Low-complexity Neighborhood-based Weighted Centroid Localization for Secondary Users in Cognitive Radio Network

Abstract

Traditional spectrum sensing is usually viewed as one of the enabling technologies for cognitive radio (CR) systems due to its capability of guaranteeing the minimum interference between the primary users (PU) and secondary users (SU). In order to determine the possibility of accessing the licensed frequency band (LFB), we propose to exploit the mutual location information of the PUs and SUs in the cognitive radio network (CRN). Specifically, a low-complexity neighborhood-based weighted centroid localization (NB-WCL) algorithm is proposed to acquire the SU localizations. Once the positioning results are obtained, the proposed algorithm assists the SUs in setting their LFB-access flags in the CRN. Simulation results show that the proposed algorithm outperforms some existing conventional localization algorithms with better root mean square error (RMSE) performance. The proposed algorithm can serve as a practically effective candidate solution for LFB status identification in the CRN.