基于机器学习的调制格式识别算法研究

靳瑞哲; 迟楠

doi:10.13756/j.gtxyj.2022.06.002

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基于机器学习的调制格式识别算法研究

光通信系统与网络技术 | 更新时间：2023-01-29

- 基于机器学习的调制格式识别算法研究
- Research on Modulation Format Recognition Algorithm based on Machine Learning
- 光通信研究 2022年第6期页码：9-15,57
- 作者机构：
  
  1.复旦大学　通信科学与工程系，电磁波信息科学教育部重点实验室，上海　200433
  2.鹏城实验室，广东　深圳　518055
- 作者简介：
  
  靳瑞哲(2000-)，男，四川成都人。学士，主要研究方向为可见光通信。
  迟楠，教授。E-mail：nanchi@fudan.edu.cn
- 基金信息：
  
  国家自然科学基金杰青资助项目(61925104);国家自然科学基金重点资助项目(62031011);鹏城实验室资助项目(PCL2021A14)
- DOI：10.13756/j.gtxyj.2022.06.002
  中图分类号： TN929
- 纸质出版日期：2022-12-10，
  
  收稿日期：2022-05-01，
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靳瑞哲, 迟楠. 基于机器学习的调制格式识别算法研究[J]. 光通信研究, 2022,(6):9-15,57.

JIN Rui-zhe, CHI Nan. Research on Modulation Format Recognition Algorithm based on Machine Learning[J]. Study on Optical Communications, 2022,(6):9-15,57.
靳瑞哲, 迟楠. 基于机器学习的调制格式识别算法研究[J]. 光通信研究, 2022,(6):9-15,57. DOI： 10.13756/j.gtxyj.2022.06.002.

JIN Rui-zhe, CHI Nan. Research on Modulation Format Recognition Algorithm based on Machine Learning[J]. Study on Optical Communications, 2022,(6):9-15,57. DOI： 10.13756/j.gtxyj.2022.06.002.

摘要

为满足新时代通信需求，新一代通信网络应具有速率更快、容量更大和覆盖范围更广等特点。通过在通信网络中混合应用不同调制格式可以满足新一代通信网络的要求。面对未来通信网络中多种调制格式混用且多变的情况，正确识别信号调制格式可以为后续使用数字算法提升性能提供基础。机器学习因其在特征提取和分类能力上较传统方法具有明显优势，因此将机器学习技术应用于调制格式识别领域成为当下热点。文章介绍了几种应用于基于特征提取分类方案的机器学习方法，并对其应用做出了分析、比较与总结。

Abstract

In order to meet the communication needs of the new era

the new generation communication network should have the characteristics of faster speed

larger capacity and wider coverage. By mixing different modulation formats in communication networks

the requirements of new generation communication networks can be satisfied. Facing the situation that multiple modulation formats are mixed and variable in future communication networks

the correct identification of signal modulation formats can provide the basis for the subsequent use of digital technique to improve the performance. Machine learning has obvious advantages over traditional methods in feature extraction and classification. The application of machine learning techniques to realize modulation format identification has become a hot topic nowadays. This paper introduces several machine learning methods applied to feature extraction-based classification schemes

and try to summarize the field by analyzing and comparing their applications.

关键词

机器学习特征提取调制格式识别深度学习

Keywords

machine learningfeature extractionmodulation format recognitiondeep learning

references

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