Radio Frequency Fingerprint Identification Using Conditional Generative Adversarial Network for SAT-AIS

Radio frequency fingerprint identification (RFFI) offers a paradigm for the security authentication of the satellite component in automatic identification system (SAT-AIS). This article proposes a data-driven RFFI solution for the SAT-AIS with multiple communication scenarios. Conditional generative adversarial networks (CGANs) embedded with channel estimation can generate numerous realistic samples for data augmentation. Both the generator and the discriminator in CGANs employ deep neural networks to adaptively extract radio frequency fingerprint features, thus enhancing the robustness of this system. Moreover, in light of the dilemma of collecting large datasets, Bayesian meta-learning via variational autoencoder is performed for model training to enable few-shot learning, where only a small number of samples are required to complete the inference for efficient RFFI in multiscenario communications. Experimental results show that the proposed RFFI framework outperforms the existing methods by achieving up to 97.30% accuracy on datasets with signal-to-noise ratios of 10 dB.