SDGNet: A Handover-Aware Spatiotemporal Graph Neural Network for Mobile Traffic Forecasting

Accurate mobile traffic prediction at city-scale is becoming increasingly important as data demand surges and network deployments become denser. How mobile networks and user mobility are modelled is key to high-quality forecasts. Prior work builds on distance-based Euclidean (grids) or invariant graph representations, which cannot capture dynamic spatiotemporal correlations with high fidelity. In ...