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%.