计算机工程2026，Vol.52Issue(5)：383-395,13.DOI:10.19678/j.issn.1000-3428.0069677
基于ResNet-Transformer的通信信号自动调制识别
Automatic Modulation Recognition of Communication Signals Based on the ResNet-Transformer
摘要
Abstract
Automatic Modulation Recognition(AMR)is a crucial component in communication identification,situational awareness,and electronic reconnaissance.Deep neural networks,known for their powerful feature extraction and classification capabilities,offer higher recognition accuracy compared to traditional methods.However,current neural networks exhibit limitations in effectively extracting temporal information from signals,leading to high complexity and poor recognition accuracy under low Signal-to-Noise Ratio(SNR)conditions.To address these issues,this paper proposes a decision fusion recognition scheme based on a Residual Neural Network(ResNet)and Transformer network(ResNet-Transformer).This scheme aims to handle more complex SNR scenarios and improve the overall recognition accuracy.By leveraging the temporal memory characteristics of ResNet to deeply extract time-domain features from communication signals,and combining the outstanding long-distance dependency extraction capabilities of the Transformer network to enhance noise resistance,the proposed scheme employs a decision fusion strategy to obtain the final decision based on the outputs of each branch.Experimental results show that the proposed scheme achieves an average recognition accuracy of over 93％for SNRs above 10 dB and maintains a recognition accuracy of 56％even at an SNR of 0 on the open dataset RML2018.01A.Compared to traditional network models,the proposed scheme achieves a higher modulation recognition accuracy and exhibits a high noise resistance.关键词
自动调制识别/通信信号/残差神经网络/Transformer网络/决策融合Key words
Automatic Modulation Recognition(AMR)/communication signal/Residual Neural Network(ResNet)/Transformer network/decision fusion分类
信息技术与安全科学引用本文复制引用
沈丹阳,麦文..基于ResNet-Transformer的通信信号自动调制识别[J].计算机工程,2026,52(5):383-395,13.基金项目
无线传感器网络四川省高校重点实验室开放课题(WSN202205). （WSN202205）
