AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
It addresses the challenge of efficient spectrum resource allocation for mobile network operators and regulators, though it appears incremental as it applies existing AI/ML methods to a specific domain.
This paper tackles the problem of forecasting spectrum demand for wireless services by developing a data-driven AI/ML approach that uses multiple proxies validated against real-world traffic data, achieving an R^2 value of 0.89 for an enhanced proxy and demonstrating generalizability across five major Canadian cities.
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.