用图神经网络分析基站邻居关系，精准识别网络故障异常。
Uncovering Issues in the Radio Access Network by Looking at the Neighbors
- 基于图神经网络建模基站与邻近小区的时空关联
- 在7890个基站上检测到45.95%需人工干预的长期异常
- 模型可跨区域泛化，支持大规模统一部署
移动网络运营商管理着涵盖多个无线代际（2G-5G）的海量基站。为应对复杂性，运维团队依赖监控系统，包括异常检测工具以识别异常行为。本文提出c-ANEMON，一种基于图神经网络（GNN）的上下文异常检测监控系统，通过分析单个基站与其局部邻区的行为关系，捕捉时空变化，从而识别不受外部移动性因素影响的异常，聚焦于网络问题（如配置错误、设备故障）。我们在欧洲某大城市的真实数据（7,890个基站，3个月）上评估该方案：首先证明模型能有效泛化至未见过的区域，表明可在大范围部署中使用单一模型；其次通过人工检查分析检测到的异常，定义了持续6小时以上的长期异常类别，其中45.95%更可能需要运维团队介入。
原文摘要 · Abstract (English)
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.
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