Correlation Based Secondary Users Selection for Cooperative Spectrum Sensing Network

Cognitive radio (CR) can significantly enhance spectrum efficiency by dynamical accessing the licensed spectrum. However, single user spectrum sensing may be inaccurate and the second user (SU) may preempt the channel of the primary users (PUs). The appearance of cooperative spectrum sensing (CSS) can effectively improve the spectrum sensing performance by fusing the results of multiple SUs’ decisions to yield reliable decisions. Nevertheless, the communication overhead and the energy consumption of SUs bring a heavy burden for the resource limited secondary network. Therefore, in this paper, we propose a correlation based scheme to select representative SUs based on their correlation by using improved Density-Based Spatial Clustering of Applications algorithm (DSCN). First, we set a threshold to screen out SUs with good channel quality. Then, we propose a improved DSCN algorithm to select SUs that participate in CSS. This algorithm can select representative SUs based on their correlations. Simulation results show that the sensing overhead has been greatly reduced and the probability of detection and the probability of false alarm are better than the traditional spectrum sensing schemes.