AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910048154812416 |
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| author | Brown, Colin Alkadamani, Mohamad Yanikomeroglu, Halim |
| author_facet | Brown, Colin Alkadamani, Mohamad Yanikomeroglu, Halim |
| contents | Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R$^2$ value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09916 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation Brown, Colin Alkadamani, Mohamad Yanikomeroglu, Halim Systems and Control Artificial Intelligence Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R$^2$ value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands. |
| title | AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2603.09916 |