
19.4.2 Theoretical Analysis
411(2)
19.4.3 Numerical Analysis
413(1)
19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks
413(1)
19.5.1 System Model and Problem Formulation
413(3)
19.5.2 COUS Algorithm
416(2)
19.5.3 Performance Evaluation
418(2)
19.6 Conclusion
420(5)
Bibliography
421(4)
20 Physical-Layer Location Verification by Machine Learning
425(14)
Stefano Tomasin
Alessondro Brighente
Francesco Formaggio
Gabriele Ruvoletto
20.1 IRLV by Wireless Channel Features
427(1)
20.1.1 Optimal Test
428(1)
20.2 ML Classification for IRLV
428(3)
20.2.1 Neural Networks
429(1)
20.2.2 Support Vector Machines
430(1)
20.2.3 ML Classification Optimality
431(1)
20.3 Learning Phase Convergence
431(2)
20.3.1 Fundamental Learning Theorem
431(1)
20.3.2 Simulation Results
432(1)
20.4 Experimental Results
433(4)
20.5 Conclusions
437(2)
Bibliography
437(2)
21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching
439(20)
M. Cenk Gursoy
Chen Zhong
Senem Vellpasalar
21.1 Introduction
439(2)
21.2 System Model
441(2)
21.2.1 Multi-Cell Network Model
441(1)
21.2.2 Single-Cell Network Model with D2D Communication
442(1)
21.2.3 Action Space
443(1)
21.3 Problem Formulation
443(3)
21.3.1 Cache Hit Rate
443(1)
21.3.2 Transmission Delay
444(2)
21.4 Deep Actor-Critic Framework for Content Caching
446(2)
21.5 Application to the Multi-Cell Network
448(4)
21.5.1 Experimental Settings
448(1)
21.5.2 Simulation Setup
448(1)
21.5.3 Simulation Results
449(1)
21.5.3.1 Cache Hit Rate
449(1)
21.5.3.2 Transmission Delay
450(1)
21.5.3.3 Time-Varying Scenario
451(1)
21.6 Application to the Single-Cell Network with D2D Communications
452(2)
21.6.1 Experimental Settings
452(1)
21.6.2 Simulation Setup
452(1)
21.6.3 Simulation Results
453(1)
21.6.3.1 Cache Hit Rate
453(1)
21.6.3.2 Transmission Delay
454(1)
21.7 Conclusion
454(5)
Bibliography
455(4)
Index 459
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.
Pastovi nuoroda: https://www.kriso.lt/db/9781119562306_pe.html
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