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Automatic Modulation Recognition Method Based on Channel-Enhanced Convolution and Linear-Angular Attention
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DOI：10.1109/ACCESS.2025.3552414.png)
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Abstract
En 中文
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.
Keywords:
Modulation
Feature extraction
Convolution
Accuracy
Signal to noise ratio
Deep learning
Noise
Neural networks
Data models
Convolutional neural networks
automatic modulation recognition
channel-enhanced convolution
linear-angular attention
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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