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 Km2 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.

中文翻译：

---

ARENA：数据驱动的足球赛事无线电接入网络分析


群体事件是移动网络最具挑战性的场景之一，因为尽管事件的日期和时间通常是提前知道的，但由于其依赖于许多不同的因素，对资源的实际需求很难预测。根据一家欧洲主要运营商在多达 30,000 人的足球场、16 个基站扇区和 1 平方公里区域的大型活动期间提供的数据，我们对此类活动期间的无线接入网络基础设施动态进行了数据驱动的分析。根据从分析中获得的见解，我们开发了 ARENA，这是一种无模型深度学习无线接入网络 (RAN) 容量预测解决方案，该解决方案以过去的网络监控数据和事件上下文信息作为输入，为移动运营商提供有关预期 RAN 的指导未来事件期间所需的容量。我们的结果根据数据集中包含的真实事件进行了验证，说明了我们提出的解决方案的有效性。