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

Abstract

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

CLC Number:

TN911.7

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.

Figures/Tables 8

References 27

[1]	何继爱, 张文启 . 通信信号调制识别技术及其发展[J]. 高技术通讯, 2016,26(2): 157-165.
	HE J A , ZHANG W Q . The technology for modulation recognition of communication signals and its development[J]. High Technology Letters, 2016,26(2): 157-165.
[2]	SWAMI A , SADLER B M . Hierarchical digital modulation classification using cumulants[J]. IEEE Transactions on Communications, 2000,48(3): 416-429.
[3]	WANG A , LI R . Research on digital signal recognition based on higher order cumulants[C]// 2019 International Conference on Intelligent Transportation,Big Data ＆ Smart City. Piscataway:IEEE Press, 2019,doi:10.1109/ICITBS.2019.00146.
[4]	ZHANG Y , WU G , WANG J ,et al. Wireless signal classification based on high-order cumulants and machine learning[C]// 2017 International Conference on Computer Technology,Electronics and Communication. Piscataway:IEEE Press, 2017,doi:10.1109/ICCTEC.2017.00127.
[5]	PAJIC M S , VEINOVIC M , PERIC M ,et al. Modulation order reduction method for improving the performance of AMC algorithm based on sixth-order cumulants[J]. IEEE Access, 2020,8: 106386-106394.
[6]	张华娣, 楼华勋 . MQAM信号调制方式自动识别方法[J]. 通信学报, 2019,40(8): 200-211.
	ZHANG H D , LOU H X . Automatic modulation recognition algorithm for MQAM signal[J]. Journal on Communications, 2019,40(8): 200-211.
[7]	张茜 . 基于高阶统计特性的调制信号识别[D]. 成都:电子科技大学, 2018.
	ZHANG X . Recognition of digital modulation signals based on high-order statistical characteristics[D]. Chengdu:University of Electronic Science and Technology of China, 2018.
[8]	ABDELBAR M , TRANTER W H , BOSE T . Cooperative cumulants-based modulation classification in distributed networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2018,4(3): 446-461.
[9]	刘明骞, 李兵兵, 曹超凤 ,等. 认知无线电中非高斯噪声下数字调制信号识别方法[J]. 通信学报, 2014,35(1): 82-88.
	LIU M Q , LI B B , CAO C F ,et al. Recognition method of digital modulation signals over non-Gaussian noise in cognitive radio[J]. Journal on Communications, 2014,35(1): 82-88.
[10]	赵雄文, 郭春霞, 李景春 . 基于高阶累积量和循环谱的信号调制方式混合识别算法[J]. 电子与信息学报, 2016,38(3): 674-680.
	ZHAO X W , GUO C X , LI J C . Mixed recognition algorithm for signal modulation schemes by high-order cumulants and cyclic spectrum[J]. Journal of Electronics ＆ Information Technology, 2016,38(3): 674-680.
[11]	XIE W , HU S , YU C ,et al. Deep learning in digital modulation recognition using high order cumulants[J]. IEEE Access, 2019,7: 63760-63766.
[12]	吴少华 . 基于人工蜂群算法的通信信号调制识别研究[D]. 南京:南京邮电大学, 2016.
	WU S H . The research on recognition of communication modulation signal based on artificial bee colony algorithm[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2016.
[13]	YANG F , HAO B , YANG L ,et al. A method of high-precision signal recognition based on higher-order cumulants and SVM[C]// 2018 5th International Conference on Systems and Informatics. Piscataway:IEEE Press, 2018,doi:10.1109/ICSAI.2018.8599313.
[14]	宁暑光 . 基于特征提取与学习的无线通信数字调制方式自动识别方法研究[D]. 合肥:合肥工业大学, 2019.
	NING S G . Research on automatic recognition method of wireless communication digital modulation mode based on feature extraction and learning[D]. Hefei:Hefei University of Technology, 2019.
[15]	杨安锋, 赵知劲, 陈颖 . 利用稀疏自编码器的调制样式识别算法[J]. 信号处理, 2018,34(7): 833-842.
	YANG A F , ZHAO Z J , CHEN Y . Modulation recognition algorithm based on sparse auto-encoder[J]. Journal of Signal Processing, 2018,34(7): 833-842.
[16]	ZHANG Z , HUA Z , LIU Y . Modulation classification in multipath fading channels using sixth-order cumulants and stacked convolutional auto-encoders[J]. IET Communications, 2017,11(6): 910-915.
[17]	雷志坤 . 基于深度学习的调制识别技术研究[D]. 成都:电子科技大学, 2019.
	LEI Z K . Research on deep learning based modulation recognition technologies[D]. Chengdu:University of Electronic Science and Technology of China, 2019.
[18]	PEND S , JIANG H , WANG H ,et al. Modulation classification based on signal constellation diagrams and deep learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019,30: 718-727.
[19]	SANG Y , LI L A . Application of novel architectures for modulation recognition[C]// 2018 IEEE Asia Pacific Conference on Circuits and Systems. Piscataway:IEEE Press, 2018,doi:10.1109/APCCAS.2018.8605691.
[20]	XU Y , LI D , WANG Z ,et al. A deep learning method based on convolutional neural network for automatic modulation classification of wireless signals[J]. Wireless Networks, 2017,25(7): 3735-3746.
[21]	WEST N E , O’SHEA T . Deep architectures for modulation recognition[C]// 2017 IEEE International Symposium on Dynamic Spectrum Access Networks. Piscataway:IEEE Press, 2017: 1-6.
[22]	SALAKHUTDINOV R , MNIH A , HINTON G . Restricted Boltzmann machines for collaborative filtering[C]// 2007 International Conference on Machine Learning. New York:ACM Press, 2007: 791-798.
[23]	杨安锋 . 基于深度学习的调制样式识别算法研究[D]. 杭州:杭州电子科技大学, 2018.
	YANG A F . Research on modulation type recognition algorithm based on deep learning[D]. Hangzhou:Hangzhou Dianzi University, 2018.
[24]	LAROCHELLE H , MANDEL M , PASCANU R ,et al. Learning algorithms for the classification restricted Boltzmann machine[J]. Journal of Machine Learning Research, 2012,13(1): 643-669.
[25]	CHEN C L P , FENG S . Generative and discriminative fuzzy restricted Boltzmann machine learning for text and image classification[J]. IEEE Transactions on Cybernetics, 2018,50(5): 2237-2248.
[26]	KINGMA D P , BA J . Adam:a method for stochastic optimization[C]// 2015 International Conference on Learning Representations.[S.n.:s.l.], 2015: 1-13.
[27]	GLOROT X , BENGIO Y . Understanding the difficulty of training deep feedforward neural networks[C]// 2010 International Conference on Artificial Intelligence and Statistics. Piscataway:IEEE Press, 2010: 249-256.

