Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand

TL;DR

This paper introduces a data-driven approach using geospatial analytics and machine learning to estimate and understand spectrum demand variations in 6G networks, aiding regulators in policy formulation.

Contribution

It presents a novel methodology for characterizing spectrum demand patterns across space using geospatial and machine learning techniques, validated through a case study in Canada.

Findings

Captured 70% of demand variability across urban areas

Demonstrated effectiveness of the model in different regions

Provided insights for spectrum regulation and policy

Abstract

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.