Contrastive Learning with Feature-Level Augmentation for Wireless Signal Representation
Siqi Mu, Shuai Chen, Yiwen Zu, Zhixi Feng, Shuyuan Yang · Electronics · 2025
The application of self-supervised learning (SSL) is increasingly imperative for advancing wireless communication technologies, particularly in scenarios with limited labeled data. Traditional data-augmentation-based SSL methods have struggled to accurately capture the intricate properties of wireless signals. This letter introduces a novel self-supervised learning framework that leverages feature-level augmentation combined with contrastive learning to enhance wireless signal recognition. Extensive experiments conducted in various environments demonstrate that the proposed method achieves improvements of more than 2.56% over the existing supervised learning (SL) methods and SSL methods on the RadioML2016.10a and ADS-B datasets. Moreover, the experimental results show that the proposed SSL pre-training strategy improves performance by 4.67% compared to supervised approaches. These results validate that the proposed method offers stronger generalization capabilities and superior performance when handling different types of wireless signal tasks.