1st Edition
Machine Learning for Mobile Communications
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
Shaifali Garg, Shashi Kant Gupta, A. Deivasree Anbu, and Anchal Pathak
2: NR Physical Layer
Mirdula K, Chandrakumar T, Mohd Asif Shah, and Duc-Tan Tran
3: NR Layer 2 and Layer 3
P. Prasant, D. Saravanan, J. Sangeethapriya, and Moresh Mukhedkar
4: 4G and 5G NR Core Network Architecture
K. Gowri, V. Kavitha, Abolfazl Mehbodniya, and Subrata Chowdhury
5: 5G—Further Evolution
Avanthica Sri M M, Chandrakumar T, Gautam Srivastava, and Subrata Chowdhury
6: Security and Privacy
Om Prakash, Saumya Das, Sreejith L Das, Satheesh Kumar Jaganathan, and T Somassoundaram
7: Traffic Prediction and Congestion Control Using Regression Models in Machine Learning for Cellular Technology
R. Madonna Arieth, Subrata Chowdhury, B. Sundaravadivazhagan, and Gautam Srivastava
8: Resource Allocation Optimization
Monika K, Chandrakumar T, B. Sundaravadivazhagan, and Ramya Govindaraj
9: Reciprocated Bayesian-Rnn Classifier-Based Mode Switching and Mobility Management in Mobile Networks
Shashi Kant Gupta, Anchal Pathak, Sultanuddin SJ, and Nupur Soni
10: Mobility Management through Machine Learning
Nallakaruppan M K, Siva Rama Krishnan S, Ramya G, Abdul Rehman Javed, Ishita Johri, and Sweta Bhattacharya
11: Applying Heuristic Methods to the Offloading Problem in Edge Computing
Trong-Minh Hoang, Thu-Trang Ngo Thi, Hong-Hue Nguyen Thi, Duc-Minh Tran, and Nam-Hoang Nguyen
Chapter 12: AR/VR Data Prediction and Slicing Model for 5G Edge Computing
Vithya Ganesan, Viriyala Sri Anima Padmini, V. Anjana Devi, Subrata Chowdhury, Gautam Shrivastava, and Kassian T.T. Amesho
Biography
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 working in the IT Industry for more than 5 years in the R&D developments, he has handled many projects in the industry with much dedications and perfect time limits. He has been handling projects related to AI, Blockchains and the Cloud Computing for the companies from various National and Internationals Clients.
