Cross-Receiver Radio Frequency Fingerprint Identification Based on Domain Adaptation With Dynamic Distribution Alignment

Radio frequency fingerprint identification (RFFI) utilizes nonideal hardware features present in the signal to identify different transmitters. However, existing RFFI models have poor generalization capabilities. When a model trained on one receiver is deployed on a new receiver, the identification performance of the model degrades due to the effect of different receiver characteristics, which can cause the signal distribution to be shifted. To address this problem, we propose a cross-receiver RFFI based on domain adaptation with dynamic distribution alignment. First, deep features are extracted using the ResNet18 network and the global distribution of the features is aligned using maximum mean discrepancy (MMD). Then multilevel features are extracted using a designed multiscale feature extraction module and the subdomain distribution is aligned using local MMD (LMMD). Finally, a dynamic parameter is introduced to adaptively adjust the relative importance between the global and subdomain distributions. Twelve sets of cross-receiver experiments are conducted on the WiSig dataset, and the algorithm in this article achieves an average identification rate of 92.52% in the target domain. Meanwhile, the experimental results under different signal-to-noise ratios (SNR) also verify the algorithm has strong robust performance. It shows that the algorithm can effectively alleviate the model performance degradation problem in the cross-receiver scenarios.