基于判别式受限玻尔兹曼机的数字调制识别

李正权; 林媛; 李梦雅; 刘洋; 吴琼; 邢松

doi:10.11959/j.issn.1000-436x.2021012

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基于判别式受限玻尔兹曼机的数字调制识别

学术论文 | 更新时间：2024-06-05

- 基于判别式受限玻尔兹曼机的数字调制识别
- Digital modulation recognition based on discriminative restricted Boltzmann machine
- 通信学报 2021年42卷第2期页码：81-91
- 作者机构：
  
  1. 江南大学轻工过程先进控制教育部重点实验室，江苏无锡 214122
  2. 北京邮电大学网络与交换技术国家重点实验室，北京 100876
  3. 加利福尼亚州立大学信息系统系，洛杉矶 CA90032
- 作者简介：
  
  [ "李正权（1976- ），男，湖北利川人，博士，江南大学教授，主要研究方向为大规模MIMO等。" ]
  [ "林媛（1995- ），女，安徽铜陵人，江南大学硕士生，主要研究方向为信号处理。" ]
  [ "李梦雅（1997- ），女，安徽淮北人，江南大学硕士生，主要研究方向为NB-IoT的海量接入。" ]
  [ "刘洋（1988- ），男，江苏无锡人，博士，江南大学副教授，主要研究方向为无线信道建模和网络编码。" ]
  [ "吴琼（1986- ），男，江苏徐州人，博士，江南大学副教授，主要研究方向为无人驾驶。" ]
  [ "邢松（1964- ），男，江苏扬州人，博士，加利福尼亚州立大学教授，主要研究方向为5G移动通信。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61571108);国家自然科学基金资助项目(61801193);无锡市科技发展资金资助项目(H20191001);无锡市科技发展资金资助项目(G20192010);江苏省研究生科研与实践创新计划基金资助项目(SJCX20_0781);网络与交换技术国家重点实验室（北京邮电大学）开放课题资助项目(SKLNST-2020-1-13);111引智计划基金资助项目(B12018)
- DOI：10.11959/j.issn.1000-436x.2021012
  中图分类号： TN911.7
- 网络出版日期：2021-02，
  
  纸质出版日期：2021-02-25
- 稿件说明：
移动端阅览
李正权, 林媛, 李梦雅, 等. 基于判别式受限玻尔兹曼机的数字调制识别[J]. 通信学报, 2021,42(2):81-91.

Zhengquan LI, Yuan LIN, Mengya LI, et al. Digital modulation recognition based on discriminative restricted Boltzmann machine[J]. Journal on communications, 2021, 42(2): 81-91.
李正权, 林媛, 李梦雅, 等. 基于判别式受限玻尔兹曼机的数字调制识别[J]. 通信学报, 2021,42(2):81-91. DOI： 10.11959/j.issn.1000-436x.2021012.

Zhengquan LI, Yuan LIN, Mengya LI, et al. Digital modulation recognition based on discriminative restricted Boltzmann machine[J]. Journal on communications, 2021, 42(2): 81-91. DOI： 10.11959/j.issn.1000-436x.2021012.

摘要

为了提高大动态信噪比下数字调制识别性能，提出一种基于高阶累积量和判别式受限玻尔兹曼机的联合调制识别方法。该方法提取数字信号的高阶累积量作为信号特征，综合利用判别式受限玻尔兹曼机的生成能力和分类能力，分析了含有高斯噪声、时变相位偏移或瑞利衰落环境下的数字信号识别率。实验结果表明，与传统识别方法相比，所提方法的识别性能有明显改善。此外，利用该模型的生成能力对输入特征进行重构，可有效提高低信噪比下的信号识别率。

Abstract

In order to improve the performance of digital modulation recognition under high dynamic signal-to-noise ratio

a joint modulation recognition method based on high-order cumulant and discriminative restricted Boltzmann machine was proposed

which extracted the high-order cumulant of digital signals as signal features

comprehensively utilized the generation ability and classification ability of the discriminative restricted Boltzmann machine

analyzed the recognition rate of digital signals in environments containing Gaussian noise

time-varying phase offset or Rayleigh fading.Experimental results show that compared with traditional classification methods

the recognition performance of the proposed method is obviously improved.In addition

the use of the model’s generation ability to reconstruct the input features can effectively improve the signal recognition rate under low signal-to-noise ratio.

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Keywords

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