Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification

TL;DR

This paper introduces a residual channel-based data augmentation method combined with contrastive learning to improve radio frequency fingerprint identification, especially in data-scarce and unseen environments.

Contribution

It proposes a novel residual channel augmentation strategy and a lightweight contrastive learning framework to enhance RFFI performance with minimal data.

Findings

Significantly improves feature extraction and generalization in RFFI.

Requires only 1% of samples for fine-tuning in new environments.

Reduces training time and data requirements for effective RFFI.

Abstract

In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework. By applying least square (LS) and minimum mean square error (MMSE) channel estimations followed by equalization, signals with different residual channel effects are generated. These residual channels enable the model to learn more effective representations. Then the pre-trained model is fine-tuned with 1% samples in a novel environment for RFFI. Experimental results demonstrate that our method significantly enhances both feature extraction ability and generalization while requiring fewer samples and less time, making it suitable for practical wireless security applications.