Abstract
Mobile network operators (MNOs) manage Radio Access Networks (RANs) with massive amounts of cells over multiple radio generations (2G-5G). To handle such complexity, operations teams rely on monitoring systems, including anomaly detection tools that identify unexpected behaviors. In this paper, we present c-ANEMON, a Contextual ANomaly dEtection MONitor for the RAN based on Graph Neural Networks (GNNs). Our solution captures spatio-temporal variations by analyzing the behavior of individual cells in relation to their local neighborhoods, enabling the detection of anomalies that are independent of external mobility factors. This, in turn, allows focusing on anomalies associated with network issues (e.g., misconfigurations, equipment failures). We evaluate c-ANEMON using real-world data from a large European metropolitan area (7,890 cells; 3 months). First, we show that the GNN model within our solution generalizes effectively to cells from previously unseen areas, suggesting the possibility of using a single model across extensive deployment regions. Then, we analyze the anomalies detected by c-ANEMON through manual inspection and define several categories of long-lasting anomalies (6+ hours). Notably, 45.95% of these anomalies fall into a category that is more likely to require intervention by operations teams.
Abstract (translated)
移动网络运营商（MNOs）管理着跨越多个无线代际（2G-5G）的巨大蜂窝网络。为了处理这种复杂性，运营团队依赖于包括异常检测工具在内的监控系统来识别意外行为。在本文中，我们介绍了c-ANEMON，这是一种基于图神经网络（GNNs）的RAN上下文异常监测器。我们的解决方案通过分析单个小区与其本地邻域的行为关系，捕捉到了时空变化，并能独立于外部移动因素检测到异常情况。这反过来使运营团队能够专注于与网络问题（如配置错误、设备故障）相关的异常。 我们使用来自欧洲大都市区的真实数据对c-ANEMON进行了评估（7,890个小区；3个月的数据）。首先，我们展示了我们的解决方案中的GNN模型可以有效地推广到之前未见过的区域内的蜂窝小区，这表明可能在广泛的部署区域内使用单一模型。然后，通过人工检查分析了由c-ANEMON检测出的异常情况，并定义了几种长期持续异常（超过6小时）的类别。值得注意的是，这些异常中有45.95%属于更有可能需要运营团队干预的类别。 该研究结果表明，基于图神经网络的方法在大规模移动网络中有效且具有潜力，能够帮助运营商快速识别和解决关键问题，提高网络性能和用户满意度。
URL
https://arxiv.org/abs/2504.14686
