基于复数深度神经网络的电磁信号调制识别  

Electromagnetic Signal Modulation Recognition Based on Complex-Valued Deep Neural Network

作　　者：袁德品 赵亮[1] 葛宪生[1] YUAN Depin;ZHAO Liang;GE Xiansheng(The 22nd Research Institute of China Electronics Technology Group Corporation,Qingdao 266107,China)

机构地区：[1]中国电子科技集团公司第二十二研究所,山东青岛266107

出　　处：《电子科技》2025年第3期1-6,共6页Electronic Science and Technology

基　　金：国防预研(145AZB070002000X)。

摘　　要：在复杂电磁环境区域中,较难获取信号调制类型。传统调制信号识别分类方法因自身缺陷导致识别成功率不佳。目前用于信号调制的深度学习方法均基于实数来表征和处理,但因丢失复数原本的内在联系而容易出现识别偏差。针对这种情况,文中将复数深度神经网络应用于电磁信号的调制识别,设计了复卷积、批归一化和全连接网络等复数卷积深度神经网络,并通过Softmax函数完成最终的分类任务。采用标准数据集RML2016.10a完成网络训练以及测试工作。实验结果表明,通过训练后的复数深度神经网络优于传统识别算法,可以有效提升电磁信号识别率。In the region of complex electromagnetic environment,it is difficult to obtain the signal modulation type.The traditional recognition and classification methods of modulated signals are not successful because of their own defects.The current deep learning methods usually used for signal modulation are based on real values for characterization and processing,which results in recognition bias due to the loss of the original intrinsic connection of complex values.To solve this situation,the complex deep neural network is applied to the modulation recognition of electromagnetic signals,complex convolutional deep neural networks such as complex convolutional deep neural networks,batch normalization and fully connected networks are designed,and the final classification task is completed by softmax function.The standard data set RML2016.10a is used to complete the training as well as testing of the network.The experimental results show that the trained complex deep neural network is significantly better than traditional recognition algorithms,and can effectively improve the recognition rate of electromagnetic signals.

关 键 词：复数神经网络 复杂电磁环境 调制样式 相位信息 调制识别 I/Q数据 潜在特征 电磁信号 

分 类 号：TN91[电子电信—通信与信息系统]