About the Authors xi Preface xiii Acknowledgments xv 1 Introduction to 5G and Beyond Network 1 1.1 5G and Beyond System Requirements 1 1.1.1 Technical Challenges 2 1.2 Enabling Technologies 3 1.2.1 5G New Radio 3 1.2.

1.1 Non-orthogonal Multiple Access (NOMA) 3 1.2.1.2 Channel Codes 5 1.2.1.3 Massive MIMO 5 1.

2.1.4 Other 5G NR Techniques 6 1.2.2 Mobile Edge Computing (MEC) 6 1.2.3 Hybrid and Heterogeneous Communication Architecture for Pervasive IoTs 7 1.3 Book Outline 8 2 5G Wireless Networks with Underlaid D2D Communications 11 2.

1 Background 11 2.1.1 MU-MIMO 11 2.1.2 D2D Communication 11 2.1.3 MU-MIMO and D2D in 5G 12 2.2 NOMA-Aided Network with Underlaid D2D 12 2.

3 NOMA with SIC and Problem Formation 14 2.3.1 NOMA with SIC 14 2.3.2 Problem Formation 15 2.4 Precoding and User Grouping Algorithm 15 2.4.1 Zero-Forcing Beamforming 16 2.

4.1.1 First ZF Precoding 16 2.4.1.2 Second ZF Precoding 16 2.4.2 User Grouping and Optimal Power Allocation 16 2.

4.2.1 First ZF Precoding 17 2.4.2.2 Second ZF Precoding 18 2.5 Numerical Results 18 2.6 Summary 19 3 5G NOMA-Enabled Wireless Networks 21 3.

1 Background 21 3.2 Error Propagation in NOMA 22 3.3 SIC and Problem Formulation 22 3.3.1 SIC with Error Propagation 23 3.3.2 Problem Formation 24 3.4 Precoding and Power Allocation 25 3.

4.1 Precoding Design 25 3.4.2 Case Studies for Power Allocation 26 3.4.2.1 Case I 26 3.4.

2.2 Case II 27 3.5 Numerical Results 27 3.6 Summary 30 4 NOMA in Relay and IoT for 5G Wireless Networks 31 4.1 Outage Probability Study in a NOMA Relay System 31 4.1.1 Background 31 4.1.

2 System Model 32 4.1.2.1 NOMA Cooperative Scheme 32 4.1.2.2 NOMA TDMA Scheme 34 4.1.

3 Outage Probability Analysis 35 4.1.3.1 Outage Probability in NOMA Cooperative Scheme 35 4.1.4 Outage Probability in NOMA TDMA Scheme 36 4.1.5 Outage Probability with Error Propagation in SIC 37 4.

1.5.1 Outage Probability in NOMA Cooperative Scheme with EP 38 4.1.5.2 Outage Probability in NOMA TDMA Scheme with EP 38 4.1.6 Numerical Results 39 4.

2 NOMA in a mmWave-Based IoT Wireless System with SWIPT 41 4.2.1 Introduction 41 4.2.2 System Model 41 4.2.2.1 Phase 1 Transmission 42 4.

2.2.2 Phase 2 Transmission 44 4.2.3 Outage Analysis 45 4.2.3.1 UE 1 Outage Probability 45 4.

2.3.2 UE 2 Outage Probability 45 4.2.3.3 Outage at High SNR 47 4.2.3.

4 Diversity Analysis for UE 2 47 4.2.4 Numerical Results 47 4.2.5 Summary 48 5 Robust Beamforming in NOMA Cognitive Radio Networks: Bounded CSI 51 5.1 Background 51 5.1.1 RelatedWork and Motivation 52 5.

1.1.1 Linear EH Model 52 5.1.1.2 Non-linear EH Model 53 5.1.2 Contributions 53 5.

2 System and Energy Harvesting Models 54 5.2.1 System Model 54 5.2.2 Non-linear EH Model 55 5.2.3 Bounded CSI Error Model 55 5.2.

3.1 NOMA Transmission 56 5.3 Power Minimization-Based Problem Formulation 56 5.3.1 Problem Formulation 57 5.3.2 Matrix Decomposition 59 5.4 Maximum Harvested Energy Problem Formulation 60 5.

4.1 Complexity Analysis 61 5.5 Numerical Results 62 5.5.1 Power Minimization Problem 62 5.5.2 Energy Harvesting Maximization Problem 64 5.6 Summary 67 6 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI 69 6.

1 Gaussian CSI Error Model 69 6.2 Power Minimization-Based Problem Formulation 69 6.2.1 Bernstein-Type Inequality I 70 6.2.2 Bernstein-Type Inequality II 71 6.3 Maximum Harvested Energy Problem Formulation 72 6.3.

1 Complexity Analysis 73 6.4 Numerical Results 73 6.4.1 Power Minimization Problem 74 6.4.2 Energy Harvesting Maximization Problem 76 6.5 Summary 79 7 Mobile Edge Computing in 5G Wireless Networks 81 7.1 Background 81 7.

2 System Model 82 7.2.1 Data Offloading 83 7.2.2 Local Computing 83 7.3 Problem Formulation 83 7.3.1 Update pk, tk, and fk 85 7.

