Unscented kalman filter based on spectrum sensing in a cognitive radio network using an adaptive fuzzy system

被引：6

作者：

Amin M.R. ^{[1
]}

Rahman M.M. ^{[1
]}

Hossain M.A. ^{[2
]}

Islam M.K. ^{[3
]}

Ahmed K.M. ^{[1
]}

Singh B.C. ^{[1
,4
]}

Miah M.S. ^{[1
]}

机构：

[1] Department of Information and Communication Engineering, Islamic University, Kushtia

[2] Department of Information and Communication Engineering, Noakhali Science & Technology University, Sonapur, Noakhali

[3] Department of Biomedical Engineering, Islamic University, Kushtia

[4] DiSTA, University of Insubriaz, Varese

来源：

Big Data and Cognitive Computing | 2018年 / 2卷 / 04期

关键词：

Cognitive radio network; Extended Kalman filter; Fuzzy system; Kalman filter; Spectrum sensing; Unscented Kalman filter;

D O I：

10.3390/bdcc2040039

中图分类号：

学科分类号：

摘要：

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. c© 2018 by the authors. Licensee MDPI, Basel, Switzerland.

引用

页码：1 / 19

页数：18

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