基于多端卷积神经网络的调制识别方法

查雄; 彭华; 秦鑫; 李广; 李天昀

doi:10.11959/j.issn.1000-436x.2019206

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基于多端卷积神经网络的调制识别方法

学术论文 | 更新时间：2024-06-05

- 基于多端卷积神经网络的调制识别方法
- Modulation recognition method based on multi-inputs convolution neural network
- 通信学报 2019年40卷第11期页码：30-37
- 作者机构：
  
  信息工程大学信息系统工程学院，河南郑州 450001
- 作者简介：
  
  [ "查雄（1995- ），男，江西九江人，信息工程大学博士生，主要研究方向为智能信号处理、软件无线电。" ]
  [ "彭华（1973- ），男，江西萍乡人，博士，信息工程大学教授，主要研究方向为通信信号分析。" ]
  [ "秦鑫（1994- ），女，重庆人，信息工程大学硕士生，主要研究方向为雷达信号处理。" ]
  [ "李广（1996- ），男，湖南永州人，信息工程大学硕士生，主要研究方向为卫星通信技术。" ]
  [ "李天昀（1979- ），男，江西萍乡人，博士，信息工程大学副教授，主要研究方向为软件无线电。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61401511)
- DOI：10.11959/j.issn.1000-436x.2019206
  中图分类号： TN911.7
- 网络出版日期：2019-11，
  
  纸质出版日期：2019-11-25
- 稿件说明：
移动端阅览
查雄, 彭华, 秦鑫, 等. 基于多端卷积神经网络的调制识别方法[J]. 通信学报, 2019,40(11):30-37.

Xiong ZHA, Hua PENG, Xin QIN, et al. Modulation recognition method based on multi-inputs convolution neural network[J]. Journal on communications, 2019, 40(11): 30-37.
查雄, 彭华, 秦鑫, 等. 基于多端卷积神经网络的调制识别方法[J]. 通信学报, 2019,40(11):30-37. DOI： 10.11959/j.issn.1000-436x.2019206.

Xiong ZHA, Hua PENG, Xin QIN, et al. Modulation recognition method based on multi-inputs convolution neural network[J]. Journal on communications, 2019, 40(11): 30-37. DOI： 10.11959/j.issn.1000-436x.2019206.

摘要

为识别当前卫星通信系统所采用的主要调制方式，提出了一种基于多端卷积神经网络的通信信号调制识别算法。利用信号的先验信息以及对网络拓扑结构的认知，将信号时域波形转化为眼图和矢量图，作为信号的浅层特征表达，并由此设计了基于多端卷积神经网络的调制识别模型。通过训练所搭建的网络，对浅层特征进行深度提取和映射，最终完成了目标信号的调制识别。仿真实验表明，所提算法相对于传统调制识别算法以及目前基于波形和星座图的深度学习识别算法识别效果更好，当信噪比为5 dB时，识别性能可达95%。

Abstract

In order to identify the main modulation modes adopted in current satellite communication systems

a signal modulation recognition algorithm based on multi-inputs convolution neural network was proposed.With the prior information of the signals and knowledge of the network topological structure

the time-domain signal waveforms were converted into eye diagrams and vector diagrams to represent the shallow features of the signals.Meanwhile

the modulation recognition model based on multi-inputs convolution neural network was designed.Through the training of the network

the shallow features were deeply extracted and mapped.Finally

the signal modulation recognition task was completed.The simulation results show that compared with the traditional algorithms and deep learning algorithms

the proposed method has a better anti-noise performance

and the overall recognition rate of this algorithm can reach 95% when the signal-to-noise ratio is 5 dB.

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references

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