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| Auteurs principaux: | , , |
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| Format: | Recurso digital |
| Langue: | anglais |
| Publié: |
Zenodo
2026
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| Accès en ligne: | https://doi.org/10.5281/zenodo.20051347 |
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| _version_ | 1866901360889298944 |
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| author | Sagor, Md. Ifthakhar Khan Rahman, Md. Zillur Mandal, Partha |
| author_facet | Sagor, Md. Ifthakhar Khan Rahman, Md. Zillur Mandal, Partha |
| contents | <p>This study presents a regression-based framework integrating 3GPP TR 38.901 channel models with vendor-specific equipment parameters (Nokia, Huawei, ZTE) to predict 5G link performance across diverse scenarios (0.7–60 GHz). Findings indicate that ANN and decision tree models achieve high throughput accuracy, while mixed-scenario training is essential for model generalization across urban and rural environments.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20051347 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation Sagor, Md. Ifthakhar Khan Rahman, Md. Zillur Mandal, Partha <p>This study presents a regression-based framework integrating 3GPP TR 38.901 channel models with vendor-specific equipment parameters (Nokia, Huawei, ZTE) to predict 5G link performance across diverse scenarios (0.7–60 GHz). Findings indicate that ANN and decision tree models achieve high throughput accuracy, while mixed-scenario training is essential for model generalization across urban and rural environments.</p> |
| title | Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation |
| url | https://doi.org/10.5281/zenodo.20051347 |