Deep Reinforcement Learning For Wireless Networks

Author : F. Richard Yu,Ying He
Publisher : Springer
Page : 71 pages
File Size : 48,8 Mb
Release : 2019-01-17
Category : Technology & Engineering
ISBN : 9783030105464

Get Book

Deep Reinforcement Learning for Wireless Networks by F. Richard Yu,Ying He Pdf

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.

Author : Dinh Thai Hoang,Nguyen Van Huynh,Diep N. Nguyen,Ekram Hossain,Dusit Niyato
Publisher : John Wiley & Sons
Page : 293 pages
File Size : 49,6 Mb
Release : 2023-08-01
Category : Technology & Engineering
ISBN : 9781119873679

Get Book

Deep Reinforcement Learning for Wireless Communications and Networking by Dinh Thai Hoang,Nguyen Van Huynh,Diep N. Nguyen,Ekram Hossain,Dusit Niyato Pdf

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.

Author : Krishna Kant Singh,Akansha Singh,Korhan Cengiz,Dac-Nhuong Le
Publisher : John Wiley & Sons
Page : 272 pages
File Size : 42,6 Mb
Release : 2020-07-08
Category : Computers
ISBN : 9781119640363

Get Book

Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks by Krishna Kant Singh,Akansha Singh,Korhan Cengiz,Dac-Nhuong Le Pdf

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.

Author : K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching
Publisher : CRC Press
Page : 270 pages
File Size : 53,9 Mb
Release : 2021-09-14
Category : Technology & Engineering
ISBN : 9781000441857

Get Book

Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems by K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching Pdf

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.

Author : Yao Sun,Chaoqun You,Gang Feng,Lei Zhang
Publisher : John Wiley & Sons
Page : 324 pages
File Size : 50,7 Mb
Release : 2023-12-27
Category : Technology & Engineering
ISBN : 9781119913894

Get Book

Federated Learning for Future Intelligent Wireless Networks by Yao Sun,Chaoqun You,Gang Feng,Lei Zhang Pdf

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.

Author : Yonina C. Eldar,Andrea Goldsmith,Deniz Gündüz,H. Vincent Poor
Publisher : Cambridge University Press
Page : 560 pages
File Size : 48,6 Mb
Release : 2022-06-30
Category : Technology & Engineering
ISBN : 9781108967730

Get Book

Machine Learning and Wireless Communications by Yonina C. Eldar,Andrea Goldsmith,Deniz Gündüz,H. Vincent Poor Pdf

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.

Author : Fa-Long Luo
Publisher : John Wiley & Sons
Page : 490 pages
File Size : 42,8 Mb
Release : 2020-02-10
Category : Technology & Engineering
ISBN : 9781119562252

Get Book

Machine Learning for Future Wireless Communications by Fa-Long Luo Pdf

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.

Author : Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han
Publisher : Springer Nature
Page : 257 pages
File Size : 46,5 Mb
Release : 2022-01-01
Category : Computers
ISBN : 9789811649639

Get Book

Federated Learning for Wireless Networks by Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han Pdf

Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.

Author : Alice Faisal
Publisher : Springer Nature
Page : 64 pages
File Size : 47,6 Mb
Release : 2024-07-01
Category : Electronic
ISBN : 9783031525544

Get Book

Reinforcement Learning for Reconfigurable Intelligent Surfaces by Alice Faisal Pdf

Author : Dinh Thai Hoang,Nguyen Van Huynh,Diep N. Nguyen,Ekram Hossain,Dusit Niyato
Publisher : John Wiley & Sons
Page : 293 pages
File Size : 50,8 Mb
Release : 2023-06-30
Category : Technology & Engineering
ISBN : 9781119873730

Get Book

Deep Reinforcement Learning for Wireless Communications and Networking by Dinh Thai Hoang,Nguyen Van Huynh,Diep N. Nguyen,Ekram Hossain,Dusit Niyato Pdf

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.

Author : Fadi Al-Turjman
Publisher : Springer
Page : 248 pages
File Size : 46,5 Mb
Release : 2019-05-21
Category : Technology & Engineering
ISBN : 9783030147181

Get Book

Smart Cities Performability, Cognition, & Security by Fadi Al-Turjman Pdf

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.

Author : Goyal, Nitin,Sapra, Luxmi,Sandhu, Jasminder Kaur
Publisher : IGI Global
Page : 339 pages
File Size : 48,6 Mb
Release : 2020-09-04
Category : Technology & Engineering
ISBN : 9781799836421

Get Book

Energy-Efficient Underwater Wireless Communications and Networking by Goyal, Nitin,Sapra, Luxmi,Sandhu, Jasminder Kaur Pdf

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.

Author : Com?a, Ioan-Sorin,Trestian, Ramona
Publisher : IGI Global
Page : 356 pages
File Size : 53,8 Mb
Release : 2019-01-25
Category : Technology & Engineering
ISBN : 9781522574590

Get Book

Next-Generation Wireless Networks Meet Advanced Machine Learning Applications by Com?a, Ioan-Sorin,Trestian, Ramona Pdf

The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.

Author : Zhiyong Du,Bin Jiang,Qihui Wu,Yuhua Xu,Kun Xu
Publisher : Springer Nature
Page : 136 pages
File Size : 51,6 Mb
Release : 2019-11-06
Category : Technology & Engineering
ISBN : 9789811511202

Get Book

Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks by Zhiyong Du,Bin Jiang,Qihui Wu,Yuhua Xu,Kun Xu Pdf

This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond.

Author : Syed A. Jafar
Publisher : Now Publishers Inc
Page : 147 pages
File Size : 41,5 Mb
Release : 2011
Category : Computers
ISBN : 9781601984746

Get Book

Interference Alignment by Syed A. Jafar Pdf

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.