Spectrum Sensing Based on Covariance Matrix under Noise Uncertainty

Energy detection based collaborative spectrum sensing algorithm is recently studied widely for primary signals detection in cognitive radio. However, one major disadvantage of those detectors is that their performance degrades in the presence of noise uncertainty which is inevitable in practical system. In this paper, we introduce a novel statistical covariance matrix based collaborative spectrum sensing algorithm which, confirmed by simulations, can perform better than traditional energy fusion algorithm when noise uncertainty exists. In addition, the decision threshold can be easily obtained through theoretical computing whether the received noise samples at cognitive users are correlated or not.