Machine Learning For Wireless Communications And Networking
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Deep Reinforcement Learning For Wireless Communications And Networking
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Author : Dinh Thai Hoang
language : en
Publisher: John Wiley & Sons
Release Date : 2023-07-25
Deep Reinforcement Learning For Wireless Communications And Networking written by Dinh Thai Hoang 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 2023-07-25 with Technology & Engineering categories.
Deep Reinforcement Learning for Wireless Communications and Networking Comprehensive guide to Deep Reinforcement Learning (DRL) as applied to wireless communication systems 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.
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.
Deep Learning And Its Applications For Vehicle Networks
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Author : Fei Hu
language : en
Publisher: CRC Press
Release Date : 2023-05-12
Deep Learning And Its Applications For Vehicle Networks written by Fei Hu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-12 with Computers categories.
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
Ai Machine Learning And Deep Learning
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Author : Fei Hu
language : en
Publisher: CRC Press
Release Date : 2023-06-05
Ai Machine Learning And Deep Learning written by Fei Hu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-05 with Computers categories.
Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered
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 : 2021-09-13
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 2021-09-13 with Computers 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.
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.
Green Machine Learning Protocols For Future Communication Networks
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Author : Saim Ghafoor
language : en
Publisher: CRC Press
Release Date : 2023-10-25
Green Machine Learning Protocols For Future Communication Networks written by Saim Ghafoor and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-25 with Computers categories.
Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.
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.
Driving 5g Mobile Communications With Artificial Intelligence Towards 6g
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Author : Dragorad A. Milovanovic
language : en
Publisher: CRC Press
Release Date : 2023-04-06
Driving 5g Mobile Communications With Artificial Intelligence Towards 6g written by Dragorad A. Milovanovic and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-06 with Technology & Engineering categories.
Driving 5G Mobile Communications with Artificial Intelligence towards 6G presents current work and directions of continuous innovation and development in multimedia communications with a focus on services and users. The fifth generation of mobile wireless networks achieved the first deployment by 2020, completed the first phase of evolution in 2022, and started transition phase of 5G-Advanced toward the sixth generation. Perhaps one of the most important innovations brought by 5G is the platform-approach to connectivity, i.e., a single standard that can adapt to the heterogeneous connectivity requirements of vastly different use cases. 5G networks contain a list of different requirements, standardized technical specifications and a range of implementation options with spectral efficiency, latency, and reliability as primary performance metrics. Towards 6G, machine learning (ML) and artificial intelligence (AI) methods have recently proposed new approaches to modeling, designing, optimizing and implementing systems. They are now matured technologies that improve many research fields significantly. The area of wireless multimedia communications has developed immensely, generating a large number of concepts, ideas, technical specifications, mobile standards, patents, and articles. Identifying the basic ideas and their complex interconnections becomes increasingly important. The book is divided into three major parts, with each part containing four or five chapters: Advanced 5G communication Machine learning-based communication and network automation Artificial Intelligence towards 6G The first part discusses three main scenarios and standard specification of 5G use cases (eMBB, URLLC, mMTC), vehicular systems beyond 5G, and efficient edge architecture on NFV infrastructure. In the second part, different AI/ML-based methodologies and open research challenges are presented in introducing 5G-AIoT artificial intelligence of things, scheduling in 5G/6G communication systems, application of DL techniques to modulation, detection, and channel coding as well as 5G Open Source tools for experimentations and testing. The third part paved the way to deployment scenarios for different innovative services including technologies and applications of 5G/6G intelligent connectivity, AI-assisted eXtended Reality, integrated 5G-IoT architecture in next-generation Smart Grid, privacy requirements in a hyper-connected world, and evaluation of representative 6G use cases and technology trends. The book is written by field experts from Europe and Mauritius who introduce a blend of scientific and engineering concepts covering this emerging wireless communication era. It is a very good reference book for telecom professionals, engineers, and practitioner in various 5G vertical domains and, finally, a basis for student courses in 5G/6G wireless systems.
Machine Learning And Cognitive Computing For Mobile Communications And Wireless Networks
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Author : Krishna Kant Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2020-07-08
Machine Learning And Cognitive Computing For Mobile Communications And Wireless Networks written by Krishna Kant Singh 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 2020-07-08 with Computers categories.
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.