000			02743 a2200265 4500
003			OSt
020			_a9781119873679
040			_cIIT Kanpur
041			_aeng
082			_a621.384 _bD36
245			_aDeep reinforcement learning for wireless communications and networking _btheory, applications and implementation _cDinh Thai Hoang ...[et al.]
260			_bJohn Wiley _c2023 _aHoboken
300			_axxii, 264p
520			_aDeep 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.
650			_aReinforcement learning
650			_aDeep learning
650			_aWireless communication
650			_aComputer networks
700			_aHoang, Dinh Thai
700			_aHuynh, Nguyen Van
700			_aNguyen, Diep N.
700			_aHossain, Ekram
700			_aNiyato, Dusit
942			_cBK
999			_c567175 _d567175