Uncovering Issues in the Radio Access Network by Looking at the Neighbors
This addresses the challenge for mobile network operators in managing complex RANs by providing a more targeted anomaly detection method, though it is incremental as it builds on existing GNN approaches.
The paper tackles the problem of detecting anomalies in Radio Access Networks (RANs) by developing c-ANEMON, a Graph Neural Network-based tool that analyzes cell behavior relative to neighbors to identify network issues like misconfigurations, achieving 45.95% of long-lasting anomalies in a category likely requiring intervention.
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.