Machine Learning For Wireless Communication

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Deep Learning In Wireless Communications

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Author : Haijun Zhang
language : en
Publisher: Springer Nature
Release Date : 2024-10-03

Deep Learning In Wireless Communications written by Haijun Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-03 with Technology & Engineering categories.

The book offers a focused examination of deep learning-based wireless communication systems and their applications. While both principles and engineering practice are explored, greater emphasis is placed on the latter. The book offers an in-depth exploration of major topics such as cognitive spectrum intelligence, learning resource allocation optimization, transmission intelligence, learning traffic and mobility prediction, and security in wireless communication. Notably, the book provides a comprehensive and systematic treatment of practical issues related to intelligent wireless communication, making it particularly useful for those seeking to learn about practical solutions in AI-based wireless resource management. This book is a valuable resource for researchers, engineers, and graduate students in the fields of wireless communication, telecommunications, and related areas.

Machine Learning For Wireless Communication

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Author : Dr Sanjay Agal
language : en
Publisher: Notion Press
Release Date : 2025-04-10

Machine Learning For Wireless Communication written by Dr Sanjay Agal and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-10 with Education categories.

Machine Learning for Wireless Communication: Principles and Applications Edited by Dr. Sanjay Agal Unlock the future of connectivity where algorithms power intelligent communication systems. Machine Learning for Wireless Communication: Principles and Applications is your essential guide to the transformative synergy between machine learning and next-generation wireless technologies. Whether you're a researcher, student, or industry professional, this comprehensive volume bridges the gap between theory and practical application. Covering foundational concepts-from supervised and unsupervised learning to deep learning and reinforcement learning-it progresses into real-world applications such as 5G/6G optimization, IoT integration, edge computing, and network security. With clear explanations, illustrative case studies, and insights from seasoned educators and engineers, this book goes beyond technical depth to offer a strategic vision of how data-driven intelligence is reshaping wireless networks. Step into a world where machines not only learn but anticipate, adapt, and revolutionize how we connect.

Applications Of Machine Learning In Wireless Communications

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Author : Ruisi He
language : en
Publisher: Institution of Engineering and Technology
Release Date : 2019-06-20

Applications Of Machine Learning In Wireless Communications written by Ruisi He and has been published by Institution of Engineering and Technology this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-20 with Technology & Engineering categories.

Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.

Machine Learning For Future Wireless Communications

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Author : Fa-Long Luo
language : en
Publisher: John Wiley & Sons
Release Date : 2020-02-10

Machine Learning For Future Wireless Communications written by Fa-Long Luo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-10 with Technology & Engineering categories.

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

Machine Learning And Deep Learning Techniques In Wireless And Mobile Networking Systems

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Author : K. Suganthi
language : en
Publisher: CRC Press
Release Date : 2021-09-14

Machine Learning And Deep Learning Techniques In Wireless And Mobile Networking Systems written by K. Suganthi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-14 with Technology & Engineering categories.

This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.

Machine Learning For Future Wireless Communications

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Author : Fa-Long Luo
language : en
Publisher: John Wiley & Sons
Release Date : 2019-12-19

Machine Learning For Future Wireless Communications written by Fa-Long Luo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-19 with Technology & Engineering categories.

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

Machine Learning For Wireless Communication

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Author : Rohit M. Thanki
language : en
Publisher: Springer
Release Date : 2025-07-16

Machine Learning For Wireless Communication written by Rohit M. Thanki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-16 with Technology & Engineering categories.

This book covers the basic principles of wireless communication while delving into the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. The authors provide real-world examples and case studies to illustrate the use of machine learning in wireless communication applications such as channel estimation, mobility prediction, resource allocation, and beamforming. This book is an essential resource for researchers, engineers, and students interested in understanding and applying machine learning techniques in the context of wireless communication systems.

Machine Learning For Wireless Communications And Networking

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Author : Kwang-Cheng Chen
language : en
Publisher: Academic Press
Release Date : 2023-02-01

Machine Learning For Wireless Communications And Networking written by Kwang-Cheng Chen and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-01 with Computers categories.

Machine Learning for Wireless Communications and Networking: An Introduction gives an easy-to-understand introduction to machine learning methods and techniques and their application to wireless communications. The book covers a wide range of machine learning techniques, starting with concepts related to statistical signal processing (i.e. decision/detection and estimation), taking advantage of the commonality of knowledge between statistical learning and statistical communication theory that the electronic engineer will be familiar with. Each chapter focuses on a class of machine learning techniques, clearly explaining the principles with a supporting range of examples in general wireless communications, wireless networks, sensor networks, and signal processing. Every chapter also has a dedicated section applying machine learning techniques to specific, state-of-the-art wireless network applications. Machine Learning for Wireless Communications and Networking: An Introduction is ideal for graduate and senior undergraduate students in wireless communications and networking who need to understand and apply machine learning techniques, researchers in wireless communications, signal processing, and wireless networks who need background knowledge in machine learning for wireless systems and networks, and engineers and professionals in the wireless communications and networking industry seeking to learn this important new technology which is having a major impact in the field.

Wireless Communication With Artificial Intelligence

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Author : Anuj Singal
language : en
Publisher: CRC Press
Release Date : 2022-09-16

Wireless Communication With Artificial Intelligence written by Anuj Singal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-16 with Technology & Engineering categories.

This reference text discusses advances in wireless communication, design challenges, and future research directions to design reliable wireless communication. The text discusses emerging technologies including wireless sensor networks, Internet of Things (IoT), cloud computing, mm-Wave, Massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling. The text covers artificial intelligence-based applications in wireless communication, machine learning techniques and challenges in wireless sensor networks, and deep learning for channel and bandwidth estimation during optical wireless communication. The text will be useful for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.

Federated Learning For Future Intelligent Wireless Networks

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Author : Yao Sun
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-27

Federated Learning For Future Intelligent Wireless Networks written by Yao Sun and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-27 with Technology & Engineering categories.

Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL Comprehensive explorations of wireless communication network design and optimization for federated learning Practical discussions of novel federated learning algorithms and frameworks for future wireless networks Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.