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Indexed by:期刊论文

Date of Publication:2017-04-01

Journal:IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

Included Journals:SCIE、EI

Volume:16

Issue:4

Page Number:2083-2096

ISSN No.:1536-1276

Key Words:Cognitive radio; spectrum sensing; multi-antenna systems; eigenvalue weighting; likelihood ratio test

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 (E-LRT). Utilizing the random matrix theory, a simple closed-form expression for the E-LRT is obtained, which is exactly the optimal eigenvalue weighting scheme. Although the E-LRT is optimal, it is infeasible in practice due to its dependence on the knowledge of primary users and noise powers. Hence, we further analyze suboptimal methods and design maximum likelihood estimation-based approximation weighting approach. Under the approach, both semi-blind (only the noise power is known) and totally-blind methods are correspondingly proposed. In addition, the theoretical performance analysis of these proposed methods are provided. Simulation results are presented to verify the efficiency of the proposed algorithms.