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This research introduces TD-STGT, a graph neural network framework designed to predict fine-grained wireless traffic demand using population data and crowdsourced measurements. By achieving superior accuracy in forecasting grid-level demand changes across multiple Canadian regions, the model offers practical utility for optimizing 5G and future 6G network infrastructure planning. The work represents a significant advancement in applying machine learning techniques to critical telecommunications resource allocation problems.
TD-STGT: A Spatio-Temporal Graph Transformer for Mobile Traffic Demand Forecasting
arXiv:2609.06636v1 Announce Type: cross Abstract: 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 $\Delta R^2$ of 0.462 and reducing $\Delta$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.
Abstract: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 $\Delta R^2$ of 0.462 and reducing $\Delta$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.
Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2609.06636 [eess.SY]
(or arXiv:2609.06636v1 [eess.SY] for this version)
https://doi.org/10.48550/arXiv.2609.06636 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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