Semi-Supervised Specific Emitter Identification Based on Bispectrum Feature Extraction CGAN in Multiple Communication Scenarios

Specific emitter identification (SEI) refers to the technology that uses the inherent defects of the physical layer of a hardware device to identify and uniquely associate a single emitter. Given its unique capability to automatically extract high-dimensional features, deep learning has recently shown great potential in SEI. However, the training process of supervised neural networks largely depends on the generalization of sample distribution and integrity of data labels. In specific noncooperative areas, such as electronic reconnaissance and spectrum monitoring, the unknown radio frequency devices and the complexity of the electromagnetic environment make it difficult to utilize sufficient labeled samples and capture the potential distribution of the data. Therefore, we introduce semi-supervised learning into SEI and propose a self-classification generative adversarial network (GAN) using bispectrum-based feature extraction. The bispectrum estimation of the signal is used as the feature representation of emitters, and the label information is embedded into the input latent layer to guide the training of the GAN. The shared weight update of the classification network is realized through semi-supervised training, and the optimization function of the generator is redefined to solve the mode collapse caused by the game principle. We innovatively extend SEI to communication scenarios with multiple relays and evaluate the effectiveness of our algorithm in different communication scenarios with contaminated radio-frequency fingerprints. The algorithm is also verified using Universal Software Radio Peripheral based on software-defined radio platforms. The numerical experimental results on six modulation signals demonstrate the excellent semi-supervised classification performance of the proposed SEI scheme in multiple scenarios.