The effect of noise correlation on fractional sampling based spectrum sensing

Abstract

Abstract-This paper considers a Fractional Sampling (FS)technique to enhance the Spectrum Sensing (SS) efficiency of a Cognitive Radio (CR) using a decision statistic based on asymptotic Random Matrix Theory (RMT). Firstly, the effect of noise correlation on eigenvalue based SS is studied analytically and by numerical evaluation. Secondly, new bounds for the Standard Condition Number (SCN) are proposed to enhance the SS efficiency in correlated noise scenarios. It is shown that proposed FS method can enhance SS efficiency up to certain FS rates at the expense of receiver complexity and no performance advantage is obtained if the FS rate is increased beyond this limit. As a result, a method for determining the operating point for the FS rate in terms of sensing performance and complexity is suggested.

Maximum Eigenvalue detection for spectrum sensing under correlated noise

“…Gaussian but are correlated across receive dimensions. Depending on the nature of wireless channels and the relation between sensing duration τ and symbol duration Ts, different signal models can be considered [13]. In this work, we assume that τ > Ts and the channel remains constant during the period of sensing.…”

“…To address this issue, we consider the problem of detecting the PU's signal in the presence of noise correlation at the CR receiver. We analyze the considered correlated scenario using a tilted semicircular distribution for modeling the noise covariance matrix as in [22], which resembles the exponential covariance model considered in various previous works [10,13,24,23]. Furthermore, we use recent RMT results on the universality of the maximum eigenvalue of a class of sample covariance matrices from [27] in order to derive the expression for the sensing threshold, which is the main contribution of this paper.…”

“…The contributions related to the eigenvalue based sensing exploiting RMT methods include [6,7,8,10,11,12,13,14,15]. The existing techniques can be categorized into Maximum Eigenvalue (ME) based [14], Signal Condition Number (SCN) based [6,7,10,12] and Scaled Largest Eigenvalue (SLE) based [15,16].…”

“…We define the false alarm deviation as the absolute difference between the observed P f and the target P f used for determining the sensing threshold. Furthermore, we define the probability of correct decision as the number of correct decisions out of the total considered cases under both hypotheses as in [10,13].…”

Cognitive Radio Techniques Under Practical Imperfections: A Survey

“…To address the aforementioned issue, new sensing thresholds for SCN and ME based techniques have been proposed in [139], [140] using recent results from RMT in order to achieve the improved sensing in correlated noise scenarios. The analysis has been carried out by considering a one-sided exponential correlation model to include noise correlation across the receive dimensions and the similar analysis is applicable for temporally correlated case assuming the exponential correlation model still holds.…”