Open AccessFeasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix
Author(s) -
Li Zan,
Zhou Fuhui,
Si Jiangbo,
Qi Peihan,
Guan Lei
Publication year - 2016
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2015.0654
Subject(s) - cholesky decomposition , cognitive radio , covariance matrix , eigendecomposition of a matrix , computer science , eigenvalues and eigenvectors , false alarm , spectrum (functional analysis) , algorithm , matrix (chemical analysis) , test statistic , mathematics , mathematical optimization , wireless , statistics , statistical hypothesis testing , telecommunications , artificial intelligence , physics , quantum mechanics , materials science , composite material
Cooperative spectrum sensing, proposed to improve the performance of spectrum sensing in cognitive radio systems where there are multiple secondary users who can cooperatively detect the presence of one primary user, is receiving significant attention. However, few cooperative sensing algorithms take the correlation among the received primary user signals into account. A feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix of the received signals is proposed. The ratio of the maximum eigenvalue to the minimum eigenvalue of the matrix obtained by Cholesky decomposition is used to construct the test statistic. Analytical approximations for the false alarm probability and decision threshold are derived using a moment matching method. The new scheme is in the category of blind cooperative spectrum sensing schemes requiring neither information about the primary user signal nor the channel nor the noise power. The new scheme can work better than the existing eigenvalue‐based cooperative spectrum sensing methods in some conditions, and it has lower complexity.