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A Mobility Prediction Scheme with a Call Admission Control in Wireless Cellular Network

机译：具有无线蜂窝网络中呼叫准入控制的移动性预测方案

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A new prediction scheme is developed with the support of the Data Mining technique. Based on the mobility prediction, the bandwidth is reserved for the paths with the maximum support, so that the service of the handoff calls can be guaranteed. The Steps Forward with Distance Algorithm (SFDA) uses two criteria, the one of support and the other of distance, and finds the most favorite user path. Moreover, the time factor for each move is also taken into account. For taking global information of the cells at the congestion periods, a centralized collaborative system between nodes and BSs is developed. First, the BSs send the initial information for each user to the nodes. Then the nodes elaborate (using temporal databases techniques), create and give back the new aggregate bandwidth demand information, for each cell to the BS enabling them regulate the service priority. A Call Admission Control (CAC) algorithm is developed for each cell according to the reservation operation in order to minimize the call dropping probability and increase the resource utilization. We are concerned only about the system behavior at exceptional congestion time periods (periodical events), given that it works smoothly on normal daily basis. Simulation results are provided.
机译：通过支持数据挖掘技术开发了一种新的预测方案。基于移动性预测，带宽被保留用于具有最大支持的路径，从而可以保证切换呼叫的服务。使用距离算法（SFDA）向前的步骤使用两个标准，支持和距离的另一个标准，并找到最喜欢的用户路径。此外，还考虑了每个移动的时间因素。为了在拥塞期间采取细胞的全局信息，开发了节点和BS之间的集中协同系统。首先，BSS向节点发送每个用户的初始信息。然后，为BS的每个小区创建和返回新的聚合带宽需求信息，将其创建和返回新的聚合带宽需求信息，使其能够调节其调节服务优先级。根据预约操作为每个单元开发呼叫准入控制（CAC）算法，以便最小化呼叫丢弃概率并提高资源利用率。我们仅关注卓越拥塞时间段（定期事件）的系统行为，因为它在正常的每天顺利工作。提供了仿真结果。

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