计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 283-288.doi: 10.11896/JsJkx.190800072

• 计算机网络 • 上一篇    下一篇

低信噪比下基于深度学习的调制模式识别方法

陈晋音, 成凯回, 郑海斌   

  1. 浙江工业大学信息工程学院 杭州 310000
  • 发布日期:2020-07-07
  • 通讯作者: 陈晋音(chenJinyin@zJut.edu.cn)
  • 基金资助:
    浙江省自然科学基金(LY19F020025);宁波市“科技创新2025”重大专项资助(2018B10063);基于GAN的信号识别项目资助;深度学习增强识别项目资助;浙江省认知医疗工程技术研究中心(2018KFJJ07)

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).

摘要: 无线电信号的调制类型识别是信号检测与解调的中间步骤,已有的研究表明利用深度学习技术能高效地识别无线电信号调制类型。但对于低信噪比区间内识别准确率骤降的问题,仍没有一种较好的解决方案。受到深度学习在图像降噪中的启发,本文提出了低信噪比下基于深度学习的调制模式识别方法,实现了对低信噪比信号的降噪处理,解决了低信噪比区间信号识别准确率过低的问题。通过在开源数据集下的大量实验,验证了本方法的有效性,低信噪比信号调制类型识别的准确率由10%上升至15%。最后,文章对于本方法存在的问题进行分析,并对未来的研究进行了展望。

关键词: 低信噪比, 调制类型自动识别, 降噪模型, 深度学习

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

中图分类号: 

  • TP29
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