A Novel Data Fusion Scheme for Cognitive Radio Networks: An Inverse Distance Weighted and SNR Function Approach

There are many cooperative spectrum sensing means in cognitive radio networks (CRNs) have been proposed. However, the combination between signal noise ratio (SNR) and distance information was rarely considered which can improve the detection performance of cooperative spectrum sensing. We consider CRNs consists of a primary user (PU) and multiple secondary users (SU). Considering the radio environment with the influence which made by path loss, shadow fading and Rayleigh fading in detection accuracy, in order to get a better detection performance for the recognition of PU, a novel data fusion scheme which joint signal noise ratio and inverse distance weighted (IDW-SNR) is proposed. The detection probability and false alarm probability are derived and simulated. Our analysis and simulation results show that the proposed method is better than the conventional manners for the detection performance.