A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network

Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node information to similar nodes along the edges. GCN extracts more features and achieves better classification results, particularly for characterless examples. In this paper, we propose a modulation classification algorithm based on a feature-embedding GCN (FE-GCN). It comprises three parts: feature-embedding network (FEN), similarity adjacent matrix calculation network (SAMCN), and graph convolutional classification network (GCCN). The FEN embeds the signal data into a one-dimensional feature vector. The SAMCN calculates the similarity of all signal feature vectors to a matrix using a single convolutional neural network (CNN). The GCCN is used to extract the final features and classify the signals in a graph. Simulation results on the public dataset RML2016.10A show that the FE-GCN performs effectively and outperforms a series of advanced deep-learning methods.