Effects of Correlated Shadowing on Soft Decision Fusion in Cooperative Spectrum Sensing

In this paper, the impact of correlated shadowing on collaborative spectrum sensing is investigated in cognitive radio networks. A novel correlation model, called NeSh model, is introduced for more accurately capturing correlated shadowing effects on both sensing and reporting channels. Two soft decision fusion schemes are taken into account and general combining formulas are driven based on the optimal Neyman-Pearson criterion. The effect of different cognitive radio deployments on the performance is also studied. Based on the analysis, a new method for selecting collaborating cognitive radios is proposed to alleviate the effects of correlated shadowing and improve the performance of the system. Numerical results show that, the common assumption of independent and identically distributed (i.i.d.) shadowing is optimistic in most cases and correlated shadowing may have a significant impact on the performance of the system.