ATM call admission control based on a neural network for multiple service traffics

다중 서비스 트래픽을 위한 신경회로망 기반의 ATM 호 수락 제어

This paper proposed a new approach to adaptive call admission control based on a neural network for multiple service classes with different quality of service (QoS) in the ATM-based Broadband Integrated Services Digital Networks. the proposed method extend Hiramatsu's neural network based "leaky pattern table" method for the single QoS[1, 2, 3] to deal with multiple services with different QoS by constructing multiple pattern tables based on each service's acceptance or rejection at the call set-up requests, and by simultaneously controlling each service's QoS according to the target QoS of the service and the trunk capacity. Computer simulation results on two service classes with different traffic characteristics and different cell loss rates as QoS, highlight good performance and effectiveness of the proposed call admission controller for multiple service classes.e classes.