Pol-CCES based modulation recognition method under impulsive noise

Jiang’an DAI; Shengyang LUAN; Minglong ZHAO; Zhaojun ZHANG; Tianshuang QIU

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

您当前的位置：

首页 >

文章列表页 >

Pol-CCES based modulation recognition method under impulsive noise

Papers | 更新时间：2024-06-05

- Pol-CCES based modulation recognition method under impulsive noise
- Journal on Communications Vol. 42, Issue 12, Pages: 121-133(2021)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61671105);The National Natural Science Foundation of China(61801197)
- DOI：10.11959/j.issn.1000-436x.2021231
  CLC： TN911.7
- Online First：2021-12，
  
  Published：25 December 2021
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

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 .

Views

379

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Semi-supervised signal modulation recognition method based on dual-confidence fusion mechanism

Variable step-size LMP algorithm based on correntropy induced metric

Joint multi-user detection and channel estimation method for grant-free NOMA systems under impulsive noise interference

Joint channel and impulsive noise estimation method for MIMO-OFDM systems

Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal

Related Author

QIU Xin

LI Jinhai

SONG Liubin

YANG Chaosan

MIAO Xuchen

LIU Wenxue

YANG Xinliu

TANG Qi

Related Institution

School of Integrated Circuits, University of Chinese Academy of Sciences

Communications and Information Engineering R&D Center IMECAS

School of Electronic Engineering, Xi’an University of Posts and Telecommunications

China Research Institute of Radiowave Propagation

Faculty of Information Science and Engineering, Ningbo University

AI问答

⁰