Generative adversarial network-based electromagnetic signal classification: A semi-supervised learning framework

Abstract

Generative adversarial network (GAN) has achieved great success in many fields such as computer vision, speech processing, and natural language processing, because of its powerful capabilities for generating realistic samples. In this paper, we introduce GAN into the field of electromagnetic signal classification (ESC). ESC plays an important role in both military and civilian domains. However, in many specific scenarios, we can't obtain enough labeled data, which cause failure of deep learning methods because they are easy to fall into over-fitting. Fortunately, semi-supervised learning (SSL) can leverage the large amount of unlabeled data to enhance the classification performance of classifiers, especially in scenarios with limited amount of labeled data. We present an SSL framework by incorporating GAN, which can directly process the raw in-phase and quadrature (IQ) signal data. According to the characteristics of the electromagnetic signal, we propose a weighted loss function, leading to an effective classifier to realize the end-to-end classification of the electromagnetic signal. We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System (ACARS) signal dataset. Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.