AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation

TL;DR

This paper introduces a data-driven AI and machine learning approach to accurately estimate spectrum demand using proxies validated against real traffic data, aiding efficient spectrum management.

Contribution

It presents a novel AI/ML-based method utilizing proxies from license and crowdsourced data validated with real traffic, improving spectrum demand estimation accuracy.

Findings

Achieved an R² of 0.89 with the enhanced proxy.

Validated models across five Canadian cities.

Demonstrated robustness and generalizability of the approach.

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.