计算机应用与软件2025，Vol.42Issue(10)：163-170,8.DOI:10.3969/j.issn.1000-386x.2025.10.022
基于注意力机制与残差结构的联合调制识别
JOINT MODULATION RECOGNITION BASED ON ATTENTION MECHANISM AND RESIDUAL STRUCTURE
摘要
Abstract
Considering the recognition of various signal modulation types,this paper proposes a joint structure recognition classifier of signal modulation types,where we classify the received signals into two sets via SNR estimation and propose two networks for automatic identification for each set.For high SNR,we exploited the depth separable convolution and jump connection method to superimpose the residual structure,and the multi-head self-attention mechanism was considered to replace the partial convolution so that a more superior performance than the above two structures could be delivered.For low SNR,we leveraged the Transformer's self-attention mechanism to decide the importance of the different regions of the input sequence,where more effective characteristics could be extracted.Through the experiments on public dataset,we demonstrate the effectiveness of the proposed joint structure,where the recognition accuracy for the lower SNR can be remarkably raised and the recognition accuracy for the higher SNR can also be slightly improved.Moreover,it is verified that the proposed structure has relatively low complexity.关键词
自动调制分类/卷积神经网络/多头自注意力机制/深度可分离卷积/全局深度卷积Key words
Automatic modulation classification/Convolutional neural network/Multi-head self attention/Deep separable convolution/Global depthwise convolution分类
计算机与自动化引用本文复制引用
郑向阳,王忠勇,杨晨旭,陈家伟,巩克现,王玮..基于注意力机制与残差结构的联合调制识别[J].计算机应用与软件,2025,42(10):163-170,8.基金项目
国家自然科学基金青年科学基金项目(61901417) （61901417）
国家重点研发计划"前沿科技创新"专项(2019QY0302) （2019QY0302）
河南省科技攻关项目(212102210173,212102210566). （212102210173,212102210566）
