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

In the diverse landscape of 6G networks, wherewireless connectivity demands surge and spectrum resourcesremain limited, flexible spectrum access becomes paramount.The success of crafting such schemes hinges on our abilityto accurately characterize spectrum demand patterns acrossspace and time. This paper presents a data-driven methodologyfor estimating spectrum demand variations over spaceand identifying key drivers of these variations in the mobilebroadband landscape. By leveraging geospatial analytics andmachine learning, the methodology is applied to a case study inCanada to estimate spectrum demand dynamics in urban regions.Our proposed model captures 70% of the variability in spectrumdemand when trained on one urban area and tested on another.These insights empower regulators to navigate the complexities of6G networks and devise effective policies to meet future networkdemands.