Machine Learning For Low Latency Communications
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Machine Learning For Low Latency Communications
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Author : Yong Zhou
language : en
Publisher: Elsevier
Release Date : 2024-10-10
Machine Learning For Low Latency Communications written by Yong Zhou and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Technology & Engineering categories.
Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the book develops learning to estimate methods via algorithm unrolling and multiarmed bandit for reducing access latency by enlarging the number of concurrent transmissions with the same pilot length. Task-oriented learning to compress methods based on information bottleneck are given to reduce the transmission latency via avoiding unnecessary data transmission. Lastly, three learning to optimize methods for processing latency reduction are given which leverage graph neural networks, multi-agent reinforcement learning, and domain knowledge. Low-latency communications attracts considerable attention from both academia and industry, given its potential to support various emerging applications such as industry automation, autonomous vehicles, augmented reality and telesurgery. Despite the great promise, achieving low-latency communications is critically challenging. Supporting massive connectivity incurs long access latency, while transmitting high-volume data leads to substantial transmission latency. - Presents the challenges and opportunities of leveraging data and model-driven machine learning methodologies for achieving low-latency communications - Explains the principles and practices of modern machine learning algorithms (e.g., algorithm unrolling, multiarmed bandit, graph neural network, and multi-agent reinforcement learning) for achieving low-latency communications - Gives design, modeling, and optimization methods for low-latency communications that apply appropriate learning methods to solve longstanding problems - Provides full details of the simulation setup and benchmarking algorithms, with downloadable code - Outlines future research challenges and directions
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 Intelligent Communications
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Author : Xiangping Bryce Zhai
language : en
Publisher: Springer Nature
Release Date : 2019-10-27
Machine Learning And Intelligent Communications written by Xiangping Bryce Zhai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-27 with Computers categories.
This volume constitutes the refereed post-conference proceedings of the Fourth International Conference on Machine Learning and Intelligent Communications, MLICOM 2019, held in Nanjing, China, in August 2019. The 65 revised full papers were carefully selected from 114 submissions. The papers are organized thematically in machine learning, intelligent positioning and navigation, intelligent multimedia processing and security, wireless mobile network and security, cognitive radio and intelligent networking, IoT, intelligent satellite communications and networking, green communication and intelligent networking, ad-hoc and sensor networks, resource allocation in wireless and cloud networks, signal processing in wireless and optical communications, and intelligent cooperative communications and networking.
Machine Learning And Wireless Communications
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Author : Yonina C. Eldar
language : en
Publisher: Cambridge University Press
Release Date : 2022-08-04
Machine Learning And Wireless Communications written by Yonina C. Eldar and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Technology & Engineering categories.
How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
Machine Learning For Cyber Security
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Author : Xiaofeng Chen
language : en
Publisher: Springer Nature
Release Date : 2020-11-10
Machine Learning For Cyber Security written by Xiaofeng Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.
This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.
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.
Machine Learning Techniques For Smart City Applications Trends And Solutions
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Author : D. Jude Hemanth
language : en
Publisher: Springer Nature
Release Date : 2022-09-19
Machine Learning Techniques For Smart City Applications Trends And Solutions written by D. Jude Hemanth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-19 with Computers categories.
This book discusses the application of different machine learning techniques to the sub-concepts of smart cities such as smart energy, transportation, waste management, health, infrastructure, etc. The focus of this book is to come up with innovative solutions in the above-mentioned issues with the purpose of alleviating the pressing needs of human society. This book includes content with practical examples which are easy to understand for readers. It also covers a multi-disciplinary field and, consequently, it benefits a wide readership including academics, researchers, and practitioners.
Fundamentals Of 6g Communications And Networking
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Author : Xingqin Lin
language : en
Publisher: Springer Nature
Release Date : 2023-12-11
Fundamentals Of 6g Communications And Networking written by Xingqin Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-11 with Technology & Engineering categories.
This book begins with a historical overview of the evolution of mobile technologies and addresses two key questions: why do we need 6G? and what will 6G be? The remaining chapters of this book are organized into three parts: Part I covers the foundation of an end-to-end 6G system by presenting 6G vision, driving forces, key performance indicators, and societal requirements on digital inclusion, sustainability, and intelligence. Part II presents key radio technology components for the 6G communications to deliver extreme performance, including new radio access technologies at high frequencies, joint communications and sensing, AI-driven air interface, among others. Part III describes key enablers for intelligent 6G networking, including network disaggregation, edge computing, data-driven management and orchestration, network security and trustworthiness, among others. This book is relevant to researchers, professionals, and academics working in 5G/6G and beyond.
Tinyml For Edge Intelligence In Iot And Lpwan Networks
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Author : Bharat S Chaudhari
language : en
Publisher: Elsevier
Release Date : 2024-05-29
Tinyml For Edge Intelligence In Iot And Lpwan Networks written by Bharat S Chaudhari and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-29 with Computers categories.
Recently, Tiny Machine Learning (TinyML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated embedded IoT and low power wide area networks (LPWAN) nodes with very low on-board memory and computational capabilities are getting connected to the Internet each year, there is a critical need to have a special computational framework like TinyML. TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to IoT and LPWANs. It starts by providing the foundations of IoT/LPWANs, low power embedded systems and hardware, the role of artificial intelligence and machine learning in communication networks in general and cloud/edge intelligence. It then presents the concepts, methods, algorithms and tools of TinyML. Practical applications of the use of TinyML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies. TinyML for Edge Intelligence in IoT and LPWAN Networks is highly suitable for academic researchers and professional system engineers, architects, designers, testers, deployment engineers seeking to design ultra-lower power and time-critical applications. It would also help in designing the networks for emerging and future applications for resource-constrained nodes. - This book provides one-stop solutions for emerging TinyML for IoT and LPWAN applications. - The principles and methods of TinyML are explained, with a focus on how it can be used for IoT, LPWANs, and 5G applications. - Applications from the healthcare and industrial sectors are presented. - Guidance on the design of applications and the selection of appropriate technologies is provided.
Deep Learning Powered Technologies
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Author : Khaled Salah Mohamed
language : en
Publisher: Springer Nature
Release Date : 2023-06-23
Deep Learning Powered Technologies written by Khaled Salah Mohamed and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-23 with Technology & Engineering categories.
This book covers various, leading-edge deep learning technologies. The author discusses new applications of deep learning and gives insight into the integration of deep learning with various application domains, such as autonomous driving, augmented reality, AIOT, 5G and beyond.