3.2 Update Lagrange Multipliers 86 7.3.3 Update Auxiliary Variables 86 7.3.4 Complexity Analysis 87 7.4 Numerical Results 87 7.5 Summary 90 8 Toward Green MEC Offloading with Security Enhancement 91 8.

1 Background 91 8.2 System Model 92 8.2.1 Secure Offloading 92 8.2.2 Local Computing 93 8.2.3 Receiving Computed Results 93 8.

2.4 Computation Efficiency in MEC Systems 93 8.3 Computation Efficiency Maximization with Active Eavesdropper 94 8.3.1 SCA-Based Optimization Algorithm 94 8.3.2 Objective Function 95 8.3.

3 Proposed Solution to P4 with given (λΚ, βΚ) 96 8.3.4 Update (λΚ, βΚ) 97 8.4 Numerical Results 97 8.5 Summary 100 9 Wireless Systems for Distributed Machine Learning 101 9.1 Background 101 9.2 System Model 102 9.2.

1 FL Model Update 102 9.2.2 Gradient Quantization 104 9.2.3 Gradient Sparsification 104 9.3 FL Model Update with Adaptive NOMA Transmission 104 9.3.1 Uplink NOMA Transmission 104 9.

3.2 NOMA Scheduling 105 9.3.3 Adaptive Transmission 106 9.4 Scheduling and Power Optimization 107 9.4.1 Problem Formulation 107 9.5 Scheduling Algorithm and Power Allocation 108 9.

5.1 Scheduling Graph Construction 108 9.5.2 Optimal scheduling Pattern 109 9.5.3 Power Allocation 110 9.6 Numerical Results 111 9.7 Summary 114 10 Secure Spectrum Sharing with Machine Learning: An Overview 115 10.

1 Background 115 10.1.1 SS: A Brief History 116 10.1.2 Security Issues in SS 118 10.2 ML-Based Methodologies for SS 119 10.2.1 ML-Based CRN 119 10.

2.1.1 Spectrum Sensing 120 10.2.1.2 Spectrum Selection 122 10.2.1.

3 Spectrum Access 123 10.2.1.4 Spectrum Handoff 125 10.2.2 Database-Assisted SS 125 10.2.2.

1 ML-Based EZ Optimization 126 10.2.2.2 Incumbent Detection 126 10.2.2.3 Channel Selection and Transaction 127 10.2.

3 ML-Based LTE-U/LTE-LAA 127 10.2.3.1 ML-Based LBT Methods 128 10.2.3.2 ML-Based Duty Cycle Methods 129 10.2.

3.3 Game-Theory-Based Methods 129 10.2.3.4 Distributed-Algorithm-Based Methods 130 10.2.4 Ambient Backscatter Networks 131 10.2.

4.1 Information Extraction 131 10.2.4.2 Operating Mode Selection and User Coordination 132 10.2.4.3 AmBC-CR Methods 133 10.

3 Summary 134 11 Secure Spectrum Sharing with Machine Learning: Methodologies 135 11.1 Security Concerns in SS 135 11.1.1 Primary User Emulation Attack 135 11.1.2 Spectrum Sensing Data Falsification Attack 135 11.1.3 Jamming Attacks 136 11.

1.4 Intercept/Eavesdrop 137 11.1.5 Privacy Issues in Database-Assisted SS Systems 137 11.2 ML-Assisted Secure SS 138 11.2.1 State-of-the-Art Methods of Defense Against PUE Attack 138 11.2.

1.1 ML-Based Detection Methods 138 11.2.1.2 Robust Detection Methods 140 11.2.1.3 ML-Based Attack Methods 141 11.

2.2 State-of-the-Art Methods of Defense Against SSDF Attack 142 11.2.2.1 Outlier Detection Methods 143 11.2.2.2 Reputation-Based Detection Methods 143 11.

2.2.3 SSDF and PUE Combination Attacks 144 11.2.3 State-of-the-Art Methods of Defense Against Jamming Attacks 144 11.2.3.1 ML-Based Anti-Jamming Methods 145 11.

2.3.2 Attacker Enhanced Anti-Jamming Methods 146 11.2.3.3 AmBC Empowered Anti-Jamming Methods 148 11.2.4 State-of-the-Art Methods of Defense Against Intercept/Eavesdrop 149 11.

2.4.1 RL-Based Anti-Eavesdropping Methods 149 11.2.5 State-of-the-Art ML-Based Privacy Protection Methods 150 11.2.5.1 Privacy Protection for PUs in SS Networks 150 11.

2.5.2 Privacy Protection for SUs in SS Networks 151 11.2.5.3 Privacy Protection for ML Algorithms 151 11.3 Summary 153 12 Open Issues and Future Directions for 5G and Beyond Wireless Networks 155 12.1 Joint Communication and Sensing 155 12.

2 Space-Air-Ground Communication 155 12.3 Semantic Communication 156 12.4 Data-Driven Communication System Design 156 Appendix A Proof of Theorem 5.1 157 Bibliography 161 Index 181.