A LP-based Admission Control Using Artificial Neural Networks for Integrated Services in Mobile Networks

Abstract

In mobile networks the traffic fluctuation is unpredictable due to mobility and varying resource requirements of multimedia applications. Hence it is essential to maintain the traffic within the network capacity to provide the service guarantees to running applications. This paper proposes an Admission Control (AC) scheme in a single mobile cellular environment supporting real-time and non-real-time application traffic. In the case of a real-time and non-real-time multimedia applications, each application has its own distinct range of acceptable Quality of Service (QoS) requirements (e.g., packet loss, delay, jitter, etc.). The network provides the service by maintaining the application specified QoS range. We propose a Linear Programming Resource Reduction (LP-RR) principle for admission control by maintaining QoS guarantees to existing applications and to increase the percentage of admission to real-time and non-real-time applications. Artificial Neural Networks (ANNs) are used to solve linear programming problem, which facilitates an on-line admission control decision in the practical systems. The simulation results demonstrate that the proposed AC scheme performs well in terms of admitted applications and maintains lower percentage of rejection to hand-off and new applications of different traffic classes. The suggested principle also shown that it is appropriate for the fair resource allocation with improved resource utilization.