AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
2026-03-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed a method using artificial intelligence to predict how much wireless spectrum will be needed in the future. They combined different sources of data, including license records and information collected from users, and checked their accuracy against actual mobile network usage. Their models worked well in five large Canadian cities, showing they can be applied broadly. This work helps regulators better plan and manage wireless spectrum to meet growing demand.
spectrum demandwireless spectrummachine learningartificial intelligencemobile network trafficspectrum allocationcrowdsourced dataR-squared valuespectrum regulationdata-driven forecasting
Authors
Colin Brown, Mohamad Alkadamani, Halim Yanikomeroglu
Abstract
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 R$^2$ 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.