Deep Reinforcement Learning For Wireless Networks

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Deep Reinforcement Learning for Wireless Networks

Author : F. Richard Yu
Publisher : Springer
ISBN 13 : 3030105466
Total Pages : 78 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Deep Reinforcement Learning for Wireless Networks by : F. Richard Yu

Download or read book Deep Reinforcement Learning for Wireless Networks written by F. Richard Yu and published by Springer. This book was released on 2019-01-17 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool.

Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks

Author : Krishna Kant Singh
Publisher : John Wiley & Sons
ISBN 13 : 1119640369
Total Pages : 272 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks by : Krishna Kant Singh

Download or read book Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks written by Krishna Kant Singh and published by John Wiley & Sons. This book was released on 2020-07-08 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning and Wireless Communications

Author : Yonina C. Eldar
Publisher : Cambridge University Press
ISBN 13 : 1108967736
Total Pages : 560 pages
Book Rating : 4.1/5 (89 download)

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Book Synopsis Machine Learning and Wireless Communications by : Yonina C. Eldar

Download or read book Machine Learning and Wireless Communications written by Yonina C. Eldar and published by Cambridge University Press. This book was released on 2022-06-30 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and Deep Learning Techniques in Wireless and Mobile Networking Systems

Author : K. Suganthi
Publisher : CRC Press
ISBN 13 : 1000441814
Total Pages : 285 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems by : K. Suganthi

Download or read book Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems written by K. Suganthi and published by CRC Press. This book was released on 2021-09-13 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Fa-Long Luo
Publisher : John Wiley & Sons
ISBN 13 : 1119562252
Total Pages : 490 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Machine Learning for Future Wireless Communications by : Fa-Long Luo

Download or read book Machine Learning for Future Wireless Communications written by Fa-Long Luo and published by John Wiley & Sons. This book was released on 2020-02-10 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Energy-Efficient Underwater Wireless Communications and Networking

Author : Goyal, Nitin
Publisher : IGI Global
ISBN 13 : 1799836428
Total Pages : 339 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Energy-Efficient Underwater Wireless Communications and Networking by : Goyal, Nitin

Download or read book Energy-Efficient Underwater Wireless Communications and Networking written by Goyal, Nitin and published by IGI Global. This book was released on 2020-09-04 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: Underwater wireless sensor networks (UWSN) are envisioned as an aquatic medium for a variety of applications including oceanographic data collection, disaster management or prevention, assisted navigation, attack protection, and pollution monitoring. Similar to terrestrial wireless sensor networks (WSN), UWSNs consist of sensor nodes that collect the information and pass it to a base station; however, researchers have to face many challenges in executing the network in an aquatic medium. Energy-Efficient Underwater Wireless Communications and Networking is a crucial reference source that covers existing and future possibilities of the area as well as the current challenges presented in the implementation of underwater sensor networks. While highlighting topics such as digital signal processing, underwater localization, and acoustic channel modeling, this publication is ideally designed for machine learning experts, IT specialists, government agencies, oceanic engineers, communication experts, researchers, academicians, students, and environmental agencies concerned with optimized data flow in communication network, securing assets, and mitigating security attacks.

Smart Cities Performability, Cognition, & Security

Author : Fadi Al-Turjman
Publisher : Springer
ISBN 13 : 3030147185
Total Pages : 248 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Smart Cities Performability, Cognition, & Security by : Fadi Al-Turjman

Download or read book Smart Cities Performability, Cognition, & Security written by Fadi Al-Turjman and published by Springer. This book was released on 2019-05-21 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides knowledge into the intelligence and security areas of smart-city paradigms. It focuses on connected computing devices, mechanical and digital machines, objects, and/or people that are provided with unique identifiers. The authors discuss the ability to transmit data over a wireless network without requiring human-to-human or human-to-computer interaction via secure/intelligent methods. The authors also provide a strong foundation for researchers to advance further in the assessment domain of these topics in the IoT era. The aim of this book is hence to focus on both the design and implementation aspects of the intelligence and security approaches in smart city applications that are enabled and supported by the IoT paradigms. Presents research related to cognitive computing and secured telecommunication paradigms; Discusses development of intelligent outdoor monitoring systems via wireless sensing technologies; With contributions from researchers, scientists, engineers and practitioners in telecommunication and smart cities.

