基于卷积长短时深度神经网络的信号调制方式识别方法  

Signal modulation recognition method based on convolutional long short-term deep neural network

作　　者：杨海宇 郭文普 康凯 YANG Haiyu;GUO Wenpu;KANG Kai(Academy of Combat Support,Rocket Force University of Engineering,Xi’an Shaanxi 710025,China)

机构地区：[1]火箭军工程大学作战保障学院,西安710025

出　　处：《计算机应用》2023年第4期1318-1322,共5页journal of Computer Applications

摘　　要：针对信号调制方式识别计算复杂度高、低信噪比(SNR)条件下识别率较低、网络结构相对单一的问题,提出一种基于卷积长短时深度神经网络(CLDNN)的信号调制方式识别方法。首先,采用基准开源数据集RadioML2016.10a,对该数据集做同相正交(I/Q)数据转换,并将得到的结果作为网络输入;其次,构建CLDNN模型,模型分为三层卷积神经网络(CNN)、两层长短期记忆(LSTM)网络和两层全连接网络(FCN);最后,对所提模型进行训练及测试,得到分类结果。实验结果表明,对11种信号在不同SNR下进行调制方式识别时,与现有的单一网络结构模型如残差神经网络(RES)模型、CNN模型和残差生成对抗网络(RES-GAN)模型进行对比,随着SNR的提升,CLDNN模型的识别准确率也随之提高,且CLDNN模型的识别准确率均高于其他3种对比模型,当SNR在4 dB以上时,达到了92%。Focused on the high computational complexity,low recognition rate under the condition of low Signal-to-Noise Ratio(SNR),and relatively simple network structure,a signal modulation recognition method based on Convolutional Long short-term Deep Neural Network(CLDNN)was proposed.Firstly,the open-source benchmark dataset RadioML2016.10a was adopted,and In-phase/Quadrature(I/Q)data conversion was performed on it,then the obtained result was used as the network input.Secondly,the CLDNN model was constructed,which was divided into three parts,that is three-layer Convolutional Neural Network(CNN),two-layer Long Short-Term Memory(LSTM)network,and two-layer Fully Connected Network(FCN).Finally,the proposed model was trained and tested to obtain classification results.Experimental results show that recognition accuracy of CLDNN model increases with SNR improvement and reaches 92%with SNR bigger than 4 dB,which is higher than those of the existing single network structure models such as Residual Neural Network(RES)model,CNN model and RESidual Generative Adversarial Network(RES-GAN)model,in the modulation recognition of 11 kinds of signals at different SNR.

关 键 词：调制方式识别 深度学习 卷积神经网络 长短期记忆网络 深度神经网络 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构]