用残余信道增强数据，让无线指纹识别模型更省样本、更快适应新环境。
Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification
- 通过残余信道生成多样化信号，提升特征学习能力。
- 仅用1%新环境样本微调，即可实现高精度识别。
- 适合资源受限的无线安全场景快速部署。
为解决预训练模型在未知环境下因数据样本不足导致部署困难的问题，本文提出一种基于残余信道的数据增强策略，结合轻量级SimSiam对比学习框架用于射频指纹识别（RFFI）。通过最小二乘（LS）和最小均方误差（MMSE）信道估计及均衡处理，生成具有不同残余信道效应的信号，使模型能够学习更有效的表征。随后在新环境中仅用1%样本对预训练模型进行微调。实验表明，该方法显著提升了特征提取能力和泛化性能，同时大幅减少所需样本量与训练时间，适用于实际无线安全应用场景。
原文摘要 · Abstract (English)
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.
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