Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks

ArXi:2603.10802v1 Announce Type: cross The growing demand for wireless connectivity, combined with limited spectrum resources, calls for efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales.