Statistical Cross-Correlation Absolute Value-Based Blind Spectrum Sensing for MIMO Systems

Spectrum sensing can enable unauthorized users to “opportunistically” access an idle spectrum and effectively improve the utilization of spectrum resources. By using the difference between the cross-correlation absolute values of the receiving data in the presence of a signal and only noise, a blind spectrum sensing (SS) algorithm based on the absolute value of the statistical cross correlation (test statistic) is proposed for a multi-input–multioutput system. A theoretical analysis has shown that this method can effectively employ the correlation between signals and the independence of noise to construct the test statistic. The proposed algorithm improves the accuracy of SS as well as the reliability of the system while enabling signal detection with fewer samples. According to the statistical theory, we provide the probability distributions of the test statistic with and without a signal and then determine the corresponding detection thresholds with a fixed false alarm probability. Furthermore, we propose an equivalence theorem of the test statistic and prove its correctness. On this basis, it is theoretically proven that the proposed method is superior to the classical method based on the absolute value of the covariance. Finally, numerical simulations are conducted to confirm the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.