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

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.

Test of 5G System in the Reverberation Chamber at mm-wave

The performance of a real fifth generation base station was studied by using a reverberation chamber as a real life propagating environment. Preliminary tests were conducted in order to define 5G base station operation conditions at mm-wave and emulated scenarios where reconfigurable intelligent surface(s) (RISs) will successively be tested. Measurements campaign was carried out under the H2020 European project RISE-6G and a collaboration program between TIM S.p.A., Nokia and Universita` Politecnica delle Marche.