Machine Learning for Mobile Communications Industry 5.0 Series

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.

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.

Sinh Cong Lam received a Bachelor of Electronics and Telecommunication (Honours) and Master of Electronic Engineering in 2010 and 2012, respectively, from University of Engineering and Technology, Vietnam National University (UET, VNUH). He obtained his Ph.D. degree from the University of Technology, Sydney, Australia. He is currently with the Faculty of Electronics and Telecommunications, VNU University of Engineering and Technology, Vietnam. His research interests focus on modeling, performance analysis and optimization for cellular networks, stochastic geometry model for wireless communications.

Chiranji Lal Chowdhary is an associate professor in the School of Information Technology & Engineering at the Vellore Institute of Technology (VIT) in Vellore, India, where he has been since 2010. He received a B.E. (CSE) from MBM Engineering College at Jodhpur in 2001, and M. Tech. (CSE) from the M.S. Ramaiah Institute of Technology at Bangalore in 2008. He received his Ph.D. in Information Technology and Engineering from the VIT University Vellore in 2017. From 2006 to 2010 he worked at M.S. Ramaiah Institute of Technology in Bangalore, eventually as a Lecturer. His research interests span both computer vision and image processing.

Tushar Hrishikesh Jaware holds a bachelor's degree in electronics and telecommunication engineering from North Maharashtra University, Jalgaon. He further pursued a master's degree in digital electronics and obtained a Ph.D. in medical image processing from Sant Gadge Baba Amravati University, Amravati. Currently serving as the Dean of Research and Development at the R. C. Patel Institute of Technology in Shirpur, Maharashtra, India, Dr. Jaware possesses over 18 years of invaluable teaching experience.

Subrata Chowdhury is working in the Department of the Computer Science of Engineering of Sreenivasa Institute of Technology and Management as an associate professor. He has been w