Abstract
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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.
Highlights
M ASS events, such as sport events, religious events, political events or entertainment events are challenging for mobile operators despite being planned with months or weeks ahead in most cases [1]
This contextual information is available, the model capturing the relationship between mobile traffic demand and the specific context of a given event is inherently hard to build because mass events are rare and each is different in nature from one another
We propose a deep learning architecture, namely ARENA that takes as input monitoring metrics from Radio Access Network (RAN) devices as well as other contextual information to assess the extra spectrum resources required during the event;
Summary
M ASS events, such as sport events (e.g., football games), religious events (e.g., holy pilgrimages), political events (e.g., demonstrations) or entertainment events (e.g., concerts) are challenging for mobile operators despite being planned with months or weeks ahead in most cases [1]. The amount of network load resulting from these events depends on contextual features, such as the type of event—different events foster different mobile applications, such as real-time video streaming during concerts; and consumption patterns, such as data avalanches occurring during the breaks of a football match—or the ability of the event to attract attendance, such as the ranking of the matching teams in a football competition This contextual information is available, the model capturing the relationship between mobile traffic demand and the specific context of a given event is inherently hard to build because mass events are rare and each is different in nature from one another. The dataset contains only high-level aggregated and anonymous information
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