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

• URL: http://arxiv.org/abs/2307.02329v3
• Date: Thu, 31 Aug 2023 12:44:58 GMT
• Title: Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements
• Authors: Marco Skocaj, Francesca Conserva, Nicol Sarcone Grande, Andrea Orsi, Davide Micheli, Giorgio Ghinamo, Simone Bizzarri and Roberto Verdone
• Abstract summary: We present an analytical formulation of the user-plane latency as a Hypoexponential distribution. We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events.
• Score: 0.7643181805997241
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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