Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification
This addresses data scarcity issues for wireless security applications, though it appears incremental as it builds on existing contrastive learning and channel estimation techniques.
The paper tackled the problem of limited data samples for deploying pre-trained Radio Frequency Fingerprint Identification models in unseen environments by proposing a residual channel-based data augmentation strategy with a lightweight SimSiam contrastive learning framework, achieving significant enhancements in feature extraction and generalization while requiring only 1% samples for fine-tuning.
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.