Название: Machine Learning for Radio Resource Management and Optimization in 5G and Beyond
Автор: Mariyam Ouaissa, Mariya Ouaissa, Hanane Lamaazi, Khadija Slimani, Ihtiram Raza Khan, B. Sundaravadivazhagan
Издательство: CRC Press
Год: 2025
Страниц: 249
Язык: английский
Формат: pdf (true), epub
Размер: 12.3 MB

Machine Learning for Radio Resource Management and Optimization in 5G and Beyond highlights a new line of research that uses innovative technologies and methods based on Artificial Intelligence/Machine Learning techniques to address issues and challenges related to radio resource management in 5G and 6G communication systems. This book provides comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, network slicing, and resource optimization for 5G wireless networks and beyond.

Artificial Intelligence/Machine Learning (AI/ML) approaches are promising tools to tackle the big challenges in wireless communications networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency, flexibility, compatibility, quality of experience, and silicon convergence. ML techniques can provide intelligent communication designs while addressing various problems ranging from signal detection, to channel modeling, network optimization, resource management, and multiple access.

This book offers an introduction to recent trends regarding 5G toward 6G communication networks. Moreover, it provides an overview of theoretical concepts and techniques of AI/ML used to meet the requirements and the challenges of radio resource management and optimization. This book presents comprehensive coverage of current and emerging waveform design, channel modeling, multiple access, random access, scheduling, and resource optimization for 5G wireless networks and beyond.

Reinforcement Learning (RL) has emerged as a promising approach for intelligent scheduling in 5G networks and beyond. RL is a Machine Learning (ML) technique that allows an agent to learn an optimal policy by interacting with its environment and receiving rewards or penalties based on its actions. By leveraging the power of RL, intelligent scheduling algorithms can potentially achieve significant performance gains and enable more efficient resource utilization in 5G networks and beyond.

This book provides a comprehensive reference for researchers, scholars, and industry professionals in different fields related to mobile networks and intelligent systems. It can also be a hands-on resource for students interested in the fields of cellular networks (5G/6G) and computational intelligence.

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