Performance Analysis of a Cognitive Radio Network Using Network Calculus

Cognitive radio has become a promising technology to increase spectrum utilization through spectrum sharing between licensed users (primary users) and unlicensed users (secondary users). Performance evaluation and analysis is of key importance to get more knowledge of this newly emerged technology. Queueing theory and Markov chain model have been applied to conduct the analysis, where some results are obtained. The existing research sheds light on the performance of cognitive radio networks. However, there are also some limitations. Poisson arrival and exponentially distributed service time are mostly assumed in existing analysis. With these assumptions, existing queueing theory results, particularly M/G/1 priority queue results, can be directly applied, and the Markov chain model can be established. However, these assumptions are too restrictive for modern wireless communication networks, where the traffic can be of different types and the channel capacity can vary over time. In addition, particular focus is made on average values (such as average delay) with little investigation on probabilistic distribution bounds. Therefore, new methodology is needed to make a breakthrough and to bring new insights regarding performance of cognitive radio networks. Network calculus, a newly developed theory, provides a possible solution. It was firstly proposed by R. L. Cruz in 1991, and has involuted into two branches now, i.e., deterministic network calculus and stochastic network calculus. There are two basic concepts in network calculus: (stochastic) arrival curve and (stochastic) service curve, which are used to describe the arrival process of input traffic and the service process of server, respectively. Probabilistic performance guarantees can be analyzed using stochastic network calculus. In addition, the independence between arrival process and service process can be exploited to obtain tighter probabilistic bounds, which is called as independent case analysis. This work is devoted to applying network calculus analysis, particularly stochastic network calculus, to performance evaluation of a cognitive radio