A Dual-Branch Network With Feature Assistance for Automatic Modulation Recognition

Abstract

Automatic modulation recognition (AMR) is a critical technology in wireless communications, aiming to achieve high recognition accuracy with low complexity in increasingly intricate electromagnetic environments. To tackle this challenge, in this paper, we propose a dual-branch convolution cascaded transformer network with feature assistance, termed DCTFANet. To enhance the differentiation between samples, we employ the gramian angular field (GAF) to capture potential temporal correlations between each data point. Subsequently, both I/Q sequences and GAF data are input into the model for joint signal feature extraction. The network backbone is constructed using multiple improved depthwise separable convolution (DSC) blocks, which significantly reduce computational complexity. Moreover, the backbone depth is flexibly adjustable to fully exploit local features of different data types. Finally, feature transition and the transformer encoder are used to reduce parameters and extract global feature. Experimental results on RML2016.10b show that the proposed method achieves higher recognition accuracy compared to several state-of-the-art methods, especially at low signal-to-noise ratios (SNRs), with an increase of at least 10.80% at −20 dB.