Tracking angle-of-arrival (AoA) under mobility enables proactive beam alignment in multi-antenna wireless systems. However, learning this evolution directly from raw I/Q data is challenging because multipath, channel-gain variations, hardware impairments, and noise mix with angular information. This paper proposes \emp...
Wireless foundation models learn representations from unlabeled radio signals for reuse across downstream tasks. Scaling model capacity is a common strategy for learning richer representations and improving downstream performance. However, its gains are less consistent in wireless self-supervised learning when pretrain...
Ahmed P. Mohamed, Ahmed Aboulfotouh, Hatem Abou-Zeid· 0 citations
This analysis shows that current WFMs provide increasing evidence of reusable wireless representations, but this evidence varies considerably across task families and evaluation settings, and identifies open directions for improving data availability, evaluation rigor, generalization, efficient adaptation, and real-wor...
Alonso M. Pacheco Huachaca, J. J. Rodríguez Rodríguez, Ahmed Aboulfotouh et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
