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服务与应用对时延具有严格约束并要求服务质量（QoS）保障，这加速了在网络管理流程中引入自主决策与主动决策的需求。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网络中的预测时延进行深入分析。具体而言：(i) 我们提出了用户面时延的解析公式模型——亚指数分布，并通过与实测数据的对比分析验证其有效性；(ii) 我们开展了概率回归、异常检测及预测性前向估计的实验研究，这些研究基于机器学习（ML）领域的新兴技术，如贝叶斯学习（BL）与图机器学习（GML）。我们采用从车辆移动场景、密集城区交通场景及社交集会场景中采集的数据对预测框架进行测试。研究结果揭示了预测算法在实际应用中的效能，并提供了重要见解。