Efficient Methods for Performance Evaluations of Call Admission Control Schemes in Multi-Service Cellular Networks

Many dynamic call admission control (CAC) schemes have been proposed in the literature for adaptive reservations in cellular networks. Efficient application of these schemes requires reliable and up-to-date feedback of system performance to the CAC mechanism. However, exact analyses of these schemes in real time using multi-dimensional Markov chain models are challenging due to the need to solve large sets of flow equations. One dimensional Markov chain models have been widely used to derive performance metrics such as call blocking probabilities of multiple traffic classes assuming that all classes of calls have equal capacity requirements and exponentially distributed channel holding times with equal mean values. These assumptions need to be relaxed for a more general evaluation of CAC performance in multi-service cellular networks. In this paper we classify CAC schemes according to their Markov chain models into two categories: symmetric and asymmetric, and develop computationally efficient analytical methods to compute call blocking probabilities of various traffic classes for several widely known CAC schemes under relaxed assumptions. We obtain a product form solution to evaluate symmetric schemes and propose a novel performance evaluation approximation method with low computational cost for asymmetric schemes. Numerical results demonstrate the accuracy and efficiency of the proposed method.