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Indexed by:Journal Papers

Date of Publication:2017-07-01

Journal:IEEE Transactions on Vehicular Technology

Included Journals:SCI

Place of Publication:US

Discipline:Engineering

First-Level Discipline:Information and Communication Engineering

Volume:66

Issue:6

Page Number:5471 - 5475

Abstract:The state-of-the-art eigenvalue-based spectrum sensing methods only consider the partial information of eigenvalues, such as the maximum, minimum, and mean values to make detection, which does not make full use of the eigenvalues to catch correlation. In this paper, we focus on all the eigenvalues of sample covariance matrix in multi-antenna cognitive radio networks and propose eigenvalue weighting-based detection schemes. According to the Neyman-Pearson criterion, the globally optimal weighting solution is the likelihood ratio test (LRT). Hence, we analyze and derive the eigenvalue-based LRT