Machine Learning for Wireless Communications and Networking
Academic Press Inc (Verlag)
978-0-323-91238-9 (ISBN)
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Every chapter also has a dedicated section applying machine learning techniques to specific, state-of-the-art wireless network applications. This book will be ideal for graduate and senior undergraduate students in wireless communications and networking who need to understand and apply machine learning techniques, researchers in wireless communications, signal processing, wireless network professionals who need background knowledge in machine learning for wireless systems and networks, and engineers and professionals in the wireless communications and networking industry seeking to learn this important new technology which is having a major impact in the field.
Dr. Chen received the B.S. from the National Taiwan University in 1983, and the M.S. and Ph.D from the University of Maryland, College Park, in 1987 and 1989, all in electrical engineering. From 1987 to 1998, Dr. Chen worked with SSE, COMSAT, IBM Thomas J. Watson Research Center, and National Tsing Hua University, in mobile communications and networks. From 1998-2016, Dr. Chen was with National Taiwan University, Taipei, Taiwan, where he was Distinguished Professor and Irving T. Ho Chair Professor, and served as the Director, Graduate Institute of Communication Engineering, Director, Communication Research Center, and Associate Dean, College of Electrical Engineering and Computer Science. He has authored and co-authored near 300 IEEE papers, 23 granted US patents, 3 books, including a few Highly Cited Papers. Dr. Chen is an IEEE Fellow and a recipient for a number of prestigious awards including 2011 IEEE COMSOC WTC Wireless Communication Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. His technical leadership results in Best Paper Awards in major IEEE Conferences like ICC, Globecom, and PIMRC.
1. Basic Concepts of Machine Learning
2. Statistical Inference
3. Regression
4. Classification
5. Deep Learning and Big Data Driven Methodology
6. Federated Learning
7. Generative Adversarial Network
8. Reinforcement Learning
9. Wireless Robotic Communications: Wireless Networked Multi-Agent Systems
10. Naïve Bayesian, Decision Tree, and Random Forest
11. Bayesian Networks
12. Future Machine Learning Based Network Architecture
Erscheint lt. Verlag | 1.1.2025 |
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Verlagsort | Oxford |
Sprache | englisch |
Maße | 191 x 235 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Technik ► Nachrichtentechnik | |
ISBN-10 | 0-323-91238-9 / 0323912389 |
ISBN-13 | 978-0-323-91238-9 / 9780323912389 |
Zustand | Neuware |
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