Automatic Modulation Recognition Method Based on Channel-Enhanced Convolution and Linear-Angular Attention

Automatic Modulation Recognition (AMR) is an electronic signal processing technology designed to automatically identify and classify the modulation type of radio signals. Existing AMR methods suffer from a significant decrease in classification accuracy when the Signal-to-Noise Ratio (SNR) degrades. To enhance the accuracy of AMR methods under low SNR conditions, this paper proposes a novel AMR method based on Channel-Enhanced Convolution and a Linear-Angular Attention Mechanism, named CCLANN (Channel-enhanced Convolutional Linear-Angular Attention Neural Network). This method first utilizes a channel-enhanced deep convolutional module to extract spatial information from the feature maps of the input signal across different channel dimensions. Subsequently, a linear-angular attention mechanism is introduced to effectively extract time-series features from the modulated signals. Experimental results on the public datasets RML2016.10a and RML2016.10b demonstrate that the proposed AMR method improves the average classification accuracy by 2.4% and 2.1%, respectively, compared to existing schemes in the SNR range of −4 dB to 4 dB, while maintaining robustness. Slight improvements in classification accuracy are also observed at SNRs above 0 dB.