Weighted Centroid Location Based Spectrum Status Identification in Cognitive Radio Network

Due to the non-cooperative coexistence mechanism between the primary users (PUs) and secondary users (SUs), seeking secondary spectrum opportunities is usually unreliable by merely relying on traditional spectrum sensing technology. As one kind of auxiliary information, the mutual location information of the PUs and SUs can assist in determining whether the SU's access to the licensed frequency band (LFB) will interfere the PU. Aiming to identify the LFB status at different locations, we propose a low-complexity neighborhood-based weighted centroid localization (NB-WCL) algorithm to first solve the SU localization problem in the cognitive radio network (CRN). The proposed algorithm is therefore capable of setting the LFB-access flag for the SUs subsequently, based on their positioning results. The root mean square error (RMSE) of the proposed two-dimensional position estimation algorithm is analyzed. Theoretical analysis and experimental results suggest that the proposed algorithm outperforms some existing conventional localization algorithms with more robustness and better error performance. The proposed algorithm can serve as a practically effective candidate solution for LFB status identification in the CRN.