This is a dry-run for an upcoming conference presentation (WCNC2025).

As mobile networks become more complex to handle increasing data traffic and a broader range of services, operators must balance the trade-offs between static and dynamic configurations. While traditional static configurations across the entire network are simpler to manage, dynamic adjustments, though more complex to operate, are better suited to adapting to evolving demands. To explore this balance, in this paper, we use real data from a mobile network to evaluate the potential gains in throughput gains, measured by downlink traffic, when dynamically adjusting configurations at both spatial and temporal scales. Our findings show that combining these dynamic adjustments leads to significant performance improvements, with traffic volume gains exceeding 30% when configurations are tailored at the cell level and to the hour scale.

About Juan Montes

Juan Montes has obtained both his B.Sc. (2022) in Physics and M.Sc. (2023) in Physics and mathematics a at the University of Seville, and Granada, respectively. In 2023, he began his PhD in Telematics Engineering at Carlos III University of Madrid. He is currently working on Explainable AI in networking, focusing on making AI-driven network management more interpretable. His research also explores the use of Generative AI models for network-related applications. His main interests include Explainable AI, Deep Learning, and Data Analysis.

This event will be conducted in English