发表机构
Communications Research Centre Canada; Carleton University（加拿大通信研究中心; 卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TD-STGT时空图变换器，利用人口规模需求代理预测细粒度移动流量需求变化，在加拿大五个都市区实验中达到最优性能，为5G/6G网络容量升级提供决策支持。
AI 中文摘要
细粒度的移动流量需求预测对于5G及未来6G网络的长期规划至关重要，包括无线电升级、站点加密、回传扩展和频谱激活。本文提出了流量需求时空图变换器（TD-STGT），一种用于预测精细地理网格上无线移动流量需求变化的图神经预测框架。该框架使用基于众包移动测量和白天人口信息开发的人口规模需求代理。在加拿大五个大都市区域的实验表明，TD-STGT在预测网格级需求变化方面取得了最佳性能，达到了0.462的ΔR²，并将ΔRMSE相对于最强基线降低了5.7%。所提出的模型为识别需求压力增加的区域和优先安排未来移动网络容量升级提供了实用工具。
英文摘要
Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densification, backhaul expansion, and spectrum activation. This paper proposes the Traffic Demand Spatio-Temporal Graph Transformer (TD-STGT), a graph neural forecasting framework for predicting changes in wireless mobile traffic demand across fine geographic grids. The framework uses a population-scaled demand proxy developed from crowdsourced mobile measurements and daytime population information. Experiments across five Canadian metropolitan regions show that TD-STGT achieves the best performance in forecasting grid-level demand changes, reaching a $ΔR^2$ of 0.462 and reducing $Δ$RMSE by 5.7\% relative to the strongest baseline. The proposed model provides a practical tool for identifying areas with increasing demand pressure and prioritizing future mobile-network capacity upgrades.
