Unsupervised representation learning for wireless channel state information (CSI)reduces reliance on labeled data, thereby lowering annotation costs, and often improves performance on downstream tasks. However, state-of-the-art approaches take little or no account of domain-specific knowledge, forcing the model to learn well-known concepts solely from data. We introduce Sparse pretrained Radio Transformer (SpaRTran), a hybrid method based on the concept of compressed sensing for wireless channels. In contrast to existing work, SpaRTran builds around a wireless channel model that constrains the optimization procedure to physically meaningful solutions and induces a strong inductive bias. Compared to the state of the art, SpaRTran cuts positioning error by up to 28% and increases top-1 codebook selection accuracy for beamforming by 26 percentage points. Our results show that capturing the sparse nature of radio propagation as an unsupervised learning objective improves performance for network optimization and radio-localization tasks.
@article{arxiv.2505.13055,
title = {Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels},
author = {Jonathan Ott and Maximilian Stahlke and Tobias Feigl and Bjoern M. Eskofier and Christopher Mutschler},
journal= {arXiv preprint arXiv:2505.13055},
year = {2026}
}