An Improved Blind Spectrum Sensing Algorithm Based on QR Decomposition and SVM

摘要

Spectrum sensing, a basic functionality in cognitive radio, aims at detecting the presence or absence of primary user (PU). As one of the most popular spectrum sensing methods, Covariance-based sensing works based on the correlation between signal samples. However, its performance sharply declines in low Signal Noise Ratio (SNR) environment. To improve detection performance of covariance-based sensing as far as possible, an improved blind spectrum sensing scheme is proposed in this paper on the basis of QR matrix decomposition and support vector machine (SVM). In the proposed scheme, QR matrix decomposition is applied to the co-variance matrix of received signal firstly, and then the main features are constituted by extracting and arranging orderly the upper triangular elements of R matrix. After that, SVM is used to conduct the obtained features and determine whether PU exists. The proposed algorithm does not need the prior information of PU and noise. Simulation results demonstrate that the proposed method has a better performance than conventional covariance-based methods, especially in low SNR scenarios.