Sotirios K. Goudos – författare

An exploration of connected intelligent edge, artificial intelligence, and machine learning for B5G/6G architecture Artificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT). The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks. Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications. Some of the topics include: Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systemsChannel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniquesResource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MECAntenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modelingIdentifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study.

An exploration of connected intelligent edge, artificial intelligence, and machine learning for B5G/6G architecture

Artificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT).

The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks.

Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications.

Some of the topics explored in Artificial Intelligence for Future Networks include:

Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systems Channel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniques Resource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MEC Antenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modeling

Identifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study.

An exploration of connected intelligent edge, artificial intelligence, and machine learning for B5G/6G architecture

Artificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT).

The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks.

Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications.

Some of the topics include:

Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systemsChannel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniquesResource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MECAntenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modeling

Identifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study.

Several evolutionary algorithms (EAs) have emerged in recent decades that mimic the behaviour and evolution of biological entities. EAs are widely used to solve single and multi-objective optimization engineering problems. EAs have also been applied to a variety of microwave components, antenna design, radar design, and wireless communications problems. These techniques, among others, include genetic algorithms (GAs), evolution strategies (ES), particle swarm optimization (PSO), differential evolution (DE), and ant colony optimization (ACO). In addition, new innovative algorithms that are not only biology-based but also physics-based or music-based are also emerging, as are hybrid combinations of EAs.The use of evolutionary algorithms is having an increasing impact on antenna design and wireless communications problems. EAs combined with numerical methods in electromagnetics have obtained significant and successful results.This book aims to present some of the emerging EAs and their variants. Chapter 1 introduces the optimization methods in general and the evolutionary algorithms. Chapter 2 presents briefly some of the most popular evolutionary algorithms, such as particle swarm optimization (PSO), differential evolution (DE), and ant colony optimization (ACO) as well as some emerging ones. Chapter 3 focuses on antenna array synthesis, which constitutes a wide range of antenna design problems. Chapter 4 gives an overview of patch antenna design using evolutionary algorithms. Chapter 5 presents design cases from different microwave structure cases. Chapter 6 discusses on various representative design problems in wireless communications. Chapter 7 deals with design cases for 5G and beyond.

As populations rise and cities draw ever-larger populations, smart cities are emerging around the world to cater for the need for sustainable urban development. The exploration of recent advances in information and communication technology (ICT), sensing and energy engineering offers promising enablers for creating, implementing, and promoting sustainable development strategies to address the challenges of this expanding urbanization.The aim of this book is to equip readers with the conceptual and practical knowledge they need to comprehend and put into practice the technological concepts involved in the design, development, and management of smart cities. The authors explore enabling technologies, capabilities, solutions and strategies to support sustainable smart cities including smart homes, zero energy buildings, smart sensing, Internet of Things (IoT), autonomous IoT, secure IoT, federated learning, data security, network security, privacy-preserving and decentralized intelligence, clean hydrogen and energy harvesting technologies.With contributions from global experts, this reference is a valuable resource for researchers, engineers, technologists and urban planners in the fields of smart city and urban sustainable development. Lecturers and advanced students will find the book useful as a course reference and for wider reading.