Unsupervised Radio Frequency Fingerprint Identification Based on Curriculum Learning
Xiong Zha, Tianyun Li, Pei Gong
Abstract
Radio frequency fingerprint (RFF) identification aims to identify emitters by extracting the inherent physical-layer features. Despite the significant accuracy achieved in a supervised learning manner, few works have focused on unsupervised RFF identification. To this end, we propose an unsupervised training paradigm based on the curriculum learning framework. Specifically, the unlabeled samples are first sorted by the signal-to-noise (SNR) of signals. Then, the network is pretrained with contrastive learning on the high SNR subset. To further improve identification performance, the model is gradually optimized from the easy (high SNR) to the hard (low SNR) subsets with pseudo label learning. Experimental results indicate that the proposed scheme achieves comparable performance against schemes based on supervised learning.