Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand
2026-03-10 • Artificial Intelligence
Artificial IntelligenceNetworking and Internet Architecture
AI summaryⓘ
The authors developed a method to understand how much wireless spectrum is needed in different places and times, which is important for future 6G networks. They used data analysis and machine learning to study spectrum demand in Canadian cities. Their model could predict about 70% of the changes in spectrum use when applied to new urban areas. This helps policymakers plan better for network needs as wireless usage grows.
6G networksspectrum demandwireless connectivitygeospatial analyticsmachine learningmobile broadbandspectrum accessurban regionsdata-driven modelingnetwork policy
Authors
Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
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.