DocumentCode :
711968
Title :
Spectrum Sensing Algorithm Based on Estimated Covariance Matrix MME Detection
Author :
Shaolin Yao ; Zheng Bao Zhang
Author_Institution :
Dept. of Inf. Eng., Ordnance Eng. Coll., Shijiazhuang, China
fYear :
2015
fDate :
24-26 April 2015
Firstpage :
926
Lastpage :
930
Abstract :
Aiming at the problem that the MME detection algorithm has a poor detection performance while sampling data length is small, a spectrum sensing algorithm based on estimated covariance matrix MME was proposed. Covariance matrix estimation of sampling data was made using the oracle-approximating shrinkage estimator, then taking the ratio of MME of estimated covariance matrix as the detection statistic, obtaining the detection threshold through the computer simulation. The results show that the proposed algorithm has better detection performance compared with MME algorithm.
Keywords :
covariance matrices; eigenvalues and eigenfunctions; radio spectrum management; signal detection; covariance matrix estimation; detection statistic; detection threshold; estimated covariance matrix MME detection; maximum-minimum eigenvalue; oracle-approximating shrinkage estimator; spectrum sensing algorithm; Algorithm design and analysis; Covariance matrices; Detection algorithms; Eigenvalues and eigenfunctions; Estimation; Sensors; Signal processing algorithms; cognitive radio; covariance matrix estimation; maximum-minimum eigenvalue; spectrum sensing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Control Engineering (ICISCE), 2015 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4673-6849-0
Type :
conf
DOI :
10.1109/ICISCE.2015.210
Filename :
7120750
Link To Document :
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