Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

TL;DR

This paper analyzes 5G user-plane latency using a hypoexponential model and validates it with real network data, applying machine learning techniques for prediction, anomaly detection, and forecasting in various mobility scenarios.

Contribution

It introduces a novel analytical model for 5G latency and demonstrates its validation through empirical data and machine learning-based predictive methods.

Findings

Hypoexponential distribution accurately models 5G latency.

Machine learning methods improve predictive accuracy in real scenarios.

Predictive algorithms effectively detect anomalies and forecast latency.

Abstract

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.