Accurate spatial prediction of cellular traffic demand is essential for 5G NR capacity planning, network densification, and data-driven 6G planning. Although machine learning can fuse heterogeneous geospatial and socio-economic layers to estimate fine-grained demand maps, spatial autocorrelation can cause neighborhood leakage under naive train/test splits, inflating accuracy and weakening planning reliability. This paper presents an AI-driven framework that reduces leakage and improves spatial generalization via a context-aware two-stage splitting strategy with residual spatial error correction. Experiments using crowdsourced usage indicators across five major Canadian cities show consistent mean absolute error (MAE) reductions relative to location-only clustering, supporting more reliable bandwidth provisioning and evidence-based spectrum planning and sharing assessments.
Figures & tables
Fig. 1: Modeling pipeline.
Fig. 2: Moran’s I versus distance (grid cells).
Fig. 3: Local Moran’s I clusters across five Canadian cities.
Fig. 4: Two-stage clustering and spatial error correction framework.
Fig. 5: Comparison of clustering techniques for Montreal.
City
Mean Absolute Error (MAE)
R 2 Gain (%)
k-Means
Two-Stage
Two-Stage + SEM
Toronto
1532.8
1012.3
845.2
3.85
Montreal
1621.5
1123.6
825.0
2.87
Ottawa
1475.4
987.2
808.3
3.86
Vancouver
1398.6
953.5
783.4
4.88
Calgary
1450.7
1001.9
795.7
3.86
TABLE I: Performance Results Across Five Canadian Cities
Fig. 6: Learning curves comparison for different clustering strategies.
The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.
Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi
Department of Data Science, Communications Research Centre Canada · Department of Systems and Computer Engineering, Carleton University
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 ΔR2 of 0.462 and reducing ΔRMSE 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.
Mohamad Alkadamani, Halim Yanikomeroglu
Communications Research Centre Canada, Ottawa, ON, Canada · Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada
Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R2 value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands.
Colin Brown, Mohamad Alkadamani, Halim Yanikomeroglu
Communications Research Centre, Ottawa, Ontario, Canada · Carleton University, Ottawa, Ontario, Canada