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 λ FAR . 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 λ FAR 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.

Performance Analysis of a Cognitive Radio Network with Imperfect Spectrum Sensing

“…Sensing errors of both sensing and transmitting SUs are considered in [25]- [27]. However, these studies have several other limitations including the absence of sensing time and the assumption that a transmitting SU can instantly switch to another channel if one is available upon a PU arrival.…”

“…The secondary user network is a network without infrastructure composed of a set of SUs equipped with cognitive capabilities, where each SU determines whether a channel is occupied or idle based on its own measurements. Furthermore, SUs can coordinate the access to the idle channels with other SUs by exchanging messages on a common control channel [25]; a SU is thus aware of the channels occupied by other SUs and there are no collisions between SUs, as in [26], [27]. Similarly to the primary network, the secondary network is not time-slotted.…”

“…SUs perform spectrum sensing upon arrival to find an idle channel for transmission as well as during transmission to avoid the interference with PUs. As in [30]- [32], we consider the case where each SU operates in full duplex mode, i.e., a SU can sense a channel continuously while transmitting; for an implementation of a SU operating in full duplex mode we refer to [25]. Note that channel sensing of SUs during transmission has a positive impact on the primary network as it decreases the collision probability between a transmitting SU and an arriving PU; it moreover has a positive impact on the secondary network since no white space part is wasted on sensing.…”

“…We assume that a type-I false alarm occurs with probability p f 1 , i.e., p f 1 is the probability that a SU who is searching for an idle channel and has finished sensing an idle channel, wrongly thinks the channel is occupied by a PU and thus fires a type-I false alarm. Like in [25], a transmitting SU wrongly thinks there is a PU arrival and thus fires a type-II false alarm according to a Poisson process with rate δ f 2 . Note that the occurrence of type-II false alarms is not modeled by a probability, but by the rate δ f 2 .…”

Smart Spectrum for Future Wireless World

“…This implies that aspects of statistical information and the effect of statistical information on the smart spectrum should be clarified. Errors may exist in the statistical information estimation and these errors affect the spectrum sharing performance [34]. The statistics of the errors (accuracy of the measurements) are determined by using the spectrum usage measurements.…”