Artificial intelligence and quantum computing for advanced wireless networks / Savo G. Glisic, Beatriz Lorenzo

Includes bibliographical references and index.

Preface, xiii -- Part I Artificial Intelligence, 1 -- 1 Introduction, 3 -- 1.1 Motivation, 3 -- 1.2 Book Structure, 5 -- 2 Machine Learning Algorithms, 17 -- 2.1 Fundamentals, 17 -- 2.2 ML Algorithm Analysis, 37 -- 3 Artificial Neural Networks, 55 -- 3.1 Multi-layer Feedforward Neural Networks, 55 -- 3.2 FIR Architecture, 60 -- 3.3 Time Series Prediction, 68 -- 3.4 Recurrent Neural Networks, 69 -- 3.5 Cellular Neural Networks (CeNN), 81 -- 3.6 Convolutional Neural Network (CoNN), 84 -- 4 Explainable Neural Networks, 97 -- 4.1 Explainability Methods, 99 -- 4.2 Relevance Propagation in ANN, 103 -- 4.3 Rule Extraction from LSTM Networks, 110 -- 4.4 Accuracy and Interpretability, 112 -- 5 Graph Neural Networks, 135 -- 5.1 Concept of Graph Neural Network (GNN), 135 -- 5.2 Categorization and Modeling of GNN, 144 -- 5.3 Complexity of NN, 156 -- 6 Learning Equilibria and Games, 179 -- 6.1 Learning in Games, 179 -- 6.2 Online Learning of Nash Equilibria in Congestion Games, 196 -- 6.3 Minority Games, 202 -- 6.4 Nash Q-Learning, 204 -- 6.5 Routing Games, 211 -- 6.6 Routing with Edge Priorities, 220 -- 7 AI Algorithms in Networks, 227 -- 7.1 Review of AI-Based Algorithms in Networks, 227 -- 7.2 ML for Caching in Small Cell Networks, 237 -- 7.3 Q-Learning-Based Joint Channel and Power Level Selection in Heterogeneous Cellular Networks, 243 -- 7.4 ML for Self-Organizing Cellular Networks, 252 -- 7.5 RL-Based Caching, 267 -- 7.6 Big Data Analytics in Wireless Networks, 274 -- 7.7 Graph Neural Networks, 279 -- 7.8 DRL for Multioperator Network Slicing, 291 -- 7.9 Deep Q-Learning for Latency-Limited Network Virtualization, 302 -- 7.10 Multi-Armed Bandit Estimator (MBE), 317 -- 7.11 Network Representation Learning, 327 -- Part II Quantum Computing, 361 -- 8 Fundamentals of Quantum Communications, 363 -- 8.1 Introduction, 363 -- 8.2 Quantum Gates and Quantum Computing, 372 -- 8.3 Quantum Fourier Transform (QFT), 386 -- 9 Quantum Channel Information Theory, 397 -- 9.1 Communication Over a Channel, 398 -- 9.2 Quantum Information Theory, 401 -- 9.3 Channel Description, 407 -- 9.4 Channel Classical Capacities, 414 -- 9.5 Channel Quantum Capacity, 431 -- 9.6 Quantum Channel Examples, 437 -- 10 Quantum Error Correction, 451 -- 10.1 Stabilizer Codes, 458 -- 10.2 Surface Code, 465 -- 10.3 Fault-Tolerant Gates, 471 -- 10.4 Theoretical Framework, 474 -- 11 Quantum Search Algorithms, 499 -- 11.1 Quantum Search Algorithms, 499 -- 11.2 Physics of Quantum Algorithms, 510 -- 12 Quantum Machine Learning, 543 -- 12.1 QML Algorithms, 543 -- 12.2 QNN Preliminaries, 547 -- 12.3 Quantum Classifiers with ML: Near-Term Solutions, 550 -- 12.4 Gradients of Parameterized Quantum Gates, 560 -- 12.5 Classification with QNNs, 568 -- 12.6 Quantum Decision Tree Classifier, 575 -- 13 QC Optimization, 593 -- 13.1 Hybrid Quantum-Classical Optimization Algorithms, 593 -- 13.2 Convex Optimization in Quantum Information Theory, 601 -- 13.3 Quantum Algorithms for Combinatorial Optimization Problems, 609 -- 13.4 QC for Linear Systems of Equations, 614 -- 13.5 Quantum Circuit, 625 -- 13.6 Quantum Algorithm for Systems of Nonlinear Differential Equations, 628 -- 14 Quantum Decision Theory, 637 -- 14.1 Potential Enablers for Qc, 637 -- 14.2 Quantum Game Theory (QGT), 641 -- 14.3 Quantum Decision Theory (QDT), 665 -- 14.4 Predictions in QDT, 676 -- 15 Quantum Computing in Wireless Networks, 693 -- 15.1 Quantum Satellite Networks, 693 -- 15.2 QC Routing for Social Overlay Networks, 706 -- 15.3 QKD Networks, 713 -- 16 Quantum Network on Graph, 733 -- 16.1 Optimal Routing in Quantum Networks, 733 -- 16.2 Quantum Network on Symmetric Graph, 744 -- 16.3 QWs, 747 -- 16.4 Multidimensional QWs, 753 -- 17 Quantum Internet, 773 -- 17.1 System Model, 775 -- 17.2 Quantum Network Protocol Stack, 789 -- References, 814 -- Index, 821

"By increasing the density and number of different functionalities in wireless networks there is more and more need for the use of artificial intelligence for planning network deployment, running their optimization and dynamically controlling their operation. For example, machine learning algorithms are used for the prediction of traffic and network state in order to timely reserve resources for smooth communication with high reliability and low latency; Big data mining is used to predict customer behaviour and pre-distribute the information content across the network so that it can be efficiently delivered as soon as requested; Intelligent agents can search the internet on behalf of the customer in order to find the best options when it comes to buying any product online. This timely book presents a review of AI-based learning algorithms with a number of case studies supported by Python and R programs, providing a discussion of the learning algorithms used in decision making based on game theory and a number of specific applications in wireless networks, such as channel, network state and traffic prediction. It is expected that once quantum computing becomes a commercial reality, it will be used in wireless communications systems in order to speed up specific processes due to its inherent parallelization capabilities. This is a practical book packed with case studies and follows a basic through to advanced level path and is an ideal course accompaniment for graduate/masters students, and online professional study."-- Provided by publisher