Scaling of Wireless Foundation Models via Representation Diversity and Multi-Branch Architectures

Excerpt

arXiv:2610.04289v1 Announce Type: cross Abstract: 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 pretraining data are limited. We investigate objective diversity as an alternative scaling axis: different self-supervised objectives emph