Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912317082435584 |
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| author | Benzaghta, Mohamed Ammar, Sahar López-Pérez, David Shihada, Basem Geraci, Giovanni |
| author_facet | Benzaghta, Mohamed Ammar, Sahar López-Pérez, David Shihada, Basem Geraci, Giovanni |
| contents | Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven HO optimization framework based on high-dimensional Bayesian optimization (HD-BO) and enhanced with transfer learning to reduce training time and improve generalization across different user speeds. Evaluations on a real-world deployment show that HD-BO outperforms 3GPP set-1 and set-5 benchmarks, while transfer learning enables rapid adaptation without loss in performance. This highlights the potential of data-driven, site-specific mobility management in large-scale networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_02633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning Benzaghta, Mohamed Ammar, Sahar López-Pérez, David Shihada, Basem Geraci, Giovanni Information Theory Networking and Internet Architecture Signal Processing Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven HO optimization framework based on high-dimensional Bayesian optimization (HD-BO) and enhanced with transfer learning to reduce training time and improve generalization across different user speeds. Evaluations on a real-world deployment show that HD-BO outperforms 3GPP set-1 and set-5 benchmarks, while transfer learning enables rapid adaptation without loss in performance. This highlights the potential of data-driven, site-specific mobility management in large-scale networks. |
| title | Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning |
| topic | Information Theory Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2504.02633 |