残差信道提升射频指纹识别的对比学习
Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification
AI总结：
本文提出基于残差信道的数据增强与轻量级SimSiam对比学习框架，解决射频指纹识别中数据样本有限问题，提升模型泛化能力并减少样本与时间需求。
摘要：
为解决预训练模型在未见环境中部署时数据样本有限的问题，本文提出一种基于残差信道的数据增强策略，用于射频指纹识别（RFFI），并结合轻量级SimSiam对比学习框架。通过应用最小二乘（LS）和最小均方误差（MMSE）信道估计后进行均衡，生成具有不同残差信道效应的信号。这些残差信道使模型能够学习更有效的表征。随后，在新环境中使用1%的样本对预训练模型进行微调以进行RFFI。实验结果表明，我们的方法显著增强了特征提取能力和泛化能力，同时所需样本更少、时间更短，适用于实际无线安全应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
