roblem 62 5.5.2 Energy Harvesting Maximization Problem Summary 67 Robust Beamforming in NOMA Cognitive Radio Networks: Gaussian CSI 69 Gaussian CSI Error Model Power Minimization-Based Problem Formulation 6.2.1 Bernstein-Type Inequality I 6.2.2 Bernstein-Type Inequality II Maximum Harvested Energy Problem Formulation 6.3.1 Complexity Analysis Numerical Results 6.4.1 Power Minimization Problem 6.4.2 Energy Harvesting Maximization Problem Summary 79 Mobile Edge Computing in 5G Wireless Networks Background 81 System Model 7.2.1 Data Offloading 7.2.2 Local Computing Problem Formulation 7.3.1 Update Pk, tk, and fk 7.3.2 Update Lagrange Multipliers 7.3.3 Update Auxiliary Variables 7.3.4 Complexity Analysis Numerical Results Summary 90 Toward Green MEC Offloading with Security Enhancement Background 91 Contents System Model 8.2.1 Secure Offloading 8.2.2 Local Computing 8.2.3 Receiving Computed Results 8.2.4 Computation Efficiency in MEC Systems 93 Computation Efficiency Maximization with Active Eavesdropper 8.3.1 SCA-Based Optimization Algorithm 94 8.3.2 Objective Function 8.3.3 Proposed Solution to P4 with given (Ak, Bk) 8.3.4 Update (Ak, Bk) Numerical Results Summary Wireless Systems for Distributed Machine Learning Background System Model 9.2.1 FL Model Update 9.2.2 Gradient Quantization 9.2.3 Gradient Sparsification FL Model Update with Adaptive NOMA Transmission 9.3.1 Uplink NOMA Transmission 9.3.2 NOMA Scheduling 9.3.3 Adaptive Transmission Scheduling and Power Optimization 9.4.1 Problem Formulation Scheduling Algorithm and Power Allocation 9.5.1 Scheduling Graph Construction 108 9.5.2 Optimal scheduling Pattern 9.5.3 Power Allocation Numerical Results Summary 114 Secure Spectrum Sharing with Machine Learning: An Overview Background 115 10.1.1 SS: A Brief History 10.1.2 Security Issues in SS ML-Based Methodologies for SS 10.2.1 ML-Based CRN 10.2.1.1 Spectrum Sensing 10.2.1.2 Spectrum Selection 10.2.1.3 Spectrum Access 10.2.1.4 Spectrum Handoff 10.2.2 Database-Assisted SS 10.2.2.1 ML-Based EZ Optimization 10.2.2.2 