CSGNet: a network with attention mechanism for automatic modulation recognition

Xianyi Li

doi:10.1117/12.3052965

3 January 2025 CSGNet: a network with attention mechanism for automatic modulation recognition

Xianyi Li

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 1344223 (2025) https://doi.org/10.1117/12.3052965
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. This innovative network leverages the strengths of both Convolutional Neural Networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. The primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. The process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. The results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. This study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. The findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xianyi Li "CSGNet: a network with attention mechanism for automatic modulation recognition", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 1344223 (3 January 2025); https://doi.org/10.1117/12.3052965

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