SOURCE-LINKED INTELLIGENCE
TD-STGT: A Spatio-Temporal Graph Transformer for Mobile Traffic Demand Forecasting
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 region
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-06T14:33:19.000Z
First collected: 2026-09-20T21:12:06.801Z. This is not the publication date.