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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.10802 |
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| _version_ | 1866910049322926080 |
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| author | Alkadamani, Mohamad Ghasemi, Amir Yanikomeroglu, Halim |
| author_facet | Alkadamani, Mohamad Ghasemi, Amir Yanikomeroglu, Halim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10802 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks Alkadamani, Mohamad Ghasemi, Amir Yanikomeroglu, Halim Networking and Internet Architecture Artificial Intelligence Machine Learning Systems and Control 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. |
| title | Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2603.10802 |