Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and ImplementationКНИГИ » ПРОГРАММИНГ
Название: Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation Автор: Dinh Thai Hoang, Nguyen Van Huynh, Diep N. Nguyen Издательство: Wiley-IEEE Press Год: 2023 Страниц: 288 Язык: английский Формат: epub (true) Размер: 12.5 MB
Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems.
Deep Reinforcement Learning empowered by deep neural networks (DNNs) has been developing as a promising solution to address high-dimensional and continuous control problems effectively. The integration of DRL into future wireless networks will revolutionize conventional model-based network optimization with model-free approaches and meet various application demands. By interacting with the environment, DRL provides an autonomous decision-making mechanism for the network entities to solve non-convex, complex, model-free problems, e.g. spectrum access, handover, scheduling, caching, data offloading, and resource allocation. This not only reduces communication overhead but also improves network security and reliability. Though DRL has shown great potential to address emerging issues in complex wireless networks, there are still domain-specific challenges that require further investigation. The challenges may include the design of proper DNN architectures to capture the characteristics of 5G network optimization problems, the state explosion in dense networks, multi-agent learning in dynamic networks, limited training data and exploration space in practical networks, the inaccessibility and high cost of network information, as well as the balance between information quality and learning performance.
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.
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.
Introduction Part I: Fundamentals of Deep Reinforcement Learning 1 Deep Reinforcement Learning and Its Applications 2 Markov Decision Process and Reinforcement Learning 3 Deep Reinforcement Learning Models and Techniques 4 A Case Study and Detailed Implementation Part II: Applications of DRL in Wireless Communications and Networking 5 DRL at the Physical Layer 6 DRL at the MAC Layer 7 DRL at the Network Layer 8 DRL at the Application and Service Layer Part III: Challenges, Approaches, Open Issues, and Emerging Research Topics 9 DRL Challenges in Wireless Networks 10 DRL and Emerging Topics in Wireless Networks Index
Скачать Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation
Machine Learning and Wireless Communications Название: Machine Learning and Wireless Communications Автор: Edited by Yonina C. Eldar, Andrea Goldsmith, Deniz G?nd?z, H. Vincent Poor...
Deep Reinforcement Learning Название: Deep Reinforcement Learning Автор: Aske Plaat Издательство: Springer Год: 2022 Формат: PDF Страниц: 414 Размер: 11,44 MB Язык: English ...
Deep Learning and Practice with MindSpore Название: Deep Learning and Practice with MindSpore Автор: Lei Chen Издательство: Springer Год: 2021 Формат: PDF Страниц: 403 Размер: 14,1 Mb Язык:...
Deep Reinforcement Learning for Wireless Networks Название: Deep Reinforcement Learning for Wireless Networks Автор: F. Richard Yu, Ying He Издательство: Springer Год: 2019 Язык: английский Формат:...
Навигация
Вход на сайт
Реклама
Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation 
