Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 283-288.doi: 10.11896/JsJkx.190800072

• Computer Network • Previous Articles     Next Articles

Deep Learning Based Modulation Recognition Method in Low SNR

CHEN Jin-yin, CHENG Kai-hui 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 under Grant (LY19F020025),the MaJor Special Funding for “Science and Technology Innovation 2025” in Ningbo under Grant (2018B10063) and Signal Recognition Based on GAN ,Deep Learning for Enhancement Recognition ProJect,and Engineering Research Center of Cognitive Healthcare of ZheJiang Province under Grant (2018KFJJ07).

Abstract: Modulation recognition of radio signals is the intermediate step of signal detection and demodulation.The existing research shows that deep learning technology can effectively identify the modulation types of radio signals.As for the low signal-to-noise ratio,there is still no good solution to the problem of the sharp drop in recognition accuracy.Inspired by the noise reduction in image fields,a modulation recognition method based on deep learning in Low SNR was proposed in this paper.It realizes the denoising of the low signal-to-noise ratio signals and solves the problem of the sharp drop.Through a large number of experiments in the open source datasets,the effectiveness of the proposed method was verified.The recognition accuracy of the low signal-to-noise ratio signals increased from 10% to 15%.Finally,we analyze the problems existing in the method and look forward to future research.

Key words: Deep learning, Automatic recognition of modulation type, Denoising model, Low signal-to-noise ratio

CLC Number: 

  • TP29
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