FLEXIBLE：利用显式归纳式图学习预测蜂窝网络流量
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
AI总结：
针对现有蜂窝流量图预测方法未考虑基站动态变化的问题，本研究提出FLEXIBLE归纳式图学习方案与泛化GNN预测模型，支持一次训练适配多图且易迁移，较SOTA最高提升9.8%，小样本场景优势显著。
摘要：
从电信行业的角度来看，用户与业务的激增给下一代网络带来了流量需求攀升、资源有限的挑战。准确的流量预测能为网络运营商提供关于网络状况的宝贵见解，并给出最优分配策略的建议。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中一种颇具前景的方法。然而，现有研究受道路交通预测公式的启发，忽略了基站的动态部署与拆除，这就要求基于GNN的预测器能够处理演化图。本研究提出了一种全新的归纳学习方案，以及一个具备泛化能力的基于GNN的预测模型，该模型经过一次训练即可处理多种不同的蜂窝流量图。我们还证明，该模型可通过迁移学习以极低的成本轻松复用，从而适用于不同区域。实验结果表明，与现有最优方法相比，该模型的性能提升最高可达9.8%，在训练数据缩减至20%以下的小样本场景中效果尤为突出。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
