Machine Learning for Wireless Communications and Networking: An Introduction
Autor Kwang-Cheng Chenen Limba Engleză Paperback – 31 dec 2024
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.
- Provides easily accessible and understandable machine learning knowledge for the reader with a background in communications, networking and signal processing
- Presents a comprehensive and easy-to-understand discussion of machine learning techniques that are supported with a range of examples
- Includes a detailed presentation of statistical inference as a foundation for machine learning while also providing a foundation in general for communications engineering, signal processing, control and automation, cyber-physical systems, Internet of Things and cybersecurity
- Presents statistical signal processing principles that are commonly used in communications engineering, signal processing, stochastic control and networking
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Specificații
ISBN-13: 9780323912389
ISBN-10: 0323912389
Pagini: 375
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0323912389
Pagini: 375
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE
Public țintă
Graduate students, academic researchers, R&D engineers wanting to learn and apply machine learning techniques to wireless communications and networking.Cuprins
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
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
Notă biografică
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.
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.