Semi-Supervised Specific Emitter Identification via Dual Consistency Regularization

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 article, we present an 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 WiFi data set and 90.10% identification accuracy on an automatic dependent surveillance-broadcast (ADS-B) data set when only 10% of training samples are labeled, and improves the identification accuracy on the WiFi data set and the ADS-B data set by more than 19.07% and 5.30%, respectively. Our codes are available at https://github.com/lovelymimola/DCR-Based-SemiSEI.