• Journal of Communications and Networks
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• 제8권1호
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• pp.28-37
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• 2006

State-dependent call admission control (SDCAC) is proposed to make efficient use of scarce wireless resource in a hierarchical wireless network with heterogeneous traffic. With SDCAC, new calls are accepted according to an acceptance probability taking account of not only cell dwell time but also call holding time and system state (i.e., occupied bandwidth). An analytical method is developed to calculate performance measures of interest, e.g., new call blocking probability, forced termination probability, over. all weighted blocking probability. Numerical results with not only stationary but nonstationary traffic loads are presented to show the robustness of SDCAC. It is shown that SDCAC performs much better than the other considered schemes under nonstationary traffic load.

• 대한산업공학회지
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• 제29권2호
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• pp.135-144
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• 2003

Traffic performance in a microcellular system is much more affected by cell dwell time and channel holding time in each cell. Cell dwell time of a call is characterized by its mobility pattern, i.e., stochastic changes of moving speed and direction. Cell dwell time provides important information for other analyses on traffic performance such as channel holding time, handover rate, and the average number of handovers per call. In the next generation mobile communication system, the cell size is expected to be much smaller than that of current one to accommodate the increase of user demand and to achieve high bandwidth utilization. As the cell size gets small, traffic performance is much more affected by variable mobility of users, especially by that of pedestrians. In previous work, analytical models are based on simple probability models. They provide sufficient accuracy in a simple second-generation cellular system. However, the role of them is becoming invalid in a picocellular environment where there are rapid change of network traffic conditions and highly random mobility of pedestrians. Unlike in previous work, we propose an improved probability model evolved from so-called Random walk model in order to mathematically formulate variable mobility of pedestrians and analyze the traffic performance. With our model, we can figure out variable characteristics of pedestrian mobility with stochastic correlation. The above-mentioned traffic performance measures are analyzed using our model.