脉冲噪声下基于平滑循环相关熵谱的调制识别方法

戴江安; 栾声扬; 赵明龙; 张兆军; 邱天爽

doi:10.11959/j.issn.1000-436x.2021231

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脉冲噪声下基于平滑循环相关熵谱的调制识别方法

学术论文 | 更新时间：2024-06-05

- 脉冲噪声下基于平滑循环相关熵谱的调制识别方法
- Pol-CCES based modulation recognition method under impulsive noise
- 通信学报 2021年42卷第12期页码：121-133
- 作者机构：
  
  1. 大连理工大学电子信息与电气工程学部，辽宁大连 116024
  2. 江苏师范大学电气工程及自动化学院，江苏徐州 221116
- 作者简介：
  
  [ "戴江安（1991- ），男，江西抚州人，大连理工大学博士生，主要研究方向为波达方向估计、调制识别等" ]
  [ "栾声扬（1983- ），男，辽宁大连人，博士，江苏师范大学讲师，主要研究方向为无线电信号处理、人工智能技术等" ]
  [ "赵明龙（1992- ），男，安徽阜阳人，江苏师范大学硕士生，主要研究方向为深度学习和信号处理" ]
  [ "张兆军（1981- ），男，山东枣庄人，博士，江苏师范大学副教授，主要研究方向为机器学习、群体智能等" ]
  [ "邱天爽（1954- ），男，江苏海门人，博士，大连理工大学教授，主要研究方向为非高斯非平稳随机信号处理" ]
- 基金信息：
  
  国家自然科学基金资助项目(61671105);国家自然科学基金资助项目(61801197)
- DOI：10.11959/j.issn.1000-436x.2021231
  中图分类号： TN911.7
- 网络首发：2021-12，
  
  纸质出版：2021-12-25
- 稿件说明：
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戴江安, 栾声扬, 赵明龙, 等. 脉冲噪声下基于平滑循环相关熵谱的调制识别方法[J]. 通信学报, 2021,42(12):121-133.

Jiang’an DAI, Shengyang LUAN, Minglong ZHAO, et al. Pol-CCES based modulation recognition method under impulsive noise[J]. Journal on Communications, 2021, 42(12): 121-133.
戴江安, 栾声扬, 赵明龙, 等. 脉冲噪声下基于平滑循环相关熵谱的调制识别方法[J]. 通信学报, 2021,42(12):121-133. DOI： 10.11959/j.issn.1000-436x.2021231.

Jiang’an DAI, Shengyang LUAN, Minglong ZHAO, et al. Pol-CCES based modulation recognition method under impulsive noise[J]. Journal on Communications, 2021, 42(12): 121-133. DOI： 10.11959/j.issn.1000-436x.2021231.

摘要

针对脉冲噪声下的信号分类问题，提出了基于平滑循环相关熵谱和浅层残差网络的调制识别方案。所提方案不仅具有较低的计算复杂度，而且在脉冲噪声环境中具有稳健性。仿真实验表明，即使在很低的广义信噪比下，所提方案依然具有良好的性能。

Abstract

To realize signal classification in impulsive noise environment， a modulation recognition scheme based on polished cyclic correntropy spectrum and shallow residual network was proposed.The proposed scheme not only has low computational complexity but also shows robustness to impulsive noise.Simulation results demonstrate the proposed solution’s superior performance under impulsive noise， even when the generalized signal-to-noise ratio is very low.

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references

DOBRE O A , ABDI A , BAR-NESS Y , , et al . Survey of automatic modulation classification techniques:classical approaches and new trends [J ] . IET Communications , 2007 , 1 ( 2 ): 137 .

XU J L , SU W , ZHOU M C . Software-defined radio equipped with rapid modulation recognition [J ] . IEEE Transactions on Vehicular Technology , 2010 , 59 ( 4 ): 1659 - 1667 .

ZHU M T , LI Y J , PAN Z S , et al . Automatic modulation recognition of compound signals using a deep multi-label classifier:a case study with radar jamming signals [J ] . Signal Processing , 2020 , 169 : 107393 .

