TY  - JOUR
AU  - Oukebdane, Mohammed Anis
AU  - Shah, A. F. M. Shahen
PY  - 2025
DA  - 2025/07/28
TI  - Computer Vision-Powered 6G Networks: Technologies, Applications, and Challenges
JO  - ICCK Transactions on Mobile and Wireless Intelligence
T2  - ICCK Transactions on Mobile and Wireless Intelligence
JF  - ICCK Transactions on Mobile and Wireless Intelligence
VL  - 1
IS  - 1
SP  - 19
EP  - 31
DO  - 10.62762/TMWI.2025.159776
UR  - https://www.icck.org/article/abs/TMWI.2025.159776
KW  - computer vision
KW  - 6G wireless networks
KW  - FAS
KW  - edge intelligence
KW  - semantic communication
KW  - vision-aided 6G applications
AB  - 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 possibility of incorporating new technologies with CV to make it more effective, such as fluid antenna system (FAS) and fluid antenna multiple access (FAMA). The study shows that CV integration improves fundamental 6G capabilities like beamforming, mobility prediction, localisation, semantic communication, and immersive control. It also reveals limits in real-time inference under URLLC constraints, data scarcity, and energy economy, though. This work presents a unified basis for advancing CV-native 6G networks by spotting open challenges and suggesting a roadmap including generative perception, collaborative intelligence, and green vision computing.
SN  - 3069-0692
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Oukebdane2025Computer,
  author = {Mohammed Anis Oukebdane and A. F. M. Shahen Shah},
  title = {Computer Vision-Powered 6G Networks: Technologies, Applications, and Challenges},
  journal = {ICCK Transactions on Mobile and Wireless Intelligence},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {19-31},
  doi = {10.62762/TMWI.2025.159776},
  url = {https://www.icck.org/article/abs/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 possibility of incorporating new technologies with CV to make it more effective, such as fluid antenna system (FAS) and fluid antenna multiple access (FAMA). The study shows that CV integration improves fundamental 6G capabilities like beamforming, mobility prediction, localisation, semantic communication, and immersive control. It also reveals limits in real-time inference under URLLC constraints, data scarcity, and energy economy, though. This work presents a unified basis for advancing CV-native 6G networks by spotting open challenges and suggesting a roadmap including generative perception, collaborative intelligence, and green vision computing.},
  keywords = {computer vision, 6G wireless networks, FAS, edge intelligence, semantic communication, vision-aided 6G applications},
  issn = {3069-0692},
  publisher = {Institute of Central Computation and Knowledge}
}