异构无线网络的系统间切换决策模型

Topside E. Mathonsi, Okuthe P. Kogeda, T. Olwal
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引用次数: 10

摘要

在过去的几年里,移动用户的数量呈指数级增长,这些用户希望随时随地都能无线连接到最好的可用网络。异构无线网络(hwn)的存在使移动用户能够始终保持网络连接,并能够访问网络服务和应用程序。然而,提供无缝的系统间切换仍然是一个挑战,因为移动用户仍然会经历很长的切换延迟。这主要是因为先前提出的切换算法无法预测接收信号强度(RSS)的未来值。由于移交发现过程冗长,导致移交延迟。此外,这些先前提出的切换算法只表达了相对比较的尺度,而没有处理多参数使用带来的模糊性和不确定性决策问题。这些切换算法的权重矩阵固定,不能适应网络条件和用户偏好的变化。因此,这些切换算法仍然存在网络选择错误。为此,本文将灰色预测理论(GPT)、多属性决策(MADM)、模糊层次分析法(FAHP)和主成分分析法(PCA)相结合,设计了一种智能系统间切换(IH)算法。大量计算机仿真结果证实,与基于模糊逻辑的垂直切换(FLBVH)算法和自适应神经模糊推理系统(ANFIS)算法相比,IH算法缩短了切换延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intersystem Handover Decision Model for Heterogeneous Wireless Networks
The number of mobile users has exponentially grown over the past years, and these users have the desire of always being connected wirelessly at any time anywhere, to the best available network. The existence of Heterogeneous Wireless Networks (HWNs) allows mobile users to be always connected and have access to network services and applications. However, to provide seamless intersystem handover remains a challenge, since mobile users' still experience lengthy handover delay. This is mainly because, previously proposed handover algorithms fail to predict the future values of the received signal strength (RSS). This led to extensive handover delay due to lengthy handover discovery process. Furthermore, these previously proposed handover algorithms only expresses scales of relative comparison and do not deal with decision problems of fuzziness and uncertainty that comes with the use of multiple parameters. These handover algorithms have fixed weighting matrix and therefore, could not adapt to the change of the network conditions and user's preference. As a result, these handover algorithms still experience erroneous network selection. Consequently, in this paper, an intelligent Intersystem Handover (IH) algorithm was designed by integrating Grey Prediction Theory (GPT), Multiple-Attribute Decision Making (MADM), Fuzzy Analytic Hierarchy Process (FAHP) and Principal Component Analysis (PCA). Numerous computer simulation results confirmed that the proposed IH algorithm shortened handover delay as compared with Fuzzy Logic Based vertical handover (FLBVH) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm.
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