Multiantenna Based Blind Spectrum Sensing via Nonparametric Test

Multiantenna based spectrum sensing algorithms are widely used in cognitive radio networks on account of improving the system reliability. Utilizing the difference between the received signal and the noise statistical covariances, two kinds of novel spectrum sensing algorithms, binomial distribution based detection (BD) and wilcoxon signed rank test based detection (WSD), are proposed based on the sample covariance matrix calculated from a limited number of received signal samples. BD and WSD algorithms do not need any priori information of the primary signal and the noise. In addition, their thresholds are found via the statistical theory. Compared with energy detection (ED), maximum-minimum eigenvalue (MME) and covariance absolute value (CAV), those two algorithms can obtain better performance. Finally, the performance of the proposal is verified by simulations.