Estimating Latency-Reliability in B5G Radio Access Networks: an AI-Empowered Approach

The challenge of ultra-low latency is boosting the interest of Mobile Network Operators (MNOs) in advanced mitigation strategies relying on latency decomposition to trace root causes. This work proposes the first hybrid framework combining analytical and data-driven modeling to estimate Latency-Reliability (LR), defined as the percentage of packets received within a target latency threshold, for different service classes in cellular networks. A preliminary full-stack analysis of Next Generation NodeB operation, coupled with a queuing-theoretic latency model, identifies critical delay stages and maps them to relevant Key Performance Indicators (KPIs) from the wide range of network measurements collected in the Operational Support System. These KPIs feed a module that integrates Supervised Learning, expert knowledge, and statistical analysis to predict LR per service class at cell level, empowering MNOs to take proactive actions in latency-sensitive scenarios. Assessment conducted over real network data shows that the proposed approach achieves latency estimation errors below 10% across service classes, allowing for accurate LR predictions for voice traffic in the absence of ground-truth data. Additionally, dimensionality reduction improves both computational efficiency and model interpretability, supporting targeted latency mitigation.