Deep Reinforcement Learning for Wireless Communications and Networking

Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems

Deep Reinforcement Learning for Wireless Communications and Networking presents an overview of the development of DRL while providing fundamental knowledge about theories, formulation, design, learning models, algorithms and implementation of DRL together with a particular case study to practice. The book also covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking.

Covering new advanced models of DRL, e.g., deep dueling architecture and generative adversarial networks, as well as emerging problems considered in wireless networks, e.g., ambient backscatter communication, intelligent reflecting surfaces and edge intelligence, this is the first comprehensive book studying applications of DRL for wireless networks that presents the state-of-the-art research in architecture, protocol, and application design.

Deep Reinforcement Learning for Wireless Communications and Networking covers specific topics such as:

• Deep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning
• Physical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security
• Medium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association
• Network layer applications, covering traffic routing, network classification, and network slicing

With comprehensive coverage of an exciting and noteworthy new technology, Deep Reinforcement Learning for Wireless Communications and Networking is an essential learning resource for researchers and communications engineers, along with developers and entrepreneurs in autonomous systems, who wish to harness this technology in practical applications.

Table of Contents

Part I Fundamentals of Deep Reinforcement Learning

1. Deep Reinforcement Learning and Its Applications

2 Markov Decision Process and Reinforcement Learning

3 Deep Reinforcement Learning Models and Techniques

4 A Case Study and Detailed Implementation

Part II Applications of DRL in Wireless Communications and Networking

5 DRL at the Physical Layer

6 DRL at the MAC Layer

7 DRL at the Network Layer

8 DRL at the Application and Service Layer

Part III Challenges, Approaches, Open Issues, and Emerging Research Topics

9 DRL Challenges in Wireless Networks

10 DRL and Emerging Topics in Wireless Networks

About the Author

Dinh Thai Hoang, Ph.D., is a faculty member at the University of Technology Sydney, Australia. He is also an Associate Editor of IEEE Communications Surveys & Tutorials and an Editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Cognitive Communications and Networking, and IEEE Transactions on Vehicular Technology.

Nguyen Van Huynh, Ph.D., obtained his Ph.D. from the University of Technology Sydney in 2022. He is currently a Research Associate in the Department of Electrical and Electronic Engineering, Imperial College London, UK.

Diep N. Nguyen, Ph.D., is Director of Agile Communications and Computing Group and a member of the Faculty of Engineering and Information Technology at the University of Technology Sydney, Australia.

Ekram Hossain, Ph.D., is a Professor in the Department of Electrical and Computer Engineering at the University of Manitoba, Canada, and a Fellow of the IEEE. He co-authored the Wiley title Radio Resource Management in Multi-Tier Cellular Wireless Networks (2013).

Dusit Niyato, Ph.D., is a Professor in the School of Computer Science and Engineering at Nanyang Technological University, Singapore. He co-authored the Wiley title Radio Resource Management in Multi-Tier Cellular Wireless Networks (2013).