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

• URL: http://arxiv.org/abs/2603.10802v1
• Date: Wed, 11 Mar 2026 14:11:44 GMT
• Title: Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks
• Abstract summary: This paper builds and validates a spectrum demand proxy from public deployment records.<n>It uses a graph attention network in a hierarchical, multi-resolution setup to estimate spectrum demand at fine spatial scales.
• Score: 17.409018763425
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more 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. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks.

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