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 paper proposes a data-driven RFFI solution for 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 RFF 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 multi-scenario communications. Experimental results show that the proposed RFFI framework outperforms existing methods by achieving up to 97.30% accuracy on datasets with signal-to-noise ratios (SNRs) of 10dB.