Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation – Ebook Instant Download/Delivery ISBN(s): 9781119873679,1119873673
Product details:
- ISBN-10 : 1119873673
- ISBN-13 : 978-1119873679
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.
Table contents:
Part I Fundamentals of Deep Reinforcement Learning 1
1 Deep Reinforcement Learning and Its Applications 3
2 Markov Decision Process and Reinforcement Learning 25
3 Deep Reinforcement Learning Models and Techniques 37
4 A Case Study and Detailed Implementation 83
Part II Applications of Drl in Wireless Communications and Networking 109
5 DRL at the Physical Layer 111
6 DRL at the MAC Layer 137
7 DRL at the Network Layer 163
8 DRL at the Application and Service Layer 187
Part III Challenges, Approaches, Open Issues, and Emerging Research Topics 213
9 DRL Challenges in Wireless Networks 215
10 DRL and Emerging Topics in Wireless Networks 241
References 259
Index 263
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