Computer Science > Machine Learning
arXiv:2603.09859 (cs)
[Submitted on 10 Mar 2026]
Title:A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks
View a PDF of the paper titled A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks, by Mohamad Alkadamani and 2 other authors
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Abstract:The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2603.09859 [cs.LG] |
| (or arXiv:2603.09859v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09859
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From: Mohamad Alkadamani [view email][v1] Tue, 10 Mar 2026 16:20:51 UTC (2,263 KB)
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