Multiscale Correlation Networks Based on Deep Learning for Automatic Modulation Classification

Jing Xiao, Yufeng Wang, Duona Zhang, Qinyan Ma, Wenrui Ding

Abstract: Automatic Modulation Classification (AMC) is a challenging yet significant technique for communication systems. Deep learning methods, though widely employed for AMC, are challenged by the poor representation in noisy scenarios. In this letter, we propose a novel Multiscale Correlation Networks (MSCNs) approach to enhance noise suppression and bolster representation power for AMC. MSCNs leverage learning correlation wavelet transform to redistribute radio signal and noise features across various scales, and incorporate multiscale correlation and attention mechanisms to characterize feature properties in terms of frequency. Experiments reveal that MSCNs achieve an overall classification rate of 91.36% at 10 dB using 152 k training samples on the public RadioML 2018.01 A benchmark.