Distributed Call Admission Control for a Heterogeneous PCS Network
IEEE Transactions on Computers
Complete Sharing versus Partitioning: Quality of Service Management for Wireless Multimedia Networks
IC3N '98 Proceedings of the International Conference on Computer Communications and Networks
A Predictive End-to-End QoS Scheme in a Mobile Environment
ISCC '01 Proceedings of the Sixth IEEE Symposium on Computers and Communications
ICPADS '04 Proceedings of the Parallel and Distributed Systems, Tenth International Conference
Handoff Prediction by Mobility Characteristics in Wireless Broadband Networks
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
Wireless Personal Communications: An International Journal
Predictive mobility support for QoS provisioning in mobile wireless environments
IEEE Journal on Selected Areas in Communications
IPv6-based dynamic coordinated call admission control mechanism over integrated wireless networks
IEEE Journal on Selected Areas in Communications
Proportional fairness of call blocking probability
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
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Due to the dynamics of topology and resources, Call Admission Control (CAC) plays a significant role for increasing resource utilization ratio and guaranteeing users' QoS requirements in wireless/mobile networks. In this paper, a dynamic multi-threshold CAC scheme is proposed to serve multi-class service in a wireless/mobile network. The thresholds are renewed at the beginning of each time interval to react to the changing mobility rate and network load. To find suitable thresholds, a reward-penalty model is designed, which provides different priorities between different service classes and call types through different reward/penalty policies according to network load and average call arrival rate. To speed up the running time of CAC, an Optimized Genetic Algorithm (OGA) is presented, whose components such as encoding, population initialization, fitness function and mutation etc., are all optimized in terms of the traits of the CAC problem. The simulation demonstrates that the proposed CAC scheme outperforms the similar schemes, which means the optimization is realized. Finally, the simulation shows the efficiency of OGA.