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Reinforcement Learning For Reconfigurable Intelligent Surfaces
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Book Synopsis Reinforcement Learning for Reconfigurable Intelligent Surfaces by : Alice Faisal
Download or read book Reinforcement Learning for Reconfigurable Intelligent Surfaces written by Alice Faisal and published by Springer Nature. This book was released on with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications by : Antonino Masaracchia
Download or read book Deep Reinforcement Learning for Reconfigurable Intelligent Surfaces and UAV Empowered Smart 6G Communications written by Antonino Masaracchia and published by . This book was released on with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This co-authored book explores the many challenges arising from real-time and autonomous decision-making for 6G by covering crucial advanced signal control and real-time decision-making methods for UAV-and RIS-assisted 6G wireless communications including the serious constraints in real-time optimisation problems.
Book Synopsis Reconfigurable Intelligent Surface and UAV-assisted Communications by : Khoi Khac Nguyen
Download or read book Reconfigurable Intelligent Surface and UAV-assisted Communications written by Khoi Khac Nguyen and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reconfigurable Intelligent Surface-Empowered 6G by : Hongliang Zhang
Download or read book Reconfigurable Intelligent Surface-Empowered 6G written by Hongliang Zhang and published by Springer Nature. This book was released on 2021-05-29 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents novel RIS-Based Smart Radio techniques, targeting at achieving high-quality channel links in cellular communications via design and optimization of the RIS construction. Unlike traditional antenna arrays, three unique characteristics of the RIS will be revealed in this book. First, the built-in programmable configuration of the RIS enables analog beamforming inherently without extra hardware or signal processing. Second, the incident signals can be controlled to partly reflect and partly transmit through the RIS simultaneously, adding more flexibility to signal transmission. Third, the RIS has no digital processing capability to actively send signals nor any radio frequency (RF) components. As such, it is necessary to develop novel channel estimation and communication protocols, design joint digital and RIS-based analog beamforming schemes and perform interference control via mixed reflection and transmission. This book also investigates how to integrate the RIS to legacy communication systems. RIS techniques are further investigated in this book (benefited from its ability to actively shape the propagation environment) to achieve two types of wireless applications, i.e., RF sensing and localization. The influence of the sensing objectives on the wireless signal propagation can be potentially recognized by the receivers, which are then utilized to identify the objectives in RF sensing. Unlike traditional sensing techniques, RIS-aided sensing can actively customize the wireless channels and generate a favorable massive number of independent paths interacting with the sensing objectives. It is desirable to design RIS-based sensing algorithms, and optimize RIS configurations. For the second application, i.e., RIS aided localization, an RIS is deployed between the access point (AP) and users. The AP can then analyze reflected signals from users via different RIS configurations to obtain accurate locations of users. However, this is a challenging task due to the dynamic user topology, as well as the mutual influence between multiple users and the RIS. Therefore, the operations of the RIS, the AP, and multiple users need to be carefully coordinated. A new RIS-based localization protocol for device cooperation and an RIS configuration optimization algorithm are also required. This book targets researchers and graduate-level students focusing on communications and networks. Signal processing engineers, computer and information scientists, applied mathematicians and statisticians, who work in RIS research and development will also find this book useful.
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.
Book Synopsis Reinforcement Learning for Maritime Communications by : Liang Xiao
Download or read book Reinforcement Learning for Maritime Communications written by Liang Xiao and published by Springer Nature. This book was released on 2023-07-25 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and location privacy protection. In the IRS aided maritime communication system, the reflecting elements of IRS can be intelligently controlled to change the phase of signal, and finally enhance the received signal strength of maritime ships (or sensors) or jam maritime eavesdroppers illustrated in this book. The power and spectrum resource in maritime communications can be jointly optimized to guarantee the quality of service (i.e., security and reliability requirements), and reinforcement leaning is adopted to smartly choose the resource allocation strategy. Moreover, learning based privacy-aware offloading and location privacy protection are proposed to intelligently guarantee the privacy-preserving requirements of maritime ships or (sensors). Therefore, these communication schemes based on reinforcement learning algorithms can help maritime communication systems to improve the information security, especially in dynamic and complex maritime environments. This timely book also provides broad coverage of the maritime wireless communication issues, such as reliability, security, resource management, and privacy protection. Reinforcement learning based methods are applied to solve these issues. This book includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students. Practitioners seeking solutions to maritime wireless communication and security related issues will benefit from this book as well.
Book Synopsis Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks by : Muhammad Ali Imran
Download or read book Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks written by Muhammad Ali Imran and published by John Wiley & Sons. This book was released on 2022-12-13 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authoritative resource covering preliminary concepts and advanced concerns in the field of IRS and its role in 6G wireless systems Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks provides an in-depth treatment of the fundamental physics behind reconfigurable metasurfaces, also known as intelligent reflecting surfaces (IRS), and outlines the research roadmap towards their development as a low-complexity and energy-efficient solution aimed at turning the wireless environment into a software-defined entity. The text demonstrates IRS from different angles, including the underlying physics, hardware architecture, operating principles, and prototype designs. It enables readers to grasp the knowledge of the interplay of IRS and state-of-the-art technologies, examining the advantages, key principles, challenges, and potential use-cases. Practically, it equips readers with the fundamental knowledge of the operational principles of reconfigurable metasurfaces, resulting in its potential applications in various intelligent, autonomous future wireless communication technologies. To aid in reader comprehension, around 50 figures, tables, illustrations, and photographs to comprehensively present the material are also included. Edited by a team of highly qualified professionals in the field, sample topics covered in Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks are as follows: Evolution of antenna arrays design, introducing the fundamental principles of antenna theory and reviewing the stages of development of the field Beamforming design for IRS-assisted communications, discussing optimal IRS configuration in conjunction with overviewing novel beamforming designs Reconfigurable metasurfaces from physics to applications, discussing the working principles of tunable/reconfigurable metasurfaces and their capabilities and functionalities IRS hardware architectures, detailing the general hardware architecture of IRS and features related to the IRS’s main operational principle Wireless communication systems assisted by IRS, discussing channel characterization, system integration, and aspects related to the performance analysis and network optimization of state-of-the-art wireless applications. For students and engineers in wireless communications, microwave engineering, and radio hardware and design, Intelligent Reconfigurable Surfaces (IRS) for Prospective 6G Wireless Networks serves as an invaluable resource on the subject and is a useful course accompaniment for general Antenna Theory, Microwave Engineering, Electromagnetics courses.
