SigMixer: Lightweight Automatic Modulation Classification via Multi -Layer Perceptrons Neural Network

Automatic modulation recognition (AMR) plays a vital role in non-cooperative communication systems, which is an important technological component of blind signal processing. The application of deep learning (DL) methods in the field of modulation recognition has shown tremendous potential, greatly surpassing the performance of traditional methods. Existing DL-based AMR schemes employ modules of convolution and attentions to capture the local patterns and long-range dependencies for improving the performance of modulation classification, e.g., transformer-based methods. However, the complicated structure of existing artificial neural networks incur exceedingly long running time that hinders the practice implementation of these methods. Against this background, for the first time, this paper shows that the commonly adopted convolution and attentions are not necessary modules for the signal recognition. Specifically, we propose a lightweight architecture exploiting the multilayer perceptrons (MLP) for signal modulation classification, named SigMixer, which includes layers of token-mixing MLP and channel-mixing MLP that are interleaved for enabling spatial and temporal interaction. The proposed SigMixer not only has a simpler structure and lower computational complexity than the existing state-of-the-art approaches, but can also achieve a competitive performance with the formers. Experimental results show that compared with state-of-the-art approaches, SigMixer achieves better recognition accuracy, with 1.44%-10.65% improvement. Besides, the recognition accuracy can reach 89.36% for a low signal-to-noise ratio (SNR) = - 2 dB.