Accurate kernel-based spectrum sensing for Gaussian and non-Gaussian noise models
Argin Margoosian, Jamshid Abouei, Konstantinos N. Plataniotis
Abstract
This paper introduces a spectrum sensing scenario based on kernel theory which compares favorably against the conventional Energy Detector (ED) in a cognitive radio system. The so-called Kerenlized Energy Detector (KED) can provide superior accuracy in the case of non-Gaussian noise. The incorporation of the nonlinear kernel function in the KED test statistics allows for the development of a nonlinear algorithm capable of considering both higher order and Fractional Lower Order Moments (FLOMs) in the sensing task. Simulation results show that the proposed semi-blind kernelized spectrum sensing algorithm is much robust against impulsive noises and displays a considerably better detection performance than the conventional ED in practical impulsive man-made noises which are generally modeled as the Laplacian and the α-stable distributions. Moreover, for the Gaussian signal and noise model, the performance of the KED scheme is almost identical to that of the conventional ED.
BibTeX
@inproceedings{icassp2015_accuratekernelba,
title = {Accurate kernel-based spectrum sensing for Gaussian and non-Gaussian noise models},
author = {Argin Margoosian and Jamshid Abouei and Konstantinos N. Plataniotis},
booktitle = {ICASSP 2015},
year = {2015}
}