On the effect of false alarm rate on the performance of cognitive radio networks

Abstract

Spectrum sensing plays a significant role in enabling utilization of spectrum holes by unlicensed secondary users (SUs) in cognitive radio networks (CRNs). Most of the related work concerning spectrum sensing has focused on sensing carried out by incoming secondary users (SUs) aiming at locating spectrum opportunities. However, in order to appropriately protect returning licensed primary users (PUs), SUs should continuously perform spectrum sensing during their ongoing transmissions. An important issue associated with the continuous sensing is the false alarm rate (FAR), which is defined as the average number of false alarms per unit of time and can be modeled by a Poisson process with Poisson parameter λFAR. In this paper, we address this issue and develop a continuous time Markov chain (CTMC)-based analytical model to evaluate the effect of the false alarm rate on the performance of CRNs. A major feature of the proposed analytical framework is that it takes into account the effects of sensing errors by both incoming SUs looking for free channels to transmit on and the already transmitting SUs expecting the presence of returning PUs. The analytical model also examines the interference tolerance among PUs and SUs as well as the impact of SUs residual self-interference. The performance results show that high λFAR can severely degrade PUs performance and reduce the overall system resource utilization. However, with increasing PU interference tolerance, PUs performance improves as well. SU residual interference was found to decrease the detection probability resulting in a low PU performance. Extensive simulations validate the analytical model, demonstrating excellent agreement with the theoretical results.