Abstract: RF fingerprinting uses the hardware features of RF front-end as identifiers to identify devices.Aiming at the problem that existing RF fingerprinting research ignores the interference of receiver hardware features,resulting in poor generalization of the model on different receiver devices,an RF fingerprinting method based on squeeze and excitation(SE) attention multi-source domain adversarial network is proposed.Multiple source-domain labelled data and a small amount of target-domain unlabelled data are used for adversarial training to extract receiver-domain independent features.Incorporating SE attention mechanism enhances the model’s ability to learn RF fingerprint features from the transmitter.The model parameters are fine-tuned by combining a very small amount of tagged data in the target domain to further improve the performance of transmitter identification.Experimental results on the Wisig dataset show that this method can effectively identify the transmitter device in the cross-receiver scenario,with an average accuracy of up to 83.1%,and the average accuracy can be further improved to 93.1% by adding a small amount of tagged data to fine-tune the model.

Key words: RF fingerprint recognition, Multi-source domain adversarial, Deep learning, Physical layer security, SE attention