Semi-Supervised Specific Emitter Identification via Dual Consistency Regularization

Abstract

Deep Learning (DL)-based Specific Emitter Identification (SEI) is a potential physical layer authentication technique for Industrial Internet-of-Things (IIoT) Security, which detects the individual emitter according to its unique signal features resulting from transmitter hardware impairments. The success of DL-based SEI often depends on sufficient training samples and the integrity of samples’ labels. The extensive deployment of wireless devices generates a huge amount of signals, but signals labeling is quite difficult and expensive with the high demand for expertise. In this paper, we present a SEI method based on Dual Consistency Regularization (DCR), which enables feature extraction and identification using a few labeled samples and a large number of unlabeled samples. With the help of pseudo-labeling, we leverage consistency between the predicted class distribution of weakly-augmented unlabeled training samples and that of strongly-augmented training unlabeled samples, and consistency between semantic feature distribution of labeled samples and that of pseudo-labeled samples, which takes the unlabeled samples into account to model parameter tuning for a more accurate emitter identification. Extensive numerical results demonstrate that compared with well-known semi-supervised learning-based SEI methods, our method obtains 99.77% identification accuracy on a Wi-Fi dataset and 90.10% identification accuracy on an Automatic Dependent Surveillance-Broadcast (ADS-B) dataset when only 10% of training samples are labeled, and improves the identification accuracy on the Wi-Fi dataset and the ADS-B dataset by more than 19.07% and 5.30%, respectively. Our codes are available at https://github.com/lovelymimola/DCR-Based-SemiSEI.