Data-driven Optimization and Transfer Learning for Cellular Network Antenna Configurations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913784316035072 |
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| author | Benzaghta, Mohamed Geraci, Giovanni López-Pérez, David Valcarce, Alvaro |
| author_facet | Benzaghta, Mohamed Geraci, Giovanni López-Pérez, David Valcarce, Alvaro |
| contents | We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting in more than double the 10\%-worst user rates compared to a 3GPP baseline. In scenarios involving aerial users, we identify configurations that increase their median rates fivefold without compromising ground user performance. We further demonstrate the efficacy of model generalization through transfer learning, leveraging available data from a scenario source to predict the optimal solution for a scenario target within a similar number of iterations, without requiring a new initial dataset, and with a negligible performance loss. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_00825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven Optimization and Transfer Learning for Cellular Network Antenna Configurations Benzaghta, Mohamed Geraci, Giovanni López-Pérez, David Valcarce, Alvaro Information Theory Networking and Internet Architecture Signal Processing We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting in more than double the 10\%-worst user rates compared to a 3GPP baseline. In scenarios involving aerial users, we identify configurations that increase their median rates fivefold without compromising ground user performance. We further demonstrate the efficacy of model generalization through transfer learning, leveraging available data from a scenario source to predict the optimal solution for a scenario target within a similar number of iterations, without requiring a new initial dataset, and with a negligible performance loss. |
| title | Data-driven Optimization and Transfer Learning for Cellular Network Antenna Configurations |
| topic | Information Theory Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2504.00825 |