ARENA: A Data-driven Radio Access Networks Analysis of Football Events
This work addresses capacity planning for mobile operators during mass events like football games, though it appears incremental as it applies deep learning to a known bottleneck in network management.
The authors tackled the problem of unpredictable mobile network demand during mass events by developing ARENA, a model-free deep learning solution for forecasting radio access network capacity, which was validated on real data from football stadium events with up to 30,000 people.
Mass events represent one of the most challenging scenarios for mobile networks because, although their date and time are usually known in advance, the actual demand for resources is difficult to predict due to its dependency on many different factors. Based on data provided by a major European carrier during mass events in a football stadium comprising up to 30.000 people, 16 base station sectors and $1$Km$^2$ area, we performed a data-driven analysis of the radio access network infrastructure dynamics during such events. Given the insights obtained from the analysis, we developed ARENA, a model-free deep learning Radio Access Network (RAN) capacity forecasting solution that, taking as input past network monitoring data and events context information, provides guidance to mobile operators on the expected RAN capacity needed during a future event. Our results, validated against real events contained in the dataset, illustrate the effectiveness of our proposed solution.