Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.09859 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910048141180928 |
|---|---|
| author | Alkadamani, Mohamad Yanikomeroglu, Halim Ghasemi, Amir |
| author_facet | Alkadamani, Mohamad Yanikomeroglu, Halim Ghasemi, Amir |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09859 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks Alkadamani, Mohamad Yanikomeroglu, Halim Ghasemi, Amir Machine Learning Artificial Intelligence Networking and Internet Architecture Systems and Control 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. |
| title | A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2603.09859 |