Dynamic User Grouping based on Location and Heading in 5G NR Systems
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909366116941824 |
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| author | Pjanić, Dino Arslantürk, Korkut Emre Cai, Xuesong Tufvesson, Fredrik |
| author_facet | Pjanić, Dino Arslantürk, Korkut Emre Cai, Xuesong Tufvesson, Fredrik |
| contents | User grouping based on geographic location in fifth generation (5G) New Radio (NR) systems has several applications that can significantly improve network performance, user experience, and service delivery. We demonstrate how Sounding Reference Signals channel fingerprints can be used for dynamic user grouping in a 5G NR commercial deployment based on outdoor positions and heading direction employing machine learning methods such as neural networks combined with clustering methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19854 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic User Grouping based on Location and Heading in 5G NR Systems Pjanić, Dino Arslantürk, Korkut Emre Cai, Xuesong Tufvesson, Fredrik Signal Processing Information Theory Machine Learning User grouping based on geographic location in fifth generation (5G) New Radio (NR) systems has several applications that can significantly improve network performance, user experience, and service delivery. We demonstrate how Sounding Reference Signals channel fingerprints can be used for dynamic user grouping in a 5G NR commercial deployment based on outdoor positions and heading direction employing machine learning methods such as neural networks combined with clustering methods. |
| title | Dynamic User Grouping based on Location and Heading in 5G NR Systems |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2410.19854 |