Author
Contributions by role
Author 1
A. F. M. Shahen Shah
Yildiz Technical University, Turkey
Summary
A. F. M. Shahen Shah is an Associate Professor with the Department of Electronics and Communication Engineering and Director of the AI and Next-Generation Wireless Communication Laboratory (ANWCL) at Yildiz Technical University, Turkey. He received the B.Sc. degree in electronics and telecommunication engineering from Daffodil International University, Bangladesh, in 2009, the M.Sc. degree in information technology from the University of Dhaka, Bangladesh, in 2011, and the Ph.D. degree in electronics and communication engineering from Yildiz Technical University, Turkey, in 2020. For his Ph.D. work, Dr. Shahen won a gold medal at the 32nd International Invention, Innovation & Technology Exhibition (ITEX 2021). He has authored a book. He has published a good number of research papers in international conferences and journals. His current research interests include wireless communication, artificial intelligence, 6G, blockchain, and IoT, etc. He is a senior member of IEEE since 2019. He has been a TPC member for several international conferences and a regular reviewer for various international journals. He is currently serving as an Editor-in-Chief of ICCK Transactions on Mobile and Wireless Intelligence, and ICRRD Quality Index Research Journal, Editor of the Open Transportation Journal (Bentham) and Discover Vehicles (Springer), and Associate Editor of the Journal of Cyber Security Technology (Taylor & Francis).
Edited Journals
ICCK Contributions

Free Access | Review Article | 28 July 2025
Computer Vision-Powered 6G Networks: Technologies, Applications, and Challenges
ICCK Transactions on Mobile and Wireless Intelligence | Volume 1, Issue 1: 19-31, 2025 | DOI: 10.62762/TMWI.2025.159776
Abstract
Aiming to move from conventional throughput-centric paradigms to intelligent, context-aware systems able of perception and autonomous decision-making, sixth-generation (6G) wireless networks is seeking. Driven by recent developments in deep learning and edge artificial intelligence, computer vision (CV) proves to be a key enabler for such perceptive 6G systems. This paper offers a thorough overview bringing together the scattered terrain of CV-enabled 6G technologies. It benchmarks current models against major 6G performance criteria, evaluates architectural paradigms including federated and split learning, and presents a disciplined taxonomy of use cases. This study also notes the possibili... More >

Graphical Abstract
Computer Vision-Powered 6G Networks: Technologies, Applications, and Challenges