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.
Figures & tables
Fig. 1: Illustration of the HR-GAT hierarchical graph construction.
Feature Category
Description
Population Density
Number of residents per grid tile
Household Density
Number of households per grid tile
Daytime Population
Estimated population during working hours
Economic Activity
Number of businesses by sector
Infrastructure Density
Number of buildings and their total area
Road Network
Total road length and number of roads
TABLE I: Summary of Features Processed for Demand Modeling
Fig. 3: Deployed bandwidth heatmaps at zoom level 15 for Calgary, Montreal, Toronto, Vancouver, and Ottawa. Darker regions indicate higher estimated spectrum deployment.
Proxy Variable
R²
F-Statistic
p-Value
Deployed Bandwidth (PBW)
0.727
4477
<0.001
TABLE II: OLS Regression Results for Proxy Validation
Model
Median MAE ↓
Median RMSE ↓
R² ↑
Gradient Boosting
23.44
44.27
0.86
XGBoost
23.53
44.72
0.86
LightGBM
30.78
55.70
0.80
Decision Tree
41.60
79.92
0.53
Random Forest
45.89
77.48
0.55
Linear Model
55.50
89.31
0.43
TABLE III: Performance Comparison using CBCV
Fig. 4: eCDF of RMSE values across models. HR-GAT demonstrates superior prediction accuracy.
Fig. 5: Log-scaled residual error distribution across cities, showing HR-GAT’s consistent performance.
Model
Median MAE (Ottawa) ↓
Gradient Boosting
37.34
XGBoost
39.49
LightGBM
47.20
Decision Tree
52.47
Random Forest
50.66
Linear Model
60.93
TABLE IV: Median MAE for Ottawa (Unseen Test Set)
Fig. 6: SHAP summary plot illustrating the relative importance of the top 10 features in predicting mobile spectrum demand. Higher absolute SHAP values indicate stronger influence on the model’s estimation.
The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks.
Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
Communications Research Centre Canada, Ottawa, ON, Canada · 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
In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands.
Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
Communications Research Centre, Ottawa, Ontario, Canada · Carleton University, Ottawa, Ontario, Canada