LI Y B , WANG B , SHAO G P , et al . Automatic modulation classification for short burst underwater acoustic communication signals based on hybrid neural networks [J ] . IEEE Access , 2020 , 8 : 227793 - 227809 .

SAIF W S , ESMAIL M A , RAGHEB A M , et al . Machine learning techniques for optical performance monitoring and modulation format identification:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2020 , 22 ( 4 ): 2839 - 2882 .

NANDI A K , AZZOUZ E E . Modulation recognition using artificial neural networks [J ] . Signal Processing , 1997 , 56 ( 2 ): 165 - 175 .

SWAMI A , SADLER B M . Hierarchical digital modulation classification using cumulants [J ] . IEEE Transactions on Communications , 2000 , 48 ( 3 ): 416 - 429 .

MAJHI S , GUPTA R , XIANG W D , et al . Hierarchical hypothesis and feature-based blind modulation classification for linearly modulated signals [J ] . IEEE Transactions on Vehicular Technology , 2017 , 66 ( 12 ): 11057 - 11069 .

陈红 , 蔡晓霞 , 徐云 , 等 . 基于多重分形特征的通信调制方式识别研究 [J ] . 电子与信息学报 , 2016 , 38 ( 4 ): 863 - 869 .

CHEN H , CAI X X , XU Y , et al . Communication modulation recognition based on multi-fractal dimension characteristics [J ] . Journal of Electronics ＆ Information Technology , 2016 , 38 ( 4 ): 863 - 869 .

PENG S L , JIANG H Y , WANG H X , et al . Modulation classification based on signal constellation diagrams and deep learning [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 ( 3 ): 718 - 727 .

ZHANG J , LI Y , YIN J P . Modulation classification method for frequency modulation signals based on the time-frequency distribution and CNN [J ] . IET Radar,Sonar ＆ Navigation , 2018 , 12 ( 2 ): 244 - 249 .

ZHANG D N , DING W R , LIU C H , et al . Modulated autocorrelation convolution networks for automatic modulation classification based on small sample set [J ] . IEEE Access , 2020 , 8 : 27097 - 27105 .

HUYNH-THE T , HUA C H , PHAM Q V , et al . MCNet:an efficient CNN architecture for robust automatic modulation classification [J ] . IEEE Communications Letters , 2020 , 24 ( 4 ): 811 - 815 .

SHAO M , NIKIAS C L . Signal processing with fractional lower order moments:stable processes and their applications [J ] . Proceedings of the IEEE , 1993 , 81 ( 7 ): 986 - 1010 .

邱天爽 . 相关熵与循环相关熵信号处理研究进展 [J ] . 电子与信息学报 , 2020 , 42 ( 1 ): 105 - 118 .

QIU T S . Development in signal processing based on correntropy and cyclic correntropy [J ] . Journal of Electronics ＆ Information Technology , 2020 , 42 ( 1 ): 105 - 118 .

LUAN S Y , QIU T S , ZHU Y J , et al . Cyclic correntropy and its spectrum in frequency estimation in the presence of impulsive noise [J ] . Signal Processing , 2016 , 120 : 503 - 508 .

MA J T , QIU T S . Automatic modulation classification using cyclic correntropy spectrum in impulsive noise [J ] . IEEE Wireless Communications Letters , 2019 , 8 ( 2 ): 440 - 443 .

MA J T , LIN S C , GAO H J , et al . Automatic modulation classification under non-Gaussian noise:a deep residual learning approach [C ] // Proceedings of 2019 IEEE International Conference on Communications (ICC) . Piscataway:IEEE Press , 2019 : 1 - 6 .

邱天爽 , 栾声扬 , 田全 , 等 . 相关熵与循环相关熵信号处理教程 [M ] . 北京 : 电子工业出版社 , 2021 .

QIU T S , LUAN S Y , TIAN Q , et al . Signal processing of correntropy and cyclic correntropy [M ] . Beijing : Publishing House of Electronics Industry , 2021 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2016 : 770 - 778 .

YAN X , LIU G N , WU H C , et al . Robust modulation classification over α-stable noise using graph-based fractional lower-order cyclic spectrum analysis [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 3 ): 2836 - 2849 .

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