Radio frequency fingerprinting identification using semi-supervised learning with meta labels

Abstract

Radio frequency fingerprinting (RFF) is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things (IoT) systems. Deep learning (DL) is a critical enabler of RFF identification by leveraging the hardware-level features. However, traditional supervised learning methods require huge labeled training samples. Therefore, how to establish a highperformance supervised learning model with few labels under practical application is still challenging. To address this issue, we in this paper propose a novel RFF semi-supervised learning (RFFSSL) model which can obtain a better performance with few meta labels. Specifically, the proposed RFFSSL model is constituted by a teacher-student network, in which the student network learns from the pseudo label predicted by the teacher. Then, the output of the student model will be exploited to improve the performance of teacher among the labeled data. Furthermore, a comprehensive evaluation on the accuracy is conducted. We derive about 50 GB real long-term evolution (LTE) mobile phone's raw signal datasets, which is used to evaluate various models. Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97% experimental testing accuracy over a noisy environment only with 10% labeled samples when training samples equal to 2700.