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Deep Reinforcement Learning For Wireless Communications And Networking - Theory Applications And Implementation at Meripustak

Deep Reinforcement Learning For Wireless Communications And Networking - Theory Applications And Implementation by Hoang, John Wiley

Books from same Author: Hoang

Books from same Publisher: John Wiley

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  • General Information  
    Author(s)Hoang
    PublisherJohn Wiley
    Edition1st Edition
    ISBN9781119873679
    Pages288
    BindingHardcover
    LanguageEnglish
    Publish YearJuly 2023

    Description

    John Wiley Deep Reinforcement Learning For Wireless Communications And Networking - Theory Applications And Implementation by Hoang

    Deep Reinforcement Learning for Wireless Communications and NetworkingComprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systemsDeep 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 learningPhysical layer applications covering signal detection, decoding, and beamforming, power and rate control, and physical-layer securityMedium access control (MAC) layer applications, covering resource allocation, channel access, and user/cell associationNetwork layer applications, covering traffic routing, network classification, and network slicingWith 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.



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