Spectrum Sensing Under Non-gaussian Noise Using Fractional Lower Order Moments Covariance MME Detection

In view of the problem that the performance of the traditional spectrum sensing algorithm of the of the maximum minimum eigenvalues (MME) in non-gaussian noise is degraded and even disabled, an improved the fractional lower order moment sampling covariance MME is presented in this paper. The algorithm use fractional lower order moment of observation data preprocessing, scoring low moments of covariance matrix, and maximum ratio of the minimum eigenvalue of matrix as a statistic. This paper adopted the Alpha and Laplace distribution fitting non-gaussian noise environment, Monte Carlo simulation results show that the performance of the fractional lower order moment of covariance MME is superior to MME in non-gaussian situation.