Artificial Intelligence and Quantum Computing for Advanced Wireless Networks
Increasingly dense and flexible wireless networks require the use of artificial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning 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 Artificial Intelligence and Quantum Computing Technology in Advanced Wireless Networks, 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 machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from:
- A thorough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machines
- An exploration of artificial neural networks, including multilayer neural networks, training and backpropagation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and more
- Discussions of explainable neural networks and XAI
- Examinations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both classical and quantum computing technology
| Publisher Name | Wiley |
|---|---|
| Author Name | Hagendorf, Col |
| Format | Audio |
| Bisac Subject Major | COM |
| Language | NG |
| Isbn 10 | 1119790298 |
| Isbn 13 | 9781119790297 |
| Target Age Group | min:NA, max:NA |
| Dimensions | 00.00" H x 00.00" L x 00.00" W |
| Page Count | 848 |
Savo Glisic, Professor of Telecommunications at University of Oulu, Finland, and Director of Institute for Networking Sciences. His research interest is in the area of network optimization theory, network topology control and graph theory, cognitive networks and game theory, radio resource management, QoS and queuing theory, networks information theory, protocol design, advanced routing and network coding, relaying, cellular, WLAN, ad hoc, sensor, active and bio inspired networks with emphasis on genetic algorithms and stochastic geometry. The latest interest is in the area of spectra sharing, robust heterogeneous network design, Artificial Intelligence (AI), Inter System Networking (ISN), block chains and complex networks theory. Dr. Glisic has served as the Technical Program Chairman of the third IEEE ISSSTA'94, the eighth IEEE PIMRC'97, and IEEE ICC'01. He was also Director of IEEE ComSoc MD programs. Beatriz Lorenzo, Assistant Professor, Electrical and Computer Engineering Department, University of Massachusetts at Amherst, USA. Dr Lorenzo obtained her Ph.D degree from the University of Oulu, Finland, in 2012. She co-published Advanced Wireless Networks: 4G Cognitive Opportunistic and Cooperative Technology with Savo Glisic in 2009, and her current research interests include communication networks, wireless networks, mobile computing, dynamic networking paradigms, network economics, optimization theory. She is a member of the IEEE.
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