A Two-Stage Domain Adaptation Method Based on Data and Feature Levels for Cross-Receiver RF Fingerprint Identification

Radio Frequency Fingerprint Identification (RFFI) is a promising technology for Internet of Things (IoT) device authentication, which uses a device’s unique hardware imperfections patterns as the “fingerprint”, providing a highly secure, device-specific form of authentication for IoT devices. Due to the influence of inherent hardware imperfections of receivers, the performance of RFFI is markedly reduced when the receiver is changed. In engineering practice, when the receiver changes, the data on the new receiver needs to be relabeled and retrained, resulting in significant resource consumption. To address this limitation, a few studies utilized Domain Adaptation (DA) to avoid relabeling data on the new receiver. However, existing DA methods focused exclusively on aligning the domain distribution at the feature level, resulting in performance limitations and neglecting the utilization of unlabeled target domain data. To address these shortcomings, we propose a two-stage Domain Adaptation method for cross-receiver RFFI. The proposed method sequentially performs data-level domain alignment based on Tensor Alignment Invariant Subspace Learning and feature-level domain alignment based on Subdomain Adaptation to achieve a comprehensive domain alignment. In addition, we introduce a pseudo-labeling method based on Confident Learning to improve the utilization of unlabeled data. Experimental results on the publicly available WiSig dataset show that the proposed method achieves at least 30% performance improvement in cross-receiver scenarios, and outperforms other existing Domain Adaptation methods.