Multi-Scenario Cellular KPI Prediction Based on Spatiotemporal Graph Neural Network

With the increasing demand for high-quality telecommunication services, cellular KPI prediction becomes crucial for telecommunication network monitoring and management. In this work, we propose a novel framework for cellular KPI prediction, which considers its distribution discrepancy under different network operation scenarios. In particular, three specific predictors for normal, target alarm, and neighbor alarm scenarios are proposed based on spatiotemporal graph neural networks and unified through transfer learning. Temporal convolution and attention mechanism are embedded to model the impact of anomalies on KPIs and its propagation across neighboring cells according to the cellular network topology. An experiment on a real cellular KPI dataset shows the effectiveness of the proposed method compared to the state-of-the-arts. Note to Practitioners -Cellular network KPI prediction under scenarios of network alarms is crucial to evaluate the impact of alarms on network services and guides cellular network maintenance policies. This problem is similar to a general multivariate time series prediction problem with data multimodality. However, the first challenge in our case is that, under different scenarios, i.e., normal, target alarm, and neighbor alarm, the effective information and spatiotemporal dependencies among KPIs are different. The second challenge is the imbalanced or sparse sample size for specific scenarios, deteriorating the model performance. This paper proposes a cellular KPI prediction framework consisting of three scenario-specific predictors with similar but different modules to process different scenario-specific data. To address the dataset imbalance across scenarios, we adopt a transfer learning strategy to unify the training and prediction of three predictors. The experiment results on a real cellular KPI dataset demonstrate that the proposed framework is more feasible and effective than the state-of-the-art models for multivariate time series prediction. Future research can consider developing maintenance policies such that the cost caused by abnormal KPIs can be minimized.