GIGNet: A Graph-in-Graph Neural Network for Automatic Modulation Recognition

In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural networks (GNNs) are utilized to extract internal graph-based features from signal samples and correlation information between different signals treated as nodes in a graph. Specifically, a graph-level GNN is utilized to extract local and global features of signal samples transformed into graphs. Next, a method for constructing a graph that corresponds signals to nodes is proposed to assess the degree of association between nodes and to find closer neighbors of nodes. These closer neighbors enable the subsequent node-level GNN to incorporate appropriate correlation information for the further classification task. Compared to classical deep learning models and existing GNN-based models, experimental results justify the advantages of the proposed GIGNet model on recognition accuracy and robustness at low signal-to-noise ratio (SNR).