基于判别式受限玻尔兹曼机的数字调制识别

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

通信学报 ›› 2021, Vol. 42 ›› Issue (2): 81-91.doi: 10.11959/j.issn.1000-436x.2021012

基于判别式受限玻尔兹曼机的数字调制识别

李正权¹^,², 林媛¹, 李梦雅¹, 刘洋¹, 吴琼¹, 邢松³

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

修回日期:2020-11-08 出版日期:2021-02-25 发布日期:2021-02-01
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。
基金资助:
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)

Digital modulation recognition based on discriminative restricted Boltzmann machine

Zhengquan LI¹^,², Yuan LIN¹, Mengya LI¹, Yang LIU¹, Qiong WU¹, Song XING³

¹ Ministerial Key Laboratory of Advanced Control for Light Industry Processes, Jiangnan University, Wuxi 214122, China
² State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
³ Department of Information Systems, California State University, Los Angeles CA90032, USA

Revised:2020-11-08 Online:2021-02-25 Published:2021-02-01
Supported by:
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)

摘要/Abstract

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。

关键词: 调制识别, 受限玻尔兹曼机, 高阶累积量, 数据重构

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.

Key words: modulation recognition, restricted Boltzmann machine, high-order cumulant, data reconstruction

中图分类号:

TN911.7

李正权, 林媛, 李梦雅, 刘洋, 吴琼, 邢松. 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, Yang LIU, Qiong WU, Song XING. Digital modulation recognition based on discriminative restricted Boltzmann machine[J]. Journal on Communications, 2021, 42(2): 81-91.

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调制方式	\|C20\|	\|C21\|	\|C40\|	\|C41\|	\|C42\|	\|C60\|	\|C61\|	\|C62\|	\|C63\|	\|C80\|
BPSK	1	1	2	2	2	16	16	16	16	272
QPSK	0	1	1	0	1	0	4	0	4	3
8PSK	0	1	0	0	1	0	0	0	4	1
8QAM	0.666 7	1	1	0.888 9	1	4.740 7	4.888 9	4.740 7	4.888 9	54.148 1
16QAM	0	1	0.68	0	0.68	0	2.08	0	2.08	13.980 8
4PAM	1	1	1.36	1.36	1.36	8.32	8.32	8.32	8.32	111.846 4
CPFSK	0.000 8	1	0.013 3	0.001 7	0.990 8	0.000 2	0.067 9	0.009 2	3.958 1	0.002 7
GMSK	0.004 0	1	0.317 1	0.007 2	0.963 4	0.017 5	1.186 9	0.038 6	3.845 4	3.157 7

调制方式	y	e_y
BPSK	1	00000001
QPSK	2	00000010
8PSK	3	00000100
8QAM	4	00001000
16QAM	5	00010000
4PAM	6	00100000
CPFSK	7	01000000
GMSK	8	10000000

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

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