A self-diagnosis method for spectrum sensing algorithm in cognitive radio networks

Abstract

Spectrum sensing is an important issue in cognitive radio networks (CRNs). In the most techniques, the spectrum sensing is performed on secondary users (SUs) in a CRN. For reducing the loading of the SUs, the wireless spectrum sensor networks (WSSNs) [1] have been proposed. In WSSN, sensors should provide the primary user (PU)'s interference range and states (active or inactive) to secondary users (SUs). However, due to the hardware defect and PU signal fading, sensors' reports may be incorrect. In this paper, we propose sensor self-diagnosis algorithms that can help sensors to check the correctness of interference range of the PU. According to the simulation results, our algorithms have lower sensing error rate than prior work.