Unsupervised Specific Emitter Identification via Group Label-Driven Contrastive Learning

Specific emitter identification (SEI), as an emerging physical-layer security authentication method, is crucial for maintaining information security in the Internet of Things. However, existing deep learning-based SEI methods require extensive labeled data for training, which are often unavailable in untrusted scenarios. Furthermore, due to the subtle nature of radio-frequency fingerprints, unsupervised SEI struggles to achieve high accuracy in identification without the guidance of labels. In this paper, we propose an unsupervised SEI method based on group label-driven contrastive learning (GLD-CL). We propose a novel method for constructing the dataset: all input samples derived from the same received signal segment are grouped together and assigned a unique identifier, termed the group label. Based on this, we improve the loss function of self-supervised contrastive learning. With the assistance of group labels, the feature vectors of the same class in the feature space become more closely clustered, enhancing the accuracy of unsupervised SEI. Extensive experimental results based on real-world datasets demonstrate that the normalized mutual information of GLD-CL achieves 96.4% accuracy, representing an improvement of 5.68% or more compared to the baseline algorithms. Furthermore, GLD-CL exhibits robust performance, achieving good identification accuracy across various signal-to-noise ratio scenarios.