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

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.