Performance Modeling of Finite-Source Cognitive Radio Networks Using Simulation

Abstract

Abstract. This paper deals with performance modeling of radio frequency licensing. Licensed users (Primary Users -PUs) and normal users (Secondary Users -SUs) are considered. The main idea, is that the SUs are able to access to the available non-licensed radio frequencies.A finite-source retrial queueing model with two non-independent frequency bands (considered as service units) is proposed for the performance evaluation of the system. A service unit with a priority queue and another service unit with an orbit are assigned to the PUs ans SUs, respectively. The users are classified into two classes: the PUs have got a licensed frequency, while the SUs have got a frequency band, too but it suffers from the overloading. We assume that during the service of the non-overloaded band the PUs have preemptive priority over SUs. The involved inter-event times are supposed to be independent, hypoexponentially, hyper-exponentially, lognormal distributed random variables, respectively, depending on the different cases during simulation.The novelty of this work is that we create a new model to analyze the effect of distribution of inter-event time on the mean and variance of the response time of the PUs and SUs.As the validation of the simulation program a model with exponentially distributed inter-event times is considered in which case a continuous time Markov chain is introduced and by the help of MOSEL (MOdeling Specification and Evaluation Language) tool the main performance measures of the system are derived. In several combinations of the distribution of the involved random variables we compare the effect of their distribution on the first and second moments of the response times illustrating in different figures.

Performance evaluation of finite-source cognitive networks with non-reliable services using simulation

“…The figures show that increasing the failure probability of the services involves longer response time of the users and more utilization of the channels service. Also, the figures show the effect of the distribution of the service time on the mean response time and the utilization of PCS when the failure probability is increasing, it is the consequence of [11], in where it was proved that the steady-state distribution is sensitive to the distribution of the service time.…”

Simulation of the performance of CognitiveRadio Networks with unreliable servers

“…This method is one of the most common confidence interval techniques which is used for steady-state simulation output analysis. See for example [1,7,12].…”

“…In papers [8,12] authors took into consideration that the servers are subject to breakdowns. In these works all the inter-event times including the failure and repair times were exponentially distributed.…”

“…In these works all the inter-event times including the failure and repair times were exponentially distributed. Same authors of papers [8,12] have investigated further the CRN with breakdowns, in paper [9] the failure and repair time of the servers are non-exponential distributed (Hypo-exponentially and Hyperexponentially).…”

Performance Simulation of Non-reliable Servers in Finite-Source Cognitive Radio Networks with Collision

“…In order to estimate the mean response times of the requests, the batch mean method is used which is the most popular confidence interval technique for the output analysis of a steady-state simulation, see for example [8][9][10].…”