Artificial Intelligence And Quantum Computing For Advanced Wireless Networks Savo G Glisic And Beatriz Lorenzo by Savo G. Glisic And Beatriz Lorenzo instant download after payment.

A practical overview of the implementation of artificial intelligence (AI) and quantum computing technology in large-scale communication networks Increasingly dense and flexible wireless networks require the use of AI for planning network deployment, optimization, and dynamic control. ML algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency. In this book, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few. The authors offer readers an intuitive and accessible path from basic topics on ML through advanced concepts and techniques in quantum networks. Readers will benefit from: A thorough introduction to the fundamentals of ML algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines An exploration of ANN, including multilayer NN, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum Information Theory, fundamentals of quantum internet, and more Discussions of explainable NN and XAI Examinations of graph NN, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology

Perfect for network engineers, researchers, and graduate and master’s students in computer science and electrical engineering,