论文标题
竞技场：数据驱动的无线电访问网络分析足球活动
ARENA: A Data-driven Radio Access Networks Analysis of Football Events
论文作者
论文摘要
大众事件代表了移动网络最具挑战性的方案之一，因为尽管其日期和时间通常是事先知道的，但由于其依赖于许多不同因素，因此对资源的实际需求很难预测。基于欧洲主要航空公司在大规模活动中提供的数据，其中包括30.000人，16个基站行业和$ 1 $ km $^2 $区域，我们对此类活动期间的无线电访问网络基础设施动态进行了数据驱动分析。鉴于从分析中获得的见解，我们开发了Arena，这是一种无模型的深度学习无线电访问网络（RAN）预测解决方案，该解决方案将过去的输入网络监控数据和事件上下文信息作为输入，为移动运营商提供了未来事件中所需的预期RAN容量的指导。根据数据集中包含的真实事件的验证，我们的结果说明了我们提出的解决方案的有效性。
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.
