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.
Figures & tables
Fig. 1: Overview of the spectrum demand estimation methodology.
Fig. 2: Scatter plot comparing deployed bandwidth to download throughput.
Fig. 3: Heatmap of total deployed bandwidth in the GTA.
Data Type
Description
Population Density (Statistics Canada)
Count of individuals per given area during nighttime
Socio-economic Indicators (Statistics Canada)
Data reflecting various socio-economic metrics such as income levels, education attainment, and age demographics
Industry Presence (Statistics Canada)
Density of different industrial sectors, as defined by NACIS, within a specific geographic area
Daytime Population (Environics Analytics)
Count of individuals per given area during daytime
Building Footprints (Microsoft)
Geospatial data representing the outlines of building structures
Non-Residential Buildings (OpenStreetMap)
Geospatial data representing the footprints of non-residential structures
TABLE I: Feature Engineering Data Summary
Fig. 4: Scatter plot comparing the number of transportation hubs and weighted deployed bandwidth.
Model
Scenario
Performance
Ridge Regression
Combined Urban Regions
R2=0.64 , RMSE = 0.96
Ridge Regression
GTA - Vancouver
R2=0.53 , RMSE = 1.9
GBR
Combined Urban Regions
R2=0.81 , RMSE = 0.51
GBR
GTA - Vancouver
R2=0.70 , RMSE = 0.93
TABLE II: Summary of ML Models’ Performance
Fig. 5: Feature importance for spectrum demand estimation.
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
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
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