Artificial Intelligence for Future Networks

Auteur

Mohammad Abdul Matin is a Professor in the Department of Electrical and Computer Engineering at North South University (NSU). Sotirios K Goudos is a Professor in the Department of Physics at the Aristotle University of Thessaloniki and the director of the ELEDIA@AUTH lab member of the ELEDIA Research Center Network. George K. Karagiannidis is a Professor with the Electrical and Computer Engineering Department and the Head of the Wireless Communications and Information Processing (WCIP) Group at Aristotle University in Thessaloniki, Greece.

Texte du rabat

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 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.