DocumentCode
1349795
Title
Multiantenna-Assisted Spectrum Sensing for Cognitive Radio
Author
Wang, Pu ; Fang, Jun ; Han, Ning ; Li, Hongbin
Author_Institution
Dept. of Electr. & Comput. Eng., Stevens Inst. of Technol., Hoboken, NJ, USA
Volume
59
Issue
4
fYear
2010
fDate
5/1/2010 12:00:00 AM
Firstpage
1791
Lastpage
1800
Abstract
In this paper, we consider the problem of detecting a primary user in a cognitive radio network by employing multiple antennas at the cognitive receiver. In vehicular applications, cognitive radios typically transit regions with differing densities of primary users. Therefore, speed of detection is key, and so, detection based on a small number of samples is particularly advantageous for vehicular applications. Assuming no prior knowledge of the primary user´s signaling scheme, the channels between the primary user and the cognitive user, and the variance of the noise seen at the cognitive user, a generalized likelihood ratio test (GLRT) is developed to detect the presence/absence of the primary user. Asymptotic performance analysis for the proposed GLRT is also presented. A performance comparison between the proposed GLRT and other existing methods, such as the energy detector (ED) and several eigenvalue-based methods under the condition of unknown or inaccurately known noise variance, is provided. Our results show that the proposed GLRT exhibits better performance than other existing techniques, particularly when the number of samples is small, which is particularly critical in vehicular applications.
Keywords
antenna arrays; cognitive radio; eigenvalues and eigenfunctions; GLRT; asymptotic performance analysis; cognitive radio; cognitive receiver; eigenvalue-based method; energy detector; generalized likelihood ratio test; multiantenna-assisted spectrum sensing; vehicular application; Cognitive radio (CR); generalized likelihood ratio test (GLRT); spectrum sensing;
fLanguage
English
Journal_Title
Vehicular Technology, IEEE Transactions on
Publisher
ieee
ISSN
0018-9545
Type
jour
DOI
10.1109/TVT.2009.2037912
Filename
5345867
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