Preprint Open access
Scaling of Wireless Foundation Models via Representation Diversity and Multi-Branch Architectures
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 …