Machine Learning for Mobile Communications

Sinh Cong Lam,Chiranji Lal Chowdhary,Tushar Hrishikesh Jaware,Subrata Chowdhury

• Publisher: CRC Press
• Year: 2024
• ISBN:
  9781040034415
• Edition: 1
• Topic: Technik & Maschinenbau
• Subtopic: Elektrotechnik & Telekommunikation

Machine Learning for Mobile Communications will take readers on a journey from basic to advanced knowledge about mobile communications and machine learning. For learners at the basic level, this book volume discusses a wide range of mobile communications topics from the system level, such as system design and optimization, to the user level, such as power control and resource allocation. The authors also review state-of-the-art machine learning, one of the biggest emerging trends in both academia and industry. For learners at the advanced level, this book discusses solutions for long-term problems with future mobile communications such as resource allocation, security, power control, and spectral efficiency. The book brings together some of the top mobile communications and machine learning experts throughout the world, who contributed their knowledge and experience regarding system design and optimization.

This book:

• Discusses the 5G new radio system design and architecture as specified in 3GPP documents
• Highlights the challenges including security and privacy, energy, and spectrum efficiency from the perspective of 5G new radio systems
• Identifies both theoretical and practical problems that can occur in mobile communication systems
• Covers machine learning techniques such as autoencoder and Q-learning in a comprehensive manner
• Explores how to apply machine learning techniques to mobile systems to solve modern problems

This book is for senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Contents
• Preface
• About the Editors
• List of Contributors
• 1 Introduction to 5G New Radio
• 2 NR Physical Layer
• 3 NR Layer 2 and Layer 3
• 4 4G and 5G NR Core Network Architecture
• 5 5G—Further Evolution
• 6 Security and Privacy
• 7 Traffic Prediction and Congestion Control Using Regression Models in Machine Learning for Cellular Technology
• 8 Resource Allocation Optimization
• 9 Reciprocated Bayesian-Rnn Classifier-Based Mode Switching and Mobility Management in Mobile Networks
• 10 Mobility Management through Machine Learning
• 11 Applying Heuristic Methods to the Offloading Problem in Edge Computing
• 12 AR/VR Data Prediction and a Slicing Model for 5G Edge Computing
• Index