无线电工程2026，Vol.56Issue(2)：310-318,9.DOI:10.3969/j.issn.1003-3106.2026.02.013
基于CSA策略和改进ResNet的通信信号调制识别方法
Communication Signal Modulation Recognition Method Based on CSA Strategy and Improved ResNet
冯瑞杰 1陶杰 2李雄伟 2李永波2
作者信息
- 1. 中国人民解放军陆军工程大学石家庄校区,河北 石家庄 050003||中国人民解放军32159 部队,新疆 乌鲁木齐 830011
- 2. 中国人民解放军陆军工程大学石家庄校区,河北 石家庄 050003
- 折叠
摘要
Abstract
Due to factors such as channel noise and multipath fading,the quality of modulated signals is significantly degraded.Current network models have limitations in handling such modulation signal recognition tasks,and hybrid network models are usually characterized by complex structures and high computational costs.To address these problems,a new modulation recognition network model is constructed based on an improved Residual Neural Network(ResNet),incorporating a parallel-processing Channel-Space Joint Attention(CSA)mechanism.This model leverages the attention mechanism's ability to automatically focus on key channels and spatial features,thereby enhancing its representational capability of core features.Additionally,the two-level residual connection is introduced into the residual network to effectively reduce the risk of overfitting in deep models.Simulation verification on the public dataset RML2018.01a demonstrates that when the Signal to Noise Ratio(SNR)is higher than 2 dB,the average recognition accuracy of the model reaches 93%.When the SNR surpasses 12 dB,the recognition accuracy stabilizes above 97%.Compared to models like ResNet,ResNet50,and Long Short-Term Memory(LSTM),this model achieves significant improvements in both average recognition accuracy and accuracy under low-SNR scenarios.Compared to the Convolutional,Long Short-Term Memory,Fully Connected Deep Neural Networks(CLDNN)hybrid model,this model achieves comparable recognition accuracy while having less than one-tenth of the trainable parameters,with the relative performance loss controlled within a reasonable range.The experimental results indicate that this network model holds considerable potential in the field of Automatic Modulation Recognition(AMR).关键词
自动调制识别/深度学习/残差神经网络/注意力机制/通道空间联合注意力机制Key words
AMR/deep learning/ResNet/attention mechanism/CSA分类
信息技术与安全科学引用本文复制引用
冯瑞杰,陶杰,李雄伟,李永波..基于CSA策略和改进ResNet的通信信号调制识别方法[J].无线电工程,2026,56(2):310-318,9.
