Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

被引：0
作者：
Yao, Rugui [1 ]
Wang, Peng [1 ]
Zuo, Xiaoya [1 ]
Fan, Ye [1 ]
Yu, Yongsong [1 ]
Pan, Lulu [2 ]
机构：
[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Peoples R China
[2] Northwestern Polytech Univ, Sch Math & Stat, Xian 710072, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
基金：
中国国家自然科学基金;
关键词：
Modulation; Wavelet analysis; Pattern recognition; Discrete wavelet transforms; Entropy; Signal to noise ratio; Feature extraction; Cognitive radio (CR); modulation pattern recognition; wavelet approximate coefficient entropy (WACE); deep neural network (DNN); NEURAL-NETWORK; PERFORMANCE; SIGNALS;
D O I：
10.1109/ACCESS.2020.3044619
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, in order to settle the problem of unintentional interference between communication devices and obtain effective information quickly and accurately in cognitive radio (CR), and an intelligent modulation pattern recognition method based on wavelet approximate coefficient entropy (WACE) is proposed. Based on the traditional wavelet entropy, an improved wavelet entropy, WACE, is presented, which can characterize the modulated signal pattern and suppress the noise effectively. Furthermore, in order to solve the problem of high complexity for linear weighting calculation, the deep neural network (DNN) is adopted, and the vector of the WACE is used as the input of the DNN to realize intelligent recognition of a variety of typical communication signal modulation patterns. Simulation results verify the correctness of the theoretical analysis, and show that the proposed intelligent recognition method can effectively realize the modulation pattern recognition of multiple signals at low signal-to-noise ratio (SNR), with relative low computational complexity.
引用
收藏
页码：226176 / 226187
页数：12
相关论文
共 34 条
[1]  
[Anonymous], 2011, Acm T. Intel. Syst. Tec., DOI DOI 10.1145/1961189.1961199
[2]   The performance comparison of discrete wavelet neural network and discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition [J].
Avci, E. ;
Avci, D. .
EXPERT SYSTEMS WITH APPLICATIONS, 2008, 35 (1-2) :90-101
[3]  
[陈健 Chen Jian], 2006, [电子与信息学报, Journal of electronics & information technology], V28, P2026
[4]  
Chen JB, 2011, NAT PROD COMMUN, V6, P53
[5]   Blind Modulation Recognition of the Lower Order PSK Signals Under the MIMO Keyhole Channel [J].
Das, Dibyajyoti ;
Bora, Prabin Kumar ;
Bhattacharjee, Ratnajit .
IEEE COMMUNICATIONS LETTERS, 2018, 22 (09) :1834-1837
[6]  
Dong SL, 2020, PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), P2123, DOI [10.1109/itnec48623.2020.9085022, 10.1109/ITNEC48623.2020.9085022]
[7]   Spoken Arabic Digits Recognition Using Discrete Wavelet [J].
Elrgaby, Mohammed ;
Amoura, Abdwahab ;
Ganoun, Ali .
2014 UKSIM-AMSS 16TH INTERNATIONAL CONFERENCE ON COMPUTER MODELLING AND SIMULATION (UKSIM), 2014, :275-279
[8]   On the performance of high dimensional data clustering and classification algorithms [J].
Ericson, Kathleen ;
Pallickara, Shrideep .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2013, 29 (04) :1024-1034
[9]   Multi-feature radar signal modulation recognition based on improved PSO algorithm [J].
Gao, Jing-Peng ;
Shen, Liang-Xi ;
Ye, Fang ;
Wang, Shang-Yue ;
Zhang, Ran .
JOURNAL OF ENGINEERING-JOE, 2019, 2019 (19) :5588-5592
[10]   Modulation Classification Method Based on Clustering and Gaussian Model Analysis for VLC System [J].
He, Jing ;
Zhou, Yijun ;
Shi, Jin ;
Tang, Qi .
IEEE PHOTONICS TECHNOLOGY LETTERS, 2020, 32 (11) :651-654
1234