Towards Real-Time User QoE Assessment via Machine Learning on LTE Network Data
Towards Real-Time User QoE Assessment via Machine Learning on LTE Network Data
复制标题
通过 LTE 网络数据的机器学习实现实时用户 QoE 评估
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Imran
中科院分区：
文献类型：
--
作者：
U. Hashmi;A. Rudrapatna;Zhengxu Zhao;Marek Rozwadowski;Joseph H. Kang;Raj Wuppalapati;A. Imran
众所周知，当前的被动网络管理将无法支持未来蜂窝网络中复杂性和变化速度的指数级增长。考虑到这一点，本文的目标是研究机器学习和预测模型在实时评估细胞级用户体验质量（QoE）方面的适用性。为此，我们利用一个全国性LTE网络运营商在蜂窝级粒度上收集的5周LTE指标数据。应用领域知识，利用网络关键绩效指标（kpi），即调度用户吞吐量、频间切换成功率和频内切换成功率，对用户QoE进行评估。结果表明，将增强树模型应用于精心选择的非共线特征子集，可以实现基于阈值的高精度用户吞吐量和频间切换成功率估计。我们还利用细胞数据特征的周期性，并应用最近开发的时间序列预测模型，称为PROPHET，用于未来的QoE估计。通过在端到端框架内对网络数据进行机器学习和数据分析，网络运营商可以主动识别低性能小区站点以及影响小区性能的影响因素。基于根本原因分析，对低性能的小区站点可以采取适当的纠正措施。
It is well known that current reactive network management would be unable to support the exponential increase in complexity and rapidity of change in future cellular networks. Keeping this in perspective, the goal of this paper is to investigate applicability of machine learning and predictive models to assess cell-level user quality of experience (QoE) in real-time. For this purpose, we leverage a 5 week LTE metrics data collected at cell level granularity for a national LTE network operator. Domain knowledge is applied to assess user QoE with network key performance indicators (KPIs), namely scheduled user throughput, inter-frequency handover success rate and intra-frequency handover success rate. Results indicate that applying boosted trees model on a subset of carefully selected non-collinear features allows high accuracy threshold-based estimation of user throughput and inter-frequency handover success rate. We also exploit the periodic nature of cell data characteristics and apply a recently developed time series prediction model known as PROPHET for future QoE estimation. By employing machine learning and data analytics on network data within an end-to-end framework, network operators can proactively identify low performance cell sites along with the influential factors that impact the cell performance. Based on the root cause analysis, appropriate corrective measures may then be taken for low performance cell sites.