Cooperative Spectrum Sensing and Weighted-Clustering Algorithm for Cognitive Radio Network

Cognitive radio is a promising technique for efficient utilization of idle authorized spectrum since it is able to sense the spectrum and reuse the frequency when the primary user is absent. In order to overcome the fading, shadowing or hidden terminals in independent detection, cooperative detection is presented. The performance of cooperative sensing is studied in this paper. To enhance the sensing ability, some weighted-cooperative spectrum sensing techniques have been proposed. In this paper, different from the previous studies, we propose a novel weighted-clustering cooperative spectrum sensing algorithm based on distances for cognitive radio network. We firstly classify the secondary users into a few clusters according to several existent methods, and then use cluster-head to collect the observation results come from different secondary users in the same cluster and make a cluster-decision. Considering the different distances between the clusters and the fusion center, different weightings are used to weight the cluster-decisions before combining. The simulation results show that our proposed method improve the probability of detection and reduce the probability of error.