Book Synopsis Intelligent Surfaces Empowered 6G Wireless Network by : Qingqing Wu
Download or read book Intelligent Surfaces Empowered 6G Wireless Network written by Qingqing Wu and published by John Wiley & Sons. This book was released on 2023-12-27 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTELLIGENT SURFACES EMPOWERED 6G WIRELESS NETWORK Integrate intelligent surfaces into the wireless networks of the future. The next generation of wireless technology (6G) promises to transform wireless communication and human interconnectivity like never before. Intelligent surface, which adopts significant numbers of small reflective surfaces to reconfigure wireless connections and improve network performance, has recently been recognized as a critical component for enabling future 6G. The next phase of wireless technology demands engineers and researchers are familiar with this technology and are able to cope with the challenges. Intelligent Surfaces Empowered 6G Wireless Network provides a thorough overview of intelligent surface technologies and their applications in wireless networks and 6G. It includes an introduction to the fundamentals of intelligent surfaces, before moving to more advanced content for engineers who understand them and look to apply them in the 6G realm. Its detailed discussion of the challenges and opportunities posed by intelligent surfaces empowered wireless networks makes it the first work of its kind. Intelligent Surfaces Empowered 6G Wireless Network readers will also find: An editorial team including the original pioneers of intelligent surface technology. Detailed coverage of subjects including MIMO, terahertz, NOMA, energy harvesting, physical layer security, computing, sensing, machine learning, and more. Discussion of hardware design, signal processing techniques, and other critical aspects of IRS engineering. Intelligent Surfaces Empowered 6G Wireless Network is a must for students, researchers, and working engineers looking to understand this vital aspect of the coming 6G revolution.
Book Synopsis Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security by : Sudeep Tanwar
Download or read book Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security written by Sudeep Tanwar and published by Springer Nature. This book was released on 2023-07-01 with total page 920 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected research papers presented at the Fourth International Conference on Computing, Communications, and Cyber-Security (IC4S 2022), organized in Ghaziabad India, during October 21–22, 2022. The conference was hosted at KEC Ghaziabad in collaboration with WSG Poland, SFU Russia, & CSRL India. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues.
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 . This book was released on 2019 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.
Book Synopsis 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) by : IEEE Staff
Download or read book 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) written by IEEE Staff and published by . This book was released on 2021-09-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference focus on the general area of signal processing advances in wireless communications It will include, but not limited to, the following topics Coding, Modulation, and Equalization Detection, Estimation and Filtering in Wireless Communications Beamforming, Precoding and Transceiver Designs of MIMO Massive and millimeter MIMO systems Full duplex Communications Energy Efficiency and Energy Harvesting Network Design and Performance Optimization Cooperative Communications and Multiuser Networks Resource Allocation and Scheduling in Wireless Networks Internet of Things and Connected Vehicles Distributed Signal Processing Localization and Tracking in Wireless Networks Machine Learning for Wireless Communications and Networking Future Trends and Emerging Technologies
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 203 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.
Book Synopsis Fundamentals of Reinforcement Learning by : Rafael Ris-Ala
Download or read book Fundamentals of Reinforcement Learning written by Rafael Ris-Ala and published by Springer Nature. This book was released on 2023-08-14 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: Understand essential AI concepts Gain professional experience Interpret sequential decision problems and solve them with reinforcement learning Learn how the Q-Learning algorithm works Practice with commented Python code Find advantageous directions
Book Synopsis Deep Reinforcement Learning by : Hao Dong
Download or read book Deep Reinforcement Learning written by Hao Dong and published by Springer Nature. This book was released on 2020-06-29 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
Book Synopsis Deep Reinforcement Learning Hands-On by : Maxim Lapan
Download or read book Deep Reinforcement Learning Hands-On written by Maxim Lapan and published by Packt Publishing Ltd. This book was released on 2018-06-21 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Book Description Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots. What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbots Who this book is for Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
Book Synopsis TensorFlow Reinforcement Learning Quick Start Guide by : Kaushik Balakrishnan
Download or read book TensorFlow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan and published by Packt Publishing Ltd. This book was released on 2019-03-30 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.
Book Synopsis Physical Layer Security by : Khoa N. Le
Download or read book Physical Layer Security written by Khoa N. Le and published by Springer Nature. This book was released on 2021-01-24 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book studies the vulnerability of wireless communications under line-of-sight (LoS) and non-LoS correlated fading environments. The authors theoretically and practically provide physical layer security analyses for several technologies and networks such as Fifth-Generation (5G) networks, Internet of Things (IoT) applications, and Non-orthogonal multiple access (NOMA). The authors have provided these under various practical scenarios, and developed theoretical aspects to validate their proposed applications. Presents physical layer security (PLS) under correlated fading environments, 5G wireless networks, and NOMA networks; Provides end-to-end analyses, combination of channel correlation and outdated CSI and their effects on PL; Includes contributions of PLS research written by global experts in academia and industry.