  In this work, we present a wireless localization method that operates on
self-supervised and unlabeled channel estimates. Our self-supervising method
learns general-purpose channel features robust to fading and system
impairments. Learned representations are easily transferable to new
environments and ready to use for other wireless downstream tasks. To the best
of our knowledge, the proposed method is the first joint-embedding
self-supervised approach to forsake the dependency on contrastive channel
estimates. Our approach outperforms fully-supervised techniques in small data
regimes under fine-tuning and, in some cases, linear evaluation. We assess the
performance in centralized and distributed massive MIMO systems for multiple
datasets. Moreover, our method works indoors and outdoors without additional
assumptions or design changes.
