Machine Learning for Wireless Communication (Synthesis Lectures on Communications) - Tapa dura

Acerca del autor

Dr. Rohit Thanki is a seasoned AI researcher and data scientist with over 12 years of scientific research experience and over 5 years in AI-powered MedTech startups. He held leadership roles such as Head of R&D at Prognica Labs, Dubai, and worked as a Software Consultant at Ennoventure Technologies, India. He earned his Ph.D. in biometric security and data encryption from C. U. Shah University, Gujarat, India. He has since mentored several Ph.D. and master's research students across institutions in Germany and India. His expertise spans medical image analysis, artificial intelligence, machine learning, computer vision, digital watermarking, content security, and signal processing. He has led AI projects involving a variety of medical imaging modalities, including X-ray, MRI, CT, ultrasound, and mammography. Stanford University and Elsevier recognized Dr. Thanki among the Top 2% of AI and image processing scientists in 2024. He has authored over 20 technical books (16 of which are indexed in Scopus) and published more than 100 research articles in reputed journals and conferences indexed in Scopus and the Web of Science. His work has been cited over 2,400 times and has an h-index of 23. Dr. Thanki is an active Senior Member of IEEE and the German AI Association. He serves on editorial boards for several international journals, including BMC Digital Health (Springer Nature) and PLOS ONE. He is also a frequent reviewer for top-tier journals such as IEEE Access, Pattern Recognition, and the IEEE Journal of Biomedical and Health Informatics. His current research focuses on integrating AI in medical diagnostics, explaining AI in healthcare, and using cryptographic techniques for medical data security. He is passionate about bridging clinical practice with cutting-edge AI technology to enhance diagnostic accuracy and patient outcomes.

Dr. Komal Borisagar is working as a associate professor at Gujarat Technological University (State University) in the department named Graduate School of Engineering and Technology, Ahmedabad. She has obtained her Ph.D. in Speech Enhancement Techniques for Digital Hearing Aids. Her areas of interest are wireless communication, sensor networks, signal processing, signals & systems and Internet of Things. She has teaching experience of over 20 years. She has published 5 books, 4 book chapters and more than 70 research papers to her credit in referred & indexed journals, conferences at international and in IEEE digital library. She has achieved best paper award five times for her research articles and presentation. She is awarded with “Best Women Engineer Award” in 2019 by Indian Society of Technical Education, Gujarat. She is handling project and research in the filed of IoT and Wireless Communication. 

Dr. Anjali Diwan is a highly experienced academic and software industry professionalwith over 20 years of expertise. Her areas of academic and research interests include Machine Learning, Image Processing, Artificial Intelligence, Deep Learning, Data Security, Multimedia Forensics, and the application of technologies to address humanitarian challenges. She is a senior member of IEEE and currently serves as a member of the SAC team of IEEE R10 (2023-2024) and the Section Chair of the IEEE Young Professionals affinity group of Gujarat section (2022-2024). Previously, she served as the Section Chair of Student Activity for IEEE Gujarat from 2016 to 2019 and IEEE WIE affinity group co-chair of IEEE Gujarat section from 2014 to 2017. Additionally, Dr. Diwan is a member of the TPC committee of several IEEE conferences and serves as a reviewer for international journals and IEEE transection. Currently she is working as faculty member of CE-AI/Big data department of Marwadi University, Rajkot (Gujarat) in India.

De la contraportada

This book covers the basic principles of wireless communication while delving into the fundamentals of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. The authors provide real-world examples and case studies to illustrate the use of machine learning in wireless communication applications such as channel estimation, mobility prediction, resource allocation, and beamforming. This book is an essential resource for researchers, engineers, and students interested in understanding and applying machine learning techniques in the context of wireless communication systems.

In addition, this book:

• Describes the significance of machine learning in wireless communication
• Includes case studies in channel estimation, mobility prediction, resource allocation, and beamforming
• Provides detail on various machine learning algorithms used for applications