Parametric mobility support dynamic resource reservation and call admission control scheme for cellular multimedia communications
This paper presents an advanced resource reservation and call admission control scheme for cellular networks. It exploits user mobility information in order to achieve efficient network resource utilization, and avoid severe network congestion. At the same time it achieves reduced service dropping and reduced blocking probabilities. To support the migrating services from the neighbouring cells, the proposed mobility model employs an enhanced cell visiting probability estimator in order to achieve improved mobility prediction. It does this through exploiting the key mobility parameters (speed, direction, and distance) to produce a better estimation of resource reservation requirements. It also uses a probabilistic approach to identify a cluster of cells, the shadow cluster, which the mobile unit is likely to visit. A self-learning, expected travel distance estimation, technique is proposed to overcome the obstacles faced due to the real-world road networks, as well as geographical and physical features of the cells. In each cell of the shadow cluster, the proposed resource reservation strategy identifies a reservation time window, and estimates the amount of resources to be reserved. The call admission control algorithm is invoked when a new or handoff service arrives and needs to allocate resources more efficiently, in order to provide consistent quality of service (QoS) guarantees for multimedia traffic. Concomitantly, to ensure the continuity of on-going services with better utilization of resources, bandwidth is borrowed from the existing adaptive services without affecting the minimum QoS guarantees. Simulation results prove that the proposed scheme offers substantial improvements over recent existing schemes under different traffic patterns. (c) 2006 Elsevier B.V. All rights reserved.
parametric mobility resource reservation call admission control multimedia communications cellular networks quality of service