基于通信信号时频特性的卷积神经网络调制识别

在通信环境日益密集、信号调制样式层出不穷的情况下,信号的调制识别变得愈加困难。寻求一种高精度、时效性好的自动调制识别新方法,对无线电通信应用领域有重大意义。对此,文中提出了一种结合通信信号时频特性的卷积神经网络(Convolutional Neural Network Based on Time-Frequency Characteristics,TFC-CNN)调制识别算法。首先,采集大量调制信号,将信号的时频特征通过短时傅里叶变换转换成图像特征,并将其作为网络的输入;然后,设计一种特征提取能力更强、参数更少的卷积神经网络,通过改进网络中不同层的连结方式来增加网络的特征提取能力,同时通过减小卷积核的尺度、使用全局均值池化层来减少模型参数,提高了模型的时效性;最后,在网络中添加批归一化(Batch Normalization,BN)层,在增加模型稳定性的同时防止模型出现过拟合。实验结果表明,所提算法在参数和训练时间上比传统方法明显减少,同时有更高的准确率,体现了所提算法的优越性。

Convolutional Neural Networks Based on Time-Frequency Characteristics for Modulation Classification

In the situation of increasingly dense communication environment and endless modulation patterns of signals,the modu-lation classification becomes more and more difficult.It is very important for the application of radio communication to seek a new method of automatic modulation classification(AMC)with high accuracy and good timeliness.Based on this,a novel convolutio-nal neural network based ontime-frequency characteristics(TFC-CNN)for AMC was proposed.Firstly,a large number of modulation signals are collected,and the time-frequency features of the signals are converted into image features by short-time Fourier transform,which are used as the input of the network.Secondly,a convolutional neural network with stronger feature extraction ability and fewer parameters is designed,and the feature extraction ability of the network is enhanced by improving the connection mode of different layers in the network.At the same time,the model parameters are reduced by reducing the scale of the convolution kernel and using the global average pooling,the timeliness of the model is improved.Finally,adding batch normali zation layers to network can increase the stability of the model and prevent overfitting.The experimentresults show that the proposed algorithm is significantly lessin parameters and training time than the traditional methods,and has higher accuracy,which shows the superiority of the proposed algorithm.