基于多尺度时序特征的信号调制样式识别算法  

Signal Modulation Pattern Recognition Algorithm Based on Multiscale Temporal Features

作　　者：崔凯 崔天舒 朱岩 张晔 黄永辉 赵文杰 CUI Kai;CUI Tianshu;ZHU Yan;ZHANG Ye;HUANG Yonghui;ZHAO Wenjie(Key Laboratory of Electronics and Information Technology for Space System,National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China;School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院国家空间科学中心复杂航天系统电子信息技术重点实验室,北京100190 [2]中国科学院大学计算机科学与技术学院,北京100049

出　　处：《信号处理》2021年第8期1507-1517,共11页Journal of Signal Processing

基　　金：中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(Y42613A32S)。

摘　　要：在认知无线电应用中,当前基于深度学习的信号调制样式识别算法存在运算效率低、复杂度较高的问题,为此本文提出了一种基于多尺度时序特征的信号调制识别算法。该算法首先利用多层卷积层提取不同尺度的时序数据,将数据进行特征融合后使用长短期记忆网络提取时序特征,最后由输出层输出识别结果,并通过网络结构的设计和优化,降低了算法复杂。实验结果表明,在包含11种调制信号的原始I/Q信号测试集上,在信噪比为4 dB及以上时,该算法识别准确率达到90%以上。与同等识别准确率的算法相比,该算法的复杂度更低,在嵌入式设备Jetson Nano和树莓派4B上的推理时间更短,具有更好的工程应用价值。In cognitive radio applications,the current deep learning-based signal modulation style recognition algorithm has the problems of low computing efficiency and high complexity,for which a signal modulation recognition algorithm based on multi-scale timing features is proposed in this paper.The algorithm firstly uses multi-layer convolutional layers to extract temporal data of different scales,fuses the data with features and then uses long and short-term memory networks to extract temporal features,and finally outputs the recognition results from the output layer,and reduces the complexity of the algorithm by designing and optimizing the network structure.Experimental results show that the algorithm achieves recognition accuracy of more than 90%at signal-to-noise ratios of 4 dB and above on the original I/Q signal test set containing 11 modulated signals.The algorithm has lower complexity and shorter inference time on the embedded devices Jetson Nano and Raspberry Pi 4B compared to algorithms with the same recognition accuracy,which has better engineering application value.

关 键 词：认知无线电 信号调制样式识别 算法复杂度 多尺度时序特征 模型参数量 识别准确率 

分 类 号：TN91[电子电信—通信与信息系统]