Evaluating the Impact of Wi-Fi 6 Migration on QoS/QoE: A Campus Case Study

Abstract

Wi-Fi networks face increasing pressure due to the rapid growth in the number of connected devices, the diversity of applications, and rising user expectations. Managing quality of service (QoS) in such complex environments requires a holistic approach. This study validates a machine learning (ML)-based methodology for comprehensive quality of X (QoX) management, integrating quality of service (QoS), quality of experience (QoE), and quality of business (QoBiz). The approach was evaluated during the migration of the Eduroam Wi-Fi network at the University of the Basque Country (EHU) from Wi-Fi 5 to Wi-Fi 6. Traffic patterns, protocol adoption, performance indicators, and user feedback were analyzed before and after the migration to identify the key quality indicators (KQIs) and to assess the scalability, consistency, and effectiveness of the proposed methodology. Results show that the ML-driven QoX management methodology applied during the migration process enables adaptive, efficient, and user-centric network management. The consistency of improvements across Wi-Fi generations confirms the robustness and scalability of the method for continuous optimization in dynamic wireless environments.