The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications.

翻译：新型5G服务和应用对延迟有严格的绑定要求，并需保证服务质量（QoS），这加速了在网络管理流程中融入自主和前瞻性决策的需求。本研究旨在利用移动网络运营商（MNO）可获取的真实网络数据，对5G网络中的预测延迟进行深入分析。具体而言：（i）我们提出了用户面延迟的分析公式，将其表示为次指数分布，并通过与经验测量的比较分析进行了验证；（ii）我们利用机器学习（ML）新兴领域（如贝叶斯学习（BL）和图机器学习（GML））开展了概率回归、异常检测和预测性预报的实验。我们使用从车辆移动、密集城区交通和社交聚集活动场景中收集的数据测试了预测框架。研究结果为预测算法在实际应用中的有效性提供了宝贵见解。