A Lightweight Transformer for Automatic Modulation Recognition Based on Wavelet Convolution at the Low SNR

Abstract

Automatic modulation recognition in low-computation edge devices under noisy environments has become increasingly critical. To address the dual challenges of lightweight design and noise resistance, this paper proposes WCTFormer, a novel lightweight network that integrates discrete wavelet transform for frequency-domain noise suppression and a Transformer for global feature extraction, enabling robust performance in low SNR conditions. Experiments on open-source datasets demonstrate that WCTFormer achieves superior recognition accuracy, with 92.40% accuracy at 0 dB SNR, requiring only 60K parameters. WCTFormer combines high recognition performance and computational efficiency, making it suitable for deployment in resource-constrained edge devices.