FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
Year: 2024
Type: conference-paper
Abstract: 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 $20 \%$.
Institutions: Orange (France), École Centrale de Nantes, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Laboratoire des Sciences du Numérique de Nantes, IMT Atlantique, Nantes Université, University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405974750