Spectrum Sensing Using Robust Principal Component Analysis for Cognitive Radio

Abstract

Spectrum sensing is a critical component in cognitive radio. Meanwhile, robust principal component analysis (rPCA) can decompose a matrix into low-rank and sparse matrices. In general, the covariance matrix of a correlated signal is low-rank and the covariance matrix of white noise is diagonal, which can be regarded as sparse. This fact implies that rPCA can be used as a powerful tool for spectrum sensing. A novel spectrum sensing technique which utilizes the characteristics of covariance matrices and rPCA is proposed in this paper. The proposed scheme is also compared to existing schemes based on sample covariance matrices by simulations.