Dynamic threshold-setting for RF-powered cognitive radio networks in non-Gaussian noise

Abstract

The increasing demand for spectral and energy efficient communication networks has drawn an upsurge of interest in frequency (RF) energy harvesting (EH) cognitive radio networks (CRNs), and the spectrum analyzer is the brain to provide different spectral access and energy harvesting opportunities in wireless-powered cognitive radio networks (WP-CRNs). The performance of detecting the existence of primary signals will be more crucial for the EH node. However, to the best of our knowledge, no dynamic threshold-setting method to achieve target detection performance, particularly in the practical non-Gaussian noise, is reported in the literature. Traditional assumption of additive white Gaussian noise (AWGN) fails to model the behavior of certain types of noise found in practice.In this paper we give a dynamic threshold-setting method to detect the existence of primary signals for the energy harvesting node in non-Gaussian noise background. Combined with the fractional lower order statistics (FLOS), we propose a weighting factor for dynamic threshold-setting to exploit the existence of the PU to harvest RF energy, namely weighting factor based fractional lower order moment (WF-FLOM) detector. Numerical simulation results verify that the proposed method is effective for the EH node, when the signal-to-noise ratio is low or the back ground noise varies.