Improved spectrum sensing algorithms based on high order moments of eigenvalues.

Blind spectrum sensing algorithms based on eigenvalues has been widely studied in multi-antenna cognitive radio networks. The design of detection algorithms based on eigenvalues relies on the random matrix theory and the characteristics of eigenvalues of sample covariance matrix. Some work has shown that high order moments of eigenvalues can provide additional identification information to improve the performance of statistical inference problems. Motivated by this, an improved spectrum sensing algorithm resorting to the pth moment of all the eigenvalues is proposed, and the analytical representations of false alarm probability and decision threshold are given using the random matrix theory. Moreover, the influence of the power change of high-order moments on the detection performance is analyzed through simulation experiments. The simulation results show the effectiveness of the proposed spectrum sensing algorithm. [ABSTRACT FROM AUTHOR]