CASIA OpenIR
Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model
Zhang, Shunchao1; Wang, Yonghua1,2; Yuan, Hantao1; Wan, Pin1,3; Zhang, Yongwei1
Source PublicationSENSORS
2019-09-02
Volume19Issue:18Pages:18
Corresponding AuthorWang, Yonghua(wangyonghua@gdut.edu.cn)
AbstractSpectrum sensing is a core technology in cognitive radio (CR) systems. In this paper, a multiple-antenna cooperative spectrum sensor based on the wavelet transform and Gaussian mixture model (MAWG) is proposed. Compared with traditional methods, the MAWG method avoids the derivation of the threshold and improves the performance of single secondary user (SU) spectrum sensing in cases of channel loss and hidden terminal. The MAWG method reduces the noise of the signal which collected by the multiple-antenna SUs through the wavelet transform. Then, the fusion center (FC) extracts the statistical features from the signals that are pre-processed by the wavelet transform. To extract the statistical features, an sensing data fusion method is proposed. The MAWG method divides all SUs that are involved in the cooperative spectrum sensing into two clusters and extracts a two-dimensional feature vector. In order to avoid complicated decision threshold derivation, the Gaussian mixture model (GMM) is used to train a classifier for spectrum sensing according to these two-dimensional feature vectors. Simulation experiments are performed in the kappa-mu channel model. The simulation shows that the MAWG can effectively improve spectrum sensing performance under the kappa-mu channel model.
Keywordcognitive radio spectrum sensing multiple-antenna wavelet transform Gaussian mixture model
DOI10.3390/s19183863
WOS KeywordCOGNITIVE RADIO NETWORKS
Indexed BySCI
Language英语
Funding Projectspecial funds from the central finance to support the development of local universities[400170044] ; special funds from the central finance to support the development of local universities[400180004] ; national natural science foundation of China[61971147] ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20180106] ; foundation of key laboratory of machine intelligence and advanced computing of the Ministry of Education[MSC-201706A] ; school-enterprise collaborative education project of Guangdong Province[PROJ1007512221732966400] ; foundation of National & Local Joint Engineering Research Center of Intelligent Manufacturing Cyber-Physical Systems and Guangdong Provincial Key Laboratory of Cyber-Physical Systems[008] ; higher education quality projects of Guangdong Province and Guangdong University of Technology
Funding Organizationspecial funds from the central finance to support the development of local universities ; national natural science foundation of China ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; foundation of key laboratory of machine intelligence and advanced computing of the Ministry of Education ; school-enterprise collaborative education project of Guangdong Province ; foundation of National & Local Joint Engineering Research Center of Intelligent Manufacturing Cyber-Physical Systems and Guangdong Provincial Key Laboratory of Cyber-Physical Systems ; higher education quality projects of Guangdong Province and Guangdong University of Technology
WOS Research AreaChemistry ; Engineering ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000489187800049
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26617
Collection中国科学院自动化研究所
Corresponding AuthorWang, Yonghua
Affiliation1.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.South Cent Univ Nationalities, Hubei Key Lab Intelligent Wireless Commun, Wuhan 430074, Hubei, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Shunchao,Wang, Yonghua,Yuan, Hantao,et al. Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model[J]. SENSORS,2019,19(18):18.
APA Zhang, Shunchao,Wang, Yonghua,Yuan, Hantao,Wan, Pin,&Zhang, Yongwei.(2019).Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model.SENSORS,19(18),18.
MLA Zhang, Shunchao,et al."Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model".SENSORS 19.18(2019):18.
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