Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 310-317.doi: 10.11896/JsJkx.190800073

• Computer Network • Previous Articles     Next Articles

Radio Modulation Recognition Based on Signal-noise Ratio Classification

CHEN Jin-yin, JIANG Tao and ZHENG Hai-bin   

  1. College of Information Engineering,ZheJiang University of Technology,Hangzhou 310000,China
  • Published:2020-07-07
  • About author:CHEN Jin-yin , Ph.D, associate professor.Her main research interests include artificial intelligence security, graph data mining and evolutionary computing.
  • Supported by:
    This work was supported by the ZheJiang Provincial Natural Science Foundation of China (LY19F020025),MaJor Special Funding for “Science and Technology Innovation 2025” in Ningbo (2018B10063),Signal Recognition Based on GAN,Deep Learning for Enhancement Recognition ProJect ,Engineering Research Center of Cognitive Healthcare of ZheJiang Province (2018KFJJ07).

Abstract: Radio modulation recognition has been widely used in various fields of military and civilian.Compared with the traditional methods such as artificial recognition and spectrum analysis,the modulation recognition method based on deep learning has better performance,but it still has the problem of low recognition accuracy.This paper proposed a modulation recognition method based on long-term and short-term memory network (LSTM) model.It combines deep learning classification method with SNR classification to design a SNR modulation recognition framework based on deep learning.By accurately classifying high and low SNR signals and using different denoising processing,the recognition accuracy of low SNR signal modulation is improved.The recognition accuracy of 2016.4c signal data set by machine learning method is 21%.Three modulation type identification comparison experiments,non-denoising,grading denoising and total denoising,are carried out on 2016.4C signal data set,the recognition accuracy is 69.82%,70.56%,and 66.67% respectively,which effectively verifies the feasibility and superiority of the proposed method to improve the accuracy of low SNR signal recognition.

Key words: Signal-noise ratio classification, Modulation recognition, Deep learning, Long-short term memory networks

CLC Number: 

  • TP29
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