Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements
This addresses the need for proactive latency prediction in 5G networks for mobile network operators, but it is incremental as it applies existing ML methods like Bayesian Learning and Graph ML to new network data.
The study tackled the problem of predicting latency in 5G networks to support autonomous network management, by analyzing real-world data and validating a Hypoexponential distribution model, with experimental results showing efficacy in scenarios like vehicular mobility and dense-urban traffic.
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.