Blind parameter estimation based matched filter detection for cognitive radio networks

Upadhyay, Shatrunjay; Deshmukh, Siddharth

doi:10.1109/iccsp.2015.7322627

Cited by 4 publications

(1 citation statement)

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“…However, it needs a longer time slot for the sensing phase in frame structure. In matched filter detection, it achieves a desirable value of the detection gain compared to other techniques during a short sensing time [9]. However, it needs the complete information of the PU's signaling features.…”

Section: Introductionmentioning

confidence: 99%

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

Amin

Rahman

Hossain

et al. 2018

BDCC

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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.

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Section: Introductionmentioning

confidence: 99%