Lightweight Automatic Modulation Classification Based on Efficient Convolution and Graph Sparse Attention in Low-Resource Scenarios

Automatic modulation classification (AMC) is essential in noncooperative communication systems, since it enables the automatic recognition of signal modulation types. The recent incorporation of deep learning, particularly graph neural networks (GNNs), has significantly improved the AMC accuracy. The GNNs increase the performance by decoding the relationships between nodes and edges, which represent the topological structure of data. In AMC, signal features or time points are modeled as nodes, and their interconnections represent the interactions between these features. This modeling allows the GNNs to thoroughly analyze signals and accurately identify complex modulations. However, the existing traditional methods for mapping IQ signal sequences into graphs exhibit high computational load and excessive processing time. To solve these problems, this article proposes a lightweight model of high performance, referred to as PGNet, which combines efficient partial convolution (PConv) with graph sparse attention techniques. This combination minimizes the computational load and maximizes the strengths of the convolutional neural networks and GNNs. The results of the conducted experiment show that PGNet, respectively, achieves average accuracies of 62.8% and 64.1% on the RML2016.10a and RML2016.10b datasets, with only 16315 parameters and an inference time of only 2 ms/sample. Due to its high efficiency and compact size, the proposed PGNet provides a substantial potential for deployment in low computing resource scenarios, such as IoT devices with limited resources.