An efficient spectrum sensing algorithm for cognitive radio based on finite random matrix

Spectrum sensing is the precondition of implementation of cognitive radio. Motivated by the fact that eigenvalue detection algorithms are based on eigenvalue decomposition over the covariance matrix, we propose an efficient spectrum sensing algorithm based on Cholesky decomposition over that matrix. Using eigenvalues of the matrix which is obtained by Cholesky decomposition over finite covariance matrix, the efficient spectrum sensing algorithm is proposed. Attractive advantages of our proposed technique are: a) no assumptions on the sampling size and the dimension of the random matrix are required; b) exact and simple closed-form analytical expressions for the false alarm probability and decision threshold are derived under practical scenarios of finite size of the covariance matrix and samples; c) numerical simulations show that the presented algorithm achieves performance improvement compared with previous algorithms based on eigenvalue.