{"product_id":"deep-reinforcement-learning-for-wireless-communications-and-networking-theory-applications-and-implementation-hardcover-1","title":"Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eDinh Thai Hoang\u003c\/b\u003e (Author), \u003cb\u003eNguyen Van Huynh\u003c\/b\u003e (Author), \u003cb\u003eDiep N. Nguyen\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eComprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems\u003c\/b\u003e \u003c\/p\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. \u003c\/p\u003e\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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c\/i\u003e covers specific topics such as: \u003c\/p\u003e\u003cul\u003e\n\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\n\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.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eComprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems\u003c\/b\u003e \u003c\/p\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. \u003c\/p\u003e\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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eDeep Reinforcement Learning for Wireless Communications and Networking\u003c\/i\u003e covers specific topics such as: \u003c\/p\u003e\u003cul\u003e\n\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\n\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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDinh Thai Hoang, Ph.D., \u003c\/b\u003e is a faculty member at the University of Technology Sydney, Australia. He is also an Associate Editor of \u003ci\u003eIEEE Communications Surveys \u0026amp; Tutorials\u003c\/i\u003e and an Editor of \u003ci\u003eIEEE Transactions on Wireless Communications, IEEE Transactions on Cognitive Communications and Networking\u003c\/i\u003e, and \u003ci\u003eIEEE Transactions on Vehicular Technology\u003c\/i\u003e. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNguyen Van Huynh, Ph.D., \u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDiep N. Nguyen, Ph.D., \u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eEkram Hossain, Ph.D., \u003c\/b\u003e 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 \u003ci\u003eRadio Resource Management in Multi-Tier Cellular Wireless Networks\u003c\/i\u003e (2013). \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDusit Niyato, Ph.D., \u003c\/b\u003e is a Professor in the School of Computer Science and Engineering at Nanyang Technological University, Singapore. He co-authored the Wiley title \u003ci\u003eRadio Resource Management in Multi-Tier Cellular Wireless Networks\u003c\/i\u003e (2013).\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 288\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.69 x 9 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 25, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47213818478841,"sku":"9781119873679","price":194.4,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/kJsJiKsQ3K9781119873679_d3758862-355b-43ac-bdbc-81e191c19ee9.webp?v=1768109317","url":"https:\/\/bookscloud.io\/products\/deep-reinforcement-learning-for-wireless-communications-and-networking-theory-applications-and-implementation-hardcover-1","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}