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基于用户偏好的网络质量QoE感知建模仿真分析 被引量:2

Modeling and Simulation Analysis of QoE Perception of Network Quality Based on User Preferences
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摘要 为了解决现有网络质量QoE感知模型数据粗差迭代次数多、线性回归参数小的问题,提出基于用户偏好的网络质量QoE感知建模仿真研究。依据用户偏好理论确定模型参数,并获取网络质量QoE感知数据,以此为基础,通过MCD算法判别并去除网络质量QoE数据粗差,以去除粗差的网络质量QoE数据为基础,利用ROI加权算法提取网络质量QoE数据特征,以得到的网络质量QoE数据特征为依据,将其代入多元线性回归方程计算网络质量QoE感知,实现了基于用户偏好的网络质量QoE感知。实验结果显示,与现有三种网络质量QoE感知模型相比较,构建的网络质量QoE感知模型降低了数据粗差迭代次数,提高了线性回归参数,充分说明构建的网络质量QoE感知模型具备更好的性能。 In order to solve the problems that the existing network quality QoE perception model has more data gross error iterations and smaller linear regression parameters, a simulation study of network quality QoE perception model based on user preferences is proposed. Based on user preference theory, model parameters were determined and network quality QoE perception data were acquired. On this basis, gross errors of network quality QoE data were distinguished and removed by MCD algorithm. On the basis of removing gross errors of network quality QoE data, network quality QoE data features were extracted by ROI weighting algorithm to obtain network quality QoE data. Based on the data characteristics, the QoE perception of network quality wass calculated by substituting it into multiple linear regression equation, and the QoE perception of network quality based on user preferences was realized. The experimental results show that, compared with the three existing network quality QoE perception models, the constructed network quality QoE perception model reduces the number of data gross error iterations, improves the linear regression parameters, and fully demonstrates that the constructed network quality QoE perception model has better performance.
作者 冯刚强 韩一石 王运博 程家豪 FENG Gang-qiang;HAN Yi-shi;WANG Yun-bo;CHENG Jia hao(Guangdong University of Technology School of Information Engineering,Guangdong Guangzhou 510000,China)
出处 《计算机仿真》 北大核心 2020年第4期356-360,共5页 Computer Simulation
基金 广州市产学研重大专项《面向5G网络测试云平台技术研究及产业化》(201604016079)。
关键词 用户偏好 网络质量 感知 粗差 多元线性回归 User preference Network quality Perception Gross error Multiple linear regression
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