Dynamic Multiple-Threshold Call Admission Control Based on Optimized Genetic Algorithm in Wireless/Mobile Networks

Shengling WANG
Yong CUI
Rajeev KOODLI
Yibin HOU
Zhangqin HUANG

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E91-A    No.7    pp.1597-1608
Publication Date: 2008/07/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e91-a.7.1597
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Multi-dimensional Mobile Information Networks)
Category: 
Keyword: 
call admission control,  reward-penalty model,  genetic algorithm,  new call blocking probability,  handoff call dropping probability,  

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Summary: 
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.