Optimal Eigenvalue Weighting Detection for Multi-Antenna Multi Antenna Cognitive Radio Networks
Abstract: art eigenvalue-based eigenvalue based spectrum sensing methods only consider The state-of-the-art the partial information of eigenvalues, 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 multi-antenna antenna cognitive radio networks an and propose eigenvalue weighting-based based detection schemes. According to the Neyman NeymanPearson criterion, the globally optimal weighting solution is the likelihood ratio test (LRT). Hence, we analyze and derive the eigenvalue-based eigenvalue based LRT (E-LRT). (E Utilizing the random om matrix theory, a simple closed closed-form form expression for the E-LRT E is obtained, which is exactly the optimal eigenvalue weighting scheme. Although the E-LRT LRT is optimal, it is infeasible in practice due to its dependence on the knowledge of primary users and noise noise powers. Hence, we further analyze suboptimal methods and design maximum likelihood estimation-based estimation approximation weighting approach. Under the approach, both semi-blind semi (only the noise power is known) and totally-blind totally blind methods are correspondingly proposed. posed. In addition, the theoretical performance analysis of these proposed methods are provided. Simulation results are presented to verify the efficiency of the proposed algorithms.