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\u003cb\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c/b\u003e \u003cp\u003e\u003cb\u003eComprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems\u003c/b\u003e \u003cp\u003e\u003ci\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c/i\u003e 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. \u003cp\u003eCovering 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. \u003cp\u003e\u003ci\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c/i\u003e covers specific topics such as: \u003cul\u003e\u003cli\u003eDeep reinforcement learning models, covering deep learning, deep reinforcement learning, and models of deep reinforcement learning\u003c/li\u003e \u003cli\u003ePhysical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer security\u003c/li\u003e \u003cli\u003eMedium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell association\u003c/li\u003e \u003cli\u003eNetwork layer applications, covering traffic routing, network classification, and network slicing\u003c/li\u003e\u003c/ul\u003e \u003cp\u003eWith comprehensive coverage of an exciting and noteworthy new technology, \u003ci\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c/i\u003e 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. |
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