Weighted-Clustering Cooperative Spectrum Sensing in Cognitive Radio Context

In this paper, in order to improve the sensing performance which can be severely degraded when the sensing observations are forwarded to fusion center through fading channels, we apply the clustering method into cooperative spectrum sensing by forming all the secondary users into a few clusters and selecting the most favorable user in each cluster to report sensing results to the data fusion center. This method can exploit the user selection diversity so that the sensing performance can be enhanced. Furthermore, the soft combination EGC and two bit hard combination are introduced to improve the detection performance and reduce transmitting overhead. Compared with conventional sensing scheme, numerical results show that the sensing performance is improved significantly.