Faulty node detection in HMM-based cooperative spectrum sensing for cognitive radio networks
Spectrum sensing (SS) is often considered to be one of the highly challenging issues in cognitive radio networks (CRNs), which is being extensively investigated to solve the growing problem RF spectrum scarcity in the next-generation wireless networks. Recently, spectrum prediction assisted cooperative spectrum sensing (CSS) is emerging as an effective way of SS, enabling efficient utilization of two critical resources of SS: time and energy. In this paper, we evaluate the reliability of hidden Markov model (HMM)-based CSS in CRNs, in the presence of random malfunctioning of secondary user (SU) nodes participating in the process. In view of the poor performance of the CSS, especially at low signal-to-noise ratio (SNR) values, we propose a new scheme to detect the possible presence of faulty nodes in the CSS system with high accuracy and quarantine them to maintain the reliability of the spectrum prediction process. The proposed scheme suggests a novel integration of the forward algorithm of HMM with fuzzy-C means clustering technique to design a robust spectrum prediction assisted CSS in CRNs. Simulation results confirm that our scheme delivers a significantly improved receiver operating characteristics compared to a prominent scheme even in the presence of high percentage of failure of SU nodes.
Das, Soumya and Acharya, Tamaghna, "Faulty node detection in HMM-based cooperative spectrum sensing for cognitive radio networks" (2018). Journal Articles. 1213.