Neural networks for adaptive vertical handover decision
Neural networks for adaptive vertical handover decision
复制标题
用于自适应垂直切换决策的神经网络
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
P. Godlewski
中科院分区：
文献类型：
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作者：
Sana Horrich;S. B. Jemaa;P. Godlewski
本文主要研究异构无线接入网（RANs）之间的移动性管理。提出了一种提高切换性能的模糊多准则垂直切换算法。该算法基于模糊逻辑控制。模糊逻辑控制器（FLC）基于网络的先验知识，考虑了多个相关的准则和规则。为了学习UMTS和WLAN的FLC参数与吞吐量之间的关系，训练了多层感知器（MLP）神经网络。然后，从吞吐量目标值出发，进行MLP反演，得到隶属函数的最优参数。通过仿真对该切换算法的性能进行了评价，并与传统的垂直切换算法进行了比较。提出的切换算法提高了网络性能。
This paper focuses on mobility management between heterogeneous radio access networks (RANs). A fuzzy multi- criteria vertical handover algorithm enhancing the handover performance is proposed. This algorithm is based on fuzzy logic control. The fuzzy logic controller (FLC) takes into account multiple relevant criteria and rules based on prior knowledge of the network. A multi-layer perceptron (MLP) neural network is trained in order to learn the relationship between the FLC parameters and throughputs on UMTS and WLAN. Then, MLP inversion is performed in order to obtain the optimal parameters of the membership functions starting from throughput objective values. The performances of our handover algorithm are evaluated and compared to a conventional vertical handover algorithm by means of simulations. The proposed handover algorithm improves the network performances.