Abstract
In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.
Highlights
Wireless sensor networks (WSNs) consist of spatially distributed self-governing sensors [1,2], i.e., sensors which monitor physical or environmental conditions
We proposed the unscented Kalman filter (UKF) based on spectrum sensing in a cognitive radio network (CRN) using an adaptive fuzzy system in a nonlinear system, where the unscented transformation is a promising method for computing the value of a random variable in a nonlinear system that undergoes a nonlinear transformation
We proposed UKF scheme based on cooperative spectrum sensing (CSS) in a CRN using an adaptive fuzzy system where, firstly, we developed Algorithm 1 and, secondly, the fuzzification parameters are adapted based on Algorithm 1 from received data at the fusion center (FC)
Summary
Wireless sensor networks (WSNs) consist of spatially distributed self-governing sensors [1,2], i.e., sensors which monitor physical or environmental conditions. To mitigate the impending spectrum starvation problem, an adaptive CSS scheme is proposed to detect spectrum channels accurately under the conditions i.e., the prior information, the prior activity and known SNRs of the PU signal. If these conditions are not available at CUs [23]. Both the KF and EKF do not work in a nonlinear system due to both failing to converge to the true value For these reasons, we proposed the UKF based on spectrum sensing in a CRN using an adaptive fuzzy system in a nonlinear system, where the unscented transformation is a promising method for computing the value of a random variable in a nonlinear system that undergoes a nonlinear transformation.
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