计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 310-317.doi: 10.11896/JsJkx.190800073

• 计算机网络 • 上一篇    下一篇

基于信噪比分级的信号调制类型识别

陈晋音, 蒋焘, 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(2018B10063);基于GAN的信号识别项目;深度学习增强识别项目;浙江省认知医疗工程技术研究中心(2018KFJJ07)

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).

摘要: 无线电调制类型识别广泛应用于军民的各个领域,相比人工识别和频谱分析法等传统方法,基于深度学习的信号调制类型识别方法取得了较好性能,但仍存在识别准确率低的问题。文中提出了一种基于长短时记忆网络(LSTM)模型的信号调制类型识别方法,将深度学习分类方法与信噪比分级相结合,设计了一种基于深度学习的信噪比分级调制类型识别框架。通过准确分类高低信噪比信号,并采用不同的降噪处理来提高低信噪比信号调制类型识别的准确率。通过机器学习方法对2016.4C信号数据集进行调制类型识别的准确率为21%,通过深度学习模型对2016.4C信号数据集进行不降噪、分级降噪、全部降噪3个调制类型识别对比实验,识别准确率分别为69.82%,70.50%,66.67%,有效验证了所提方法对提高低信噪比信号调制类型识别准确率的可行性与优越性。

关键词: 长短期记忆网络, 调制类型识别, 深度学习, 信噪比分级

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

中图分类号: 

  • TP29
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