Predictive Quality of Service for Reliable Wireless Networks

QC 20260415

Abstract

In recent years, emerging technologies have led to an increase in the number of safety-critical services and applications that require reliable communication services. To accommodate performance requirements, mobile network operators offer service level agreements (SLAs) that specify guaranteed quality of service (QoS) targets. However, maintaining these targets is challenging in highly mobile scenarios, where changing propagation conditions, network load, and interference can alter the statistical properties of wireless performance metrics. To address the issues with varying statistical properties, ML-based predictive QoS (pQoS) has been proposed to help the network proactively detect and prevent future network degradations.

This thesis formulates problems of data-driven QoS prediction for wireless networks, with a focus on the wireless channel, throughput, and latency. The main contribution is a set of prediction methods that address both dynamic environments and practical deployment constraints. A comprehensive empirical comparison of neural-network architectures for channel prediction provides guidance for selecting suitable models in mobile wireless settings. To reduce the effects of concept drift in pQoS models, the thesis introduces a distributed joint clustering and prediction framework that groups network cells and trains cluster-level predictors while keeping the number of models manageable. For user-side prediction in high-mobility scenarios, the thesis proposes geographical clustering combined with federated learning, enabling local adaptation while respecting privacy and communication constraints. These prediction frameworks are developed by iterative approximate solvers with convergence guarantees to improve pQoS accuracy. The first algorithm is evaluated using a network digital twin (NDT) simulation tool presented in this thesis. The thesis also presents an NDT framework that predicts the current achievable user throughput based on the network state.

Overall, the results show that combining clustering, distributed learning, and realistic system modeling can substantially improve the robustness of QoS prediction in challenging wireless environments. More generally, the thesis provides methods that can support dependable communication for future wireless networks. Future research should develop a theoretical foundation for integrating uncertainty-aware and SLA-driven objectives into predictive models. This will enable research to create pQoS frameworks that jointly optimize prediction accuracy, risk mitigation for critical services, and adaptability in non-stationary wireless environments.

Link to DiVA