Metrics

Recommended 0

No Suggested Reading articles found!

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

Digital modulation recognition based on discriminative restricted Boltzmann machine

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 27

Related Articles 7

Metrics

Recommended 0

[1]	Zeliang AN, Tianqi ZHANG, Baoze MA, Pan DENG, Yuqing XU. Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal [J]. Journal on Communications, 2021, 42(7): 84-94.
[2]	Jiang’an DAI, Shengyang LUAN, Minglong ZHAO, Zhaojun ZHANG, Tianshuang QIU. Pol-CCES based modulation recognition method under impulsive noise [J]. Journal on Communications, 2021, 42(12): 121-133.
[3]	Sicheng ZHANG,Yun LIN,Ya TU,Shiwen Mao. Electromagnetic signal modulation recognition technology based on lightweight deep neural network [J]. Journal on Communications, 2020, 41(11): 12-21.
[4]	Xiong ZHA,Hua PENG,Xin QIN,Guang LI,Tianyun LI. Modulation recognition method based on multi-inputs convolution neural network [J]. Journal on Communications, 2019, 40(11): 30-37.
[5]	Hongguang LI, Ying GUO, Ping SUI, Zisen QI. Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature [J]. Journal on Communications, 2019, 40(10): 20-29.
[6]	Yi-yin LIU,Guo-rui LI,Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network [J]. Journal on Communications, 2016, 37(Z1): 211-218.
[7]	Ming-qian LIU,Bing-bing LI,Chao-feng CAO,Zhao LI. Recognition method of digital modulation signals over non-Gaussian noise in cognitive radio [J]. Journal on Communications, 2014, 35(1): 82-88.