DTSG-Net: Dynamic Time Series Graph Neural Network and Its Application in Modulation Recognition

Modulation recognition of communication signals is of great importance in the context of the Internet of Everything (IoE), as wireless communication technology is a key foundation for implementing the IoE. Recently, graph neural networks (GNNs) have been successfully applied to modulation recognition tasks due to their ability to merge messages transmitted between adjacent nodes in the graph. However, GNN-based models are more computationally intensive when processing long signals, potentially reducing their practicality. In this article, we explore a novel signal representation from a graph perspective and propose a graph-powered modulation recognition framework. We first propose the dynamic time series graph (DTSG) algorithm, which segments the signals and maps each segment into a patch graph, with corresponding patches from different signals sharing connected edges. By integrating DTSG with both GNNs and recurrent neural networks (RNNs), we have designed an end-to-end signal classification framework, DTSG-Net, for modulation recognition. Experimental results on four datasets: 1) RML2016.10a; 2) RML2018.01a; 3) Sig2019-12; and 4) HKDD_AMC36—demonstrate that our DTSG-Net can achieve high signal modulation classification accuracy (Acc) with minimal computational resources, outperforming existing methods based on signal graph representation in terms of computational resource savings and higher accuracy.