Generalizable One-Way Delay Prediction Models for Heterogeneous UEs in 5G Networks

From a 5G operator's perspective, accurate estimates of key User Equipments (UEs) performance metrics, especially One-Way Delay (OWD), can provide valuable information. These estimates can trigger management tasks such as reconfiguration to prevent violations of Service Level Objectives (SLOs). Moreover, such insights into UE performance can empower applications to adapt their services to end-users in a more effective manner. We use advanced machine learning over data gathered at the base stations to predict OWD from UEs and show that we are able to predict OWD with over a 2x reduction in percentage error compared to the considered baseline. We discover the close coupling between the performance of the OWD model and the type of UE, which poses a model generalization challenge. Addressing this problem, we demonstrate the shortcomings of the commonly used fine-tuning approach and develop a novel method based on domain adversarial neural networks, that can adapt to a target domain without compromising on the performance of the source domain. Our results show that we can adapt our source model to provide OWD prediction performance within 1-4 percentage points of the ideal scenario when the source and the target domains are the same. Also, our work is grounded in empirical experiments conducted within a 5G testbed, using commercially available hardware.