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Machine Learning for Wireless Communications and Networking
An Introduction
- 1st Edition - December 1, 2099
- Author: Kwang-Cheng Chen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 2 3 8 - 9
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 6 1 6 - 5
Machine Learning for Wireless Communications and Networking: An Introduction provides an easy-to-understand introduction to machine learning methods and techniques and their applic… Read more
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Request a sales quoteMachine Learning for Wireless Communications and Networking: An Introduction provides an easy-to-understand introduction to machine learning methods and techniques and their application to wireless communications. The book covers a wide range of machine learning techniques, starting with concepts related to statistical signal processing (i.e.,decision/detection and estimation), taking advantage of the commonality of knowledge between statistical learning and statistical communication theory that the electronic engineer will be familiar with. Each chapter focuses on a class of machine learning techniques, clearly explaining the principles with a supporting range of examples in general wireless communications, wireless networks, sensor networks, and signal processing.
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.
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
Graduate students, academic researchers, R&D engineers wanting to learn and apply machine learning techniques to wireless communications and networking.
1. Basic Concepts of Machine Learning
2. Statistical Inference
Introduction of statistical foundation for hypothesis testing (for decision/detection) and
estimation
Applications: Internet tomography and cognitive radio network tomography
3. Regression
Linear regression, multiple regression, ridge regression, LASSO, and logistic regression
Applications: rapid channel modeling
4. Classification
Pattern recognition, support vector machine, and unsupervised learning for
classification
Applications: signal detection and interception, spectrum sensing, radio access network
5. Deep Learning and Big Data Driven Methodology
Principles of artificial neural network, convolution NN, recurrent NN, deep learning, big
data analytics
Applications: mobility pattern prediction, vehicle fleet management, traffic prediction
6. Federated Learning
Applications: wireless sensor networks and massive machine-type communication
7. Generative Adversarial Network
Applications: wireless network synchronization
8. Reinforcement Learning
Markov decision processes, multi-armed bandit problem, and reinforcement learning
Applications: radio resource management, cognitive radio, beamforming in MIMO
communications
9. Wireless Robotic Communications: Wireless Networked Multi-Agent Systems
Applications: Interplay between wireless techniques and multi-agent systems
10. Naïve Bayesian, Decision Tree, and Random Forest
Applications: predictive mobility management for ultra-low latency wireless networking
11. Bayesian Networks
Graphical models and causality in AI
Applications: Interference management
12. Future Machine Learning Based Network Architecture
Future mobile network architecture to incorporate machine learning
2. Statistical Inference
Introduction of statistical foundation for hypothesis testing (for decision/detection) and
estimation
Applications: Internet tomography and cognitive radio network tomography
3. Regression
Linear regression, multiple regression, ridge regression, LASSO, and logistic regression
Applications: rapid channel modeling
4. Classification
Pattern recognition, support vector machine, and unsupervised learning for
classification
Applications: signal detection and interception, spectrum sensing, radio access network
5. Deep Learning and Big Data Driven Methodology
Principles of artificial neural network, convolution NN, recurrent NN, deep learning, big
data analytics
Applications: mobility pattern prediction, vehicle fleet management, traffic prediction
6. Federated Learning
Applications: wireless sensor networks and massive machine-type communication
7. Generative Adversarial Network
Applications: wireless network synchronization
8. Reinforcement Learning
Markov decision processes, multi-armed bandit problem, and reinforcement learning
Applications: radio resource management, cognitive radio, beamforming in MIMO
communications
9. Wireless Robotic Communications: Wireless Networked Multi-Agent Systems
Applications: Interplay between wireless techniques and multi-agent systems
10. Naïve Bayesian, Decision Tree, and Random Forest
Applications: predictive mobility management for ultra-low latency wireless networking
11. Bayesian Networks
Graphical models and causality in AI
Applications: Interference management
12. Future Machine Learning Based Network Architecture
Future mobile network architecture to incorporate machine learning
- No. of pages: 375
- Language: English
- Edition: 1
- Published: December 1, 2099
- Imprint: Academic Press
- Paperback ISBN: 9780323912389
- eBook ISBN: 9780323916165
KC
Kwang-Cheng Chen
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.
Affiliations and expertise
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.