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

Yini Fang, Salih Ergüt, Paul Patras

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 this letter we propose SDGNet, a handover-aware spatiotemporal graph neural network that hinges on Dynamic Graph Convolution and Gated Linear Units to predict traffic consumption over short, medium and long time-frames. Experiments with a real-world dataset demonstrate SDGNet outperforms state-of-the-art neural model, attaining up to $4 lower prediction errors.
Original languageEnglish
Pages (from-to)582-586
Number of pages5
JournalIEEE Communications Letters
Volume26
Issue number3
Early online date7 Jan 2022
DOIs
Publication statusPublished - 10 Mar 2022

Keywords / Materials (for Non-textual outputs)

  • mobile traffic forecasting
  • graph neural networks
  • deep learning

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