Digital modulation recognition based on discriminative restricted Boltzmann machine

Zhengquan LI; Yuan LIN; Mengya LI; Yang LIU; Qiong WU; Song XING

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

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Digital modulation recognition based on discriminative restricted Boltzmann machine

Papers | 更新时间：2024-06-05

- Digital modulation recognition based on discriminative restricted Boltzmann machine
- Journal on Communications Vol. 42, Issue 2, Pages: 81-91(2021)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61571108);The National Natural Science Foundation of China(61801193);The Wuxi Science and Tech-nology Development Fund(H20191001);The Wuxi Science and Tech-nology Development Fund(G20192010);Postgraduate Research ＆ Practice Innovation Program of Jiangsu Province(SJCX20_0781);Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing Univer-sity of Posts and Telecommunications)(SKLNST-2020-1-13);The 111 Project(B12018)
- DOI：10.11959/j.issn.1000-436x.2021012
  CLC： TN911.7
- Online First：2021-02，
  
  Published：25 February 2021
- 稿件说明：
移动端阅览
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：

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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references

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