Unleashing the Potential of Self-Supervised RF Learning With Group Shuffle

Abstract

Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integrating group shuffle and asymmetry augmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.