Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key features. However, estimating the end-user experience is not an easy task for network operators. The amount of end-user performance information operators can measure from the network is limited, complicating this approach. Here we explore the calculation of service metrics [known as key quality indicators (KQIs)] from classic low-layer measurements and parameters. We propose a complete machine-learning (ML) modeling framework. This system's low-layer metrics can be applied to measure service-layer performance. To assess the approach, we implemented and evaluated the proposed system on a real cellular network testbed.

翻译：得益于不断演进的蜂窝通信网络，服务提供商能够部署各种服务。很快，5G移动网络将可用于通过移动设备处理大量用户的各类服务和应用。为了有效管理新的5G系统，端到端（E2E）性能分析和优化将成为关键特性。然而，对于网络运营商而言，估算最终用户体验并非易事。运营商能从网络中测量的终端用户性能信息有限，这使该方法变得复杂。本文探讨了如何从经典的低层测量和参数中计算服务指标（称为关键质量指标，KQIs）。我们提出了一个完整的机器学习（ML）建模框架。该系统的低层指标可用于测量服务层性能。为评估该方法，我们在实际蜂窝网络测试台上实施并评估了所提系统。