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 pretraining data are limited. We investigate objective diversity as an alternative scaling axis: different self-supervised objectives emphasize different signal properties, and combining their representations can preserve more information to enable diverse tasks. We develop a fusion framework that combines frozen representations from independent encoders trained through reconstructi...
| Authors | Ahmed Mohamed, Ahmed Aboulfotouh, Hatem Abou-Zeid |
|---|---|
| Primary category | eess.SP |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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