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%.

翻译：从电信角度来看，用户和服务激增给下一代网络带来了日益增长的流量需求和有限的资源挑战。精确的流量预测可为网络运营商提供关于网络状况的宝贵见解，并建议最优分配策略。近年来，采用图神经网络（GNNs）的时空预测已成为蜂窝流量预测的一种有前景的方法。然而，受道路交通流量预测公式启发的现有研究忽视了基站动态部署与移除，要求基于GNN的预测器处理不断演化的图。本文提出了一种新颖的归纳学习方案和一种可泛化的GNN预测模型，该模型可一次性训练处理蜂窝流量的多样化图。我们还证明，该模型可通过迁移学习轻松利用，只需极少的工作量，从而适用于不同区域。实验结果显示，与最先进方法相比，性能提升高达9.8%，尤其是在训练数据减少至20%以下的稀有数据场景中。