DocumentCode :
1756010
Title :
An Efficient Multiple Lags Selection Method for Cyclostationary Feature Based Spectrum-Sensing
Author :
Juei-Chin Shen ; Alsusa, Emad
Author_Institution :
Sch. of Electr. & Electron. Eng., Univ. of Manchester, Manchester, UK
Volume :
20
Issue :
2
fYear :
2013
fDate :
Feb. 2013
Firstpage :
133
Lastpage :
136
Abstract :
The principle of cognitive radio (CR) systems is to utilize the licensed spectrum when their interference to primary users (PUs) can be maintained below a certain threshold. Thus, to successfully coexist, cognitive users must have awareness of PUs presence in the vicinity. As most communication signals exhibit statistical periodicities, cyclostationary feature detection (CFD) can be used to perform the task of sensing the spectrum for PUs presence. A second-order statistical approach is most widely used to perform CFD in which a set of lags should be chosen for statistical testing. The optimal method for choosing multiple lags requires knowledge of the 4th-order cyclic cumulant of PUs´ signals, which can be a burden in practice. In this letter, we present a new idea for lag set selection with which avoids the mentioned 4th-order cumulant burden. The results, verified via analysis and simulation, show that the performance of the proposed method is comparable to the optimal one in the low signal to-noise ratio region where it is most critical for CR applications.
Keywords :
cognitive radio; radio spectrum management; signal detection; statistical analysis; testing; CFD; CR systems; cognitive radio; cyclostationary feature detection; multiple lags selection method; second-order statistical approach; spectrum sensing; statistical testing; Computational fluid dynamics; Correlation; Covariance matrix; Educational institutions; Signal to noise ratio; Testing; Vectors; Cognitive radio; cyclostationary feature detection; spectrum sensing;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2012.2233471
Filename :
6378397
Link To Document :
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