Computer Science ›› 2020, Vol. 47 ›› Issue (2): 175-179.doi: 10.11896/jsjkx.181202361

• Artificial Intelligence • Previous Articles     Next Articles

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

XU Mao,HOU Jin,WU Pei-jun,LIU Yu-ling,LV Zhi-liang   

  1. (School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China)
  • Received:2018-12-19 Online:2020-02-15 Published:2020-03-18
  • About author:XU Mao,born in 1993,postgraduate.His main research interests include machine learning,deep learning and artificial intelligence;HOU Jin,born in 1969,Ph.D.Her main research interests include machine learning and so on.
  • Supported by:
    This work was supported by the Research on Modulation Recognition Technology based on Deep Learning, CAD&CG National key Laboratory of Zhejiang University (A1823) and Automatic detection and identification of lane lines and signs of intelligent vehicles, Chengdu science and technology project (2015-HM01-00050-SF).

Abstract: 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 normalization 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.

Key words: Convolutional neural network, Modulation classification, Short-time Fourier transform, Time frequency characteristics

CLC Number: 

  • TP183
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