FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
<|reference_start|>FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning: From a telecommunication standpoint, the surge in users and services challenges next-generation networks with escalating traffic demands and limited resources. Accurate traffic prediction can offer network operators valuable insights into network conditions and suggest optimal allocation policies. Recently, spatio-temporal forecasting, employing Graph Neural Networks (GNNs), has emerged as a promising method for cellular traffic prediction. However, existing studies, inspired by road traffic forecasting formulations, overlook the dynamic deployment and removal of base stations, requiring the GNN-based forecaster to handle an evolving graph. This work introduces a novel inductive learning scheme and a generalizable GNN-based forecasting model that can process diverse graphs of cellular traffic with one-time training. We also demonstrate that this model can be easily leveraged by transfer learning with minimal effort, making it applicable to different areas. Experimental results show up to 9.8% performance improvement compared to the state-of-the-art, especially in rare-data settings with training data reduced to below 20%.<|reference_end|>
@article{ngo2024flexible:,
    title={FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive
  Graph-Based Learning},
    author={Duc Thinh Ngo (STACK), Kandaraj Piamrat (LS2N, STACK), Ons Aouedi,
  Thomas Hassan, Philippe Raipin-Parv'edy},
    journal={arXiv preprint arXiv:2405.08843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08843},
    primaryClass={cs.LG cs.AI cs.NI}
}