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 regions show that TD-STGT achieves the best performance in forecasting grid-level demand changes, reach...
| Authors | Mohamad Alkadamani, Halim Yanikomeroglu |
|---|---|
| Primary category | eess.SY |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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