Artificial Intelligent Techniques for Wireless Communication and Networking

Author : R. Kanthavel
Publisher : John Wiley & Sons
ISBN 13 : 1119821789
Total Pages : 388 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Artificial Intelligent Techniques for Wireless Communication and Networking by : R. Kanthavel

Download or read book Artificial Intelligent Techniques for Wireless Communication and Networking written by R. Kanthavel and published by John Wiley & Sons. This book was released on 2022-02-24 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.

Intelligent Wireless Communications

Author : George Mastorakis
Publisher : IET
ISBN 13 : 1839530952
Total Pages : 452 pages
Book Rating : 4.8/5 (395 download)

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Book Synopsis Intelligent Wireless Communications by : George Mastorakis

Download or read book Intelligent Wireless Communications written by George Mastorakis and published by IET. This book was released on 2021-04-21 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Aimed at researchers, engineers and scientists involved in the design and development of protocols and AI applications for wireless communication devices and networks, this edited book presents recent research and innovations in emerging AI methods and AI-powered mechanisms, and future perspectives in this field.

Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

Author : E. S. Gopi
Publisher : Springer Nature
ISBN 13 : 9811602891
Total Pages : 643 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication by : E. S. Gopi

Download or read book Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication written by E. S. Gopi and published by Springer Nature. This book was released on 2021-05-28 with total page 643 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.

Deep Reinforcement Learning for Wireless Communications and Networking

Author : Dinh Thai Hoang
Publisher : John Wiley & Sons
ISBN 13 : 1119873673
Total Pages : 293 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Deep Reinforcement Learning for Wireless Communications and Networking by : Dinh Thai Hoang

Download or read book Deep Reinforcement Learning for Wireless Communications and Networking written by Dinh Thai Hoang and published by John Wiley & Sons. This book was released on 2023-08-01 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Networking

Author : Éric Renault
Publisher : Springer
ISBN 13 : 3030199452
Total Pages : 400 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Machine Learning for Networking by : Éric Renault

Download or read book Machine Learning for Networking written by Éric Renault and published by Springer. This book was released on 2019-05-10 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

Interference Alignment

Author : Syed A. Jafar
Publisher : Now Publishers Inc
ISBN 13 : 160198474X
Total Pages : 147 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Interference Alignment by : Syed A. Jafar

Download or read book Interference Alignment written by Syed A. Jafar and published by Now Publishers Inc. This book was released on 2011 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interference Alignment: A New Look at Signal Dimensions in a Communication Network provides both a tutorial and a survey of the state-of-art on the topic.

Applications of Machine Learning in Wireless Communications

Author : Ruisi He
Publisher : Institution of Engineering and Technology
ISBN 13 : 1785616579
Total Pages : 491 pages
Book Rating : 4.7/5 (856 download)

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Book Synopsis Applications of Machine Learning in Wireless Communications by : Ruisi He

Download or read book Applications of Machine Learning in Wireless Communications written by Ruisi He and published by Institution of Engineering and Technology. This book was released on 2019-06-20 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

2021 IEEE Global Communications Conference (GLOBECOM)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781728181059
Total Pages : pages
Book Rating : 4.1/5 (81 download)

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Book Synopsis 2021 IEEE Global Communications Conference (GLOBECOM) by : IEEE Staff

Download or read book 2021 IEEE Global Communications Conference (GLOBECOM) written by IEEE Staff and published by . This book was released on 2021-12-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: All topics relating to communications and networking technologies

Deep Reinforcement Learning

Author : Mohit Sewak
Publisher : Springer
ISBN 13 : 9811382859
Total Pages : 215 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Deep Reinforcement Learning by : Mohit Sewak

Download or read book Deep Reinforcement Learning written by Mohit Sewak and published by Springer. This book was released on 2019-06-27 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.

Deep Reinforcement Learning in Action

Author : Alexander Zai
Publisher : Manning
ISBN 13 : 1617295434
Total Pages : 381 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Deep Reinforcement Learning in Action by : Alexander Zai

Download or read book Deep Reinforcement Learning in Action written by Alexander Zai and published by Manning. This book was released on 2020-04-28 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap