FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning

• URL: http://arxiv.org/abs/2405.08843v1
• Date: Tue, 14 May 2024 07:53:23 GMT
• Title: FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
• Authors: Duc Thinh Ngo, Kandaraj Piamrat, Ons Aouedi, Thomas Hassan, Philippe Raipin-Parvédy,
• Abstract summary: We introduce a novel inductive learning scheme and a generalizable GNN-based forecasting model that can process diverse graphs of cellular traffic with one-time training. Experimental results show up to 9.8% performance improvement compared to the state-of-the-art.
• Score: 1.4216957119562985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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