Author: Antonino Masaracchia Publisher: ISBN: 9781839536410 Category : Technology & Engineering Languages : en Pages : 0
Book Description
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.
Author: Bhargav Appasani Publisher: Springer Nature ISBN: 3031385063 Category : Technology & Engineering Languages : en Pages : 422
Book Description
This book is the first on Smart Grid 3.0. The book presents literature reviews of recent computational and communication technologies and their application in the evolution of smart grids to Smart Grid 3.0. It offers new control solutions, architectures and energy management strategies that are based on artificial intelligence and deep learning techniques. The book details the hardware and software implementation of fault identification or detection based on synchrophasor data and machine learning. It also discusses blockchain architectures for smart grid applications such as electric vehicles, home automation and automatic metering infrastructure.
Author: Qingqing Wu Publisher: Springer Nature ISBN: 3031441729 Category : Technology & Engineering Languages : en Pages : 188
Book Description
This book provides comprehensive insights into the theory, models, and unique characteristics of intelligent reflecting surface (IRS) and its appealing applications in wireless information and power transmission for B5G/6G. It is organized as follows: Chapter 1 gives an overview of the fundamentals of IRS, including the signal and channel models of IRS, its hardware architecture, advantages and practical constraints. Chapter 2, 3 and 4 focus on how to optimize the joint design of active and passive beamforming for three types of IRS-aided wireless information/energy transmission systems, namely IRS-aided power transfer (WPT), IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication networks (WPCN). Chapter 2 specifically presents a general framework design for IRS-aided WPT systems from the perspectives of energy beamforming design and channel estimation, while the dynamic IRS beamforming design is also discussed. Chapter 3 thoroughly studies the IRS-aided SWIPT for achieving better R-E trade-off, wherein two different designs based on weighted sum-power maximization at energy users (EUs) and under individual quality-of-service (QoS), requirements at both information users (IUs) and EUs are investigated. Chapter 4 presents three paradigms of IRS-aided WPCN, i.e., IRS-aided half-duplex (HD) WPCN, IRS-aided full-duplex (FD) WCPN and IRS-aided multiple-input-multiple-output (MIMO) FD WPCN. The authors present some related emerging topics, e.g., active IRS aided wireless communications and IRS-aided unmanned aerial vehicle (UAV) communications in chapter 5. This book targets postgraduate students studying electrical engineering and computer science, and can be used as a secondary textbook. Professionals, researchers and engineers working in wireless networks will also find this book useful as a reference.
Author: Hongliang Zhang Publisher: Springer Nature ISBN: 3030734994 Category : Computers Languages : en Pages : 260
Book Description
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.
Author: Kazuki Maruta Publisher: MDPI ISBN: 3039360167 Category : Technology & Engineering Languages : en Pages : 330
Book Description
Multiple-input, multiple-output (MIMO), which transmits multiple data streams via multiple antenna elements, is one of the most attractive technologies in the wireless communication field. Its extension, called ‘massive MIMO’ or ‘large-scale MIMO’, in which base station has over one hundred of the antenna elements, is now seen as a promising candidate to realize 5G and beyond, as well as 6G mobile communications. It has been the first decade since its fundamental concept emerged. This Special Issue consists of 19 papers and each of them focuses on a popular topic related to massive MIMO systems, e.g. analog/digital hybrid signal processing, antenna fabrication, and machine learning incorporation. These achievements could boost its realization and deepen the academic and industrial knowledge of this field.
Author: Choong Seon Hong Publisher: Springer Nature ISBN: 9811649634 Category : Computers Languages : en Pages : 257
Book Description
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: Yonina C. Eldar Publisher: Cambridge University Press ISBN: 1108967736 Category : Technology & Engineering Languages : en Pages : 560
Book Description
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: Antonino Masaracchia
Author: Alice Faisal
Author: Khoi Khac Nguyen
Author: Bhargav Appasani
Author: Qingqing Wu
Author: Hongliang Zhang
Author: Yevgeni Koucheryavy
Author: Kazuki Maruta
Author: Choong Seon Hong
Author: Yonina C. Eldar