Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System

Big Data and Cognitive Computing

MdKhairul Islam¹  KaziMowdud Ahmed²  MdMahbubur Rahman³  MdSipon Miah³  MdRuhul Amin³  BikashChandra Singh³  MohammadAmazad Hossain⁴ 

[1] Technology University, Sonapur 3814, Noakhali, Bangladesh;Department of Biomedical Engineering, Islamic University, Kushtia 7003, Banglasesh;Department of Information and Communication Engineering, Islamic University, Kushtia 7003, Bangladesh;;Department of Information and Communication Engineering, Noakhali Science &

关键词: cognitive radio network;    spectrum sensing;    Kalman filter;    extended Kalman filter;    unscented Kalman filter;    fuzzy system;   

DOI  :  10.3390/bdcc2040039

来源: DOAJ

【 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.

【 授权许可】

Unknown