Artificial Intelligence and Quantum Computing for Advanced Wireless Networks. Savo G. Glisic
Table of Contents
1 Cover
4 Preface
5 Part I: Artificial Intelligence 1 Introduction 1.1 Motivation 1.2 Book Structure References 2 Machine Learning Algorithms 2.1 Fundamentals 2.2 ML Algorithm Analysis References 3 Artificial Neural Networks 3.1 Multi‐layer Feedforward Neural Networks 3.2 FIR Architecture 3.3 Time Series Prediction 3.4 Recurrent Neural Networks 3.5 Cellular Neural Networks (CeNN) 3.6 Convolutional Neural Network (CoNN) References 4 Explainable Neural Networks 4.1 Explainability Methods 4.2 Relevance Propagation in ANN 4.3 Rule Extraction from LSTM Networks 4.4 Accuracy and Interpretability References 5 Graph Neural Networks 5.1 Concept of Graph Neural Network (GNN) 5.2 Categorization and Modeling of GNN 5.3 Complexity of NN Appendix 5.A Notes on Graph Laplacian Appendix 5.B Graph Fourier Transform References 6 Learning Equilibria and Games 6.1 Learning in Games 6.2 Online Learning of Nash Equilibria in Congestion Games 6.3 Minority Games 6.4 Nash Q‐Learning 6.5 Routing Games 6.6 Routing with Edge Priorities References 7 AI Algorithms in Networks 7.1 Review of AI‐Based Algorithms in Networks 7.2 ML for Caching in Small Cell Networks 7.3 Q‐Learning‐Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks 7.4 ML for Self‐Organizing Cellular Networks 7.5 RL‐Based Caching 7.6 Big Data Analytics in Wireless Networks 7.7 Graph Neural Networks 7.8 DRL for Multioperator Network Slicing 7.9 Deep Q‐Learning for Latency‐Limited Network Virtualization 7.10 Multi‐Armed Bandit Estimator (MBE) 7.11 Network Representation Learning References
6
Part II: Quantum Computing
8 Fundamentals of Quantum Communications
8.1 Introduction
8.2 Quantum Gates and Quantum Computing
8.3 Quantum Fourier Transform (QFT)
References
9 Quantum Channel Information Theory
9.1 Communication Over a 
