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Weighted Eigenvalues based Spectrum Sensing for Cognitive Radio Systems

Spectrum sensing in low signal to noise ratio region is of great significance in cognitive radio networks. Eigenvalue based spectrum sensing methods are generally used for detecting correlated signals. The performance of these algorithms degrades if the transmitted signal has low correlation. In this paper, a new eigenvalue-based algorithm is proposed for detecting uncorrelated signals. Weights are assigned to the maximum and minimum eigenvalues of the covariance matrix of received signal. Thereafter, the analytical expression for the probability of false alarm is derived by using Random Matrix Theorem. Numerical results verify the effectiveness of the proposed scheme.