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Network Selection Algorithm Based on Chi-Square Distance in Heterogeneous Wireless Networks

机译:基于异构无线网络中Chi-Square距离的网络选择算法

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This paper proposes an access network selection algorithm based on a cooperative strategy with Chi square distance, subjective weights for traffic classes and objective attributes of network. The algorithm gets the subjective weights of attribute in four traffic classes using AHP at first, then calculates objective weights using a cooperative method of entropy weight and criteria importance though intercriteria correlation. The final weights are obtained by a comprehensive weighting method based on minimizing the deviation between weights. Finally we measure the Chi square distance between every candidate network and the ideal network and choose the one which has the minimum Chi square distance to the idea network as the best access network. Simulation results show that the proposed algorithm can select the most suitable access network to meet the requirements of current traffic and observably extend the discernibility between candidate networks. The ratio between the best and the second-best network in this algorithm is 8 or 9 times bigger than that in technique for order preference by similarity to ideal solutions, simple additive weighting and grey relational analysis. Furthermore, it can avoid falling into local optimal situation, choose the correct optimal access network when some network attributes appear extreme data, and has good algorithm robustness.
机译:本文提出了一种基于CHI方距离的合作策略的接入网络选择算法,流量类的主观权重以及网络的客观属性。该算法首先使用AHP获得四个流量类别中属性的主观权重,然后使用熵权重的协作方法来计算目标权重,并且虽然具有相互统计器的相关性。基于最小化权重之间的偏差,通过全面的加权方法获得最终权重。最后,我们测量每个候选网络和理想网络之间的CHI方距离,并选择与思想网络的最小CHI方距离作为最佳接入网络。仿真结果表明,该算法可以选择最合适的接入网络,以满足当前流量的要求,并且可观察地延长候选网络之间的可辨别。该算法中最佳和第二最佳网络之间的比率为8或9倍,比通过与理想解决方案的相似性,简单的添加权重和灰色关系分析的顺序优先于技术。此外,它可以避免落入本地最佳情况,当某些网络属性出现极端数据时,选择正确的最佳接入网络,并且具有良好的算法鲁棒性。

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