Radio Frequency Fingerprint Identification (RFFI), which exploits non-ideal hardware-induced unique distortion resident in the transmit signals to identify an emitter, is emerging as a means to enhance the security of communication systems. Recently, machine learning has achieved great success in developing state-of-the-art RFFI models. However, few works consider cross-receiver RFFI problems, where the RFFI model is trained and deployed on different receivers. Due to altered receiver characteristics, direct deployment of RFFI model on a new receiver leads to significant performance degradation. To address this issue, we formulate the cross-receiver RFFI as a model adaptation problem, which adapts the trained model to unlabeled signals from a new receiver. We first develop a theoretical generalization error bound for the adaptation model. Motivated by the bound, we propose a novel method to solve the cross-receiver RFFI problem, which includes domain alignment and adaptive pseudo-labeling. The former aims at finding a feature space where both domains exhibit similar distributions, effectively reducing the domain discrepancy. Meanwhile, the latter employs a dynamic pseudo-labeling scheme to implicitly transfer the label information from the labeled receiver to the new receiver. Experimental results indicate that the proposed method can effectively mitigate the receiver impact and improve the cross-receiver RFFI performance.

翻译：射频指纹识别（RFFI）利用发射信号中由非理想硬件引起的独特畸变来识别辐射源，正逐渐成为增强通信系统安全性的手段。近年来，机器学习在开发最先进的RFFI模型方面取得了巨大成功。然而，很少有研究关注跨接收机RFFI问题，即RFFI模型在不同接收机上训练和部署。由于接收机特性的改变，直接在新接收机上部署RFFI模型会导致性能显著下降。为解决这一问题，我们将跨接收机RFFI建模为模型自适应问题，使训练好的模型适应来自新接收机的无标签信号。我们首先推导出自适应模型的理论泛化误差界。受该界的启发，我们提出了一种解决跨接收机RFFI问题的新方法，包括域对齐和自适应伪标签。前者旨在寻找一个特征空间，使两个域在该空间中呈现相似分布，从而有效减小域差异；后者则采用动态伪标签机制，将标签信息从有标签接收机隐式传递至新接收机。实验结果表明，该方法能有效缓解接收机影响并提升跨接收机RFFI性能。