Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand
This work addresses the challenge of limited spectrum resources for regulators and network planners in 6G, though it is incremental as it applies existing methods to new data in a specific domain.
The paper tackled the problem of characterizing spectrum demand patterns for flexible access in 6G networks by developing a data-driven methodology using geospatial analytics and machine learning, achieving a model that captures 70% of variability in demand when tested across urban areas.
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.