A Novel Collaborative Spectrum Sensing Scheme Based on Covariance Matrix

The statistical covariance matrixes of received signal and noise are usually different, thus the distinguishing property can be used to detect whether the primary user exists or not. In this paper, according to the multivariate statistical theory, a collaborative spectrum sensing method based on covariance matrix is introduced. The proposed scheme exhibits better performance than the conventional cooperative scheme for correlated signal or correlated noise case, especially for correlated noise case. On the other hand, it approaches to the OR-rule fusion scheme when both signal and noise are independent and identically distributed (i.i.d.). In addition, the decision threshold can be easily obtained through theoretical computing whether the received noise samples at cognitive users are correlated or not. Simulations based on various types of received signals are given to verify the method.