Ensemble Graph Attention Networks for Cellular Network Analytics: From Model Creation to Explainability

In automated radio network control, understanding the effect of different factors on network performance is crucial. Although there are machine learning (ML) solutions that can reliably anticipate network performance expressed as key performance indicator (KPI) values, these models are typically black-box or provide only partial explanations. Most approaches are based on flat data structures and cannot exploit the graph nature of the data, being unable to quantify neighbors’ impact. In this paper, we propose a new graph neural network-based model called Ensemble Graph Attention Network (Ensemble GAT) for network KPI prediction. We show that the proposed model results in better or comparable KPI prediction performance to the state-of-the-art while also carrying information about links between neighboring cells. In addition to model creation, we investigate how explainable AI solutions can be used to provide root-cause explanations for network KPI degradation. To generate feature attributions, we introduce an adapted version of GraphLime that works with ensemble models. In addition, we propose a new technique called Neighbor Perturbation to identify the neighboring cells that have the most significant impact on KPI prediction. We demonstrate the effectiveness of these models on both synthetic and real-world datasets.