Cross-Device Radio Frequency Fingerprinting Identification Based On Domain Adaptation

Radio frequency fingerprinting (RFF) is a lightweight authentication technology for resource-limited terminal nodes by exploiting the unique hardware imperfections resulting from the manufacturing process. Previous studies about radio frequency fingerprinting identification (RFFI) mainly concentrate on improving the accuracy which is evaluated by the single receiver device that trains and identifies all the nodes. Due to the mobility of the consumer electronic terminals, these terminal nodes may need to be identified by the different receivers. In this paper, we propose a cross-device radio frequency fingerprinting identification scheme which allows enrolled nodes to be authenticated by different devices. Motivated by the observation that signals collected by different receiver devices have a distribution shift that would violate the basic independent and identically distributed (i.i.d) assumption of supervised learning. Domain adaptation is adopted to improve the accuracy under different receivers, which can align the data captured from different devices and eliminate the distribution shift through the labeled data from one receiver device and unlabeled data from the other device. By this way, the distribution shift from different devices is corrected. Extensive experiment configurations under various Signal-to-noise ratio (SNR) are carried out to demonstrate the performance of domain adaptation with the same model structure. The results indicate that classification accuracy under different devices can be increased by 7%-15% and get a stable accuracy rate higher than 90% by leveraging our proposed cross-device RFFI scheme.