Adaptive Denoising With Efficient Channel Attention for Automatic Modulation Recognition
Hao Zhu, Yuan Ma, Xingjian Zhang, Caiyong Hao · 2024
Automatic modulation recognition (AMR) is crucial for efficient modulation type recognition in modern wireless systems, but its performance suffers from low signal-to-noise ratios (SNR). This paper proposes an adaptive denoising automatic modulation recognition network (AD-AMR Net) to improve its performance, which incorporates an adaptive denoising module (ADM) to alleviate noise and a feature extraction module (FEM) to extract multi-scale features from the denoised signals. Com-pared to conventional AMR schemes, AD-AMR Net demonstrates better recognition accuracy under low SNRs, achieving 84.8% average accuracy in the 0 – 10 dB range versus 80 – 82% for conventional methods. Overall, the proposed model achieves 64.6% accuracy on the RadioML dataset, substantially outperforming previous deep learning techniques. Key innovations of adaptive denoising approach and multi-scale feature extraction enable major performance gains in low SNR scenarios.