Automatic Modulation Classification Under Non-Gaussian Noise: A Deep Residual Learning Approach

During the last few years, automatic modulation classification (AMC) has attracted widespread attention in both civilian and military applications. Conventional AMC schemes are primarily developed under Gaussian noise assumptions. However, recent empirical studies show that non-Gaussian noise has emerged in a variety of wireless networked systems. The bursty nature of non-Gaussian noise fundamentally challenges the applicability of the conventional AMC schemes. In order to improve the classification performance under non-Gaussian noise, in this paper, a novel modulation classification method is proposed by using cyclic correntropy spectrum (CCES) and deep residual neural network (ResNet). First, CCES is introduced to effectively suppress non-Gaussian noise through the designated Gaussian kernel. CCES also provides significantly different CCES graphs with respect to different modulation schemes, enabling AMC to directly operate with the graphs without further feature extraction. Next, based on the CCES graphs, an end-to-end deep ResNet-based AMC is developed to recognize the correct modulation by iteratively evaluating the residual information in a cascade of multiple learning layers. Experimental results confirm that the proposed algorithm outperforms existing designs with much higher classification accuracy, i.e., 3 dB less in the required generalized signal to noise ratio for 100% accuracy, in non-Gaussian noise environments.