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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2309.10575 |
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| _version_ | 1866911965276798976 |
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| author | Xue, Qing Guo, Jiajia Zhou, Binggui Xu, Yongjun Li, Zhidu Ma, Shaodan |
| author_facet | Xue, Qing Guo, Jiajia Zhou, Binggui Xu, Yongjun Li, Zhidu Ma, Shaodan |
| contents | In beamformed wireless cellular systems such as 5G New Radio (NR) networks, beam management (BM) is a crucial operation. In the second phase of 5G NR standardization, known as 5G-Advanced, which is being vigorously promoted, the key component is the use of artificial intelligence (AI) based on machine learning (ML) techniques. AI/ML for BM is selected as a representative use case. This article provides an overview of the AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AI-enabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy, reducing overhead and latency. Finally, the key challenges and open issues in the standardization of AI/ML for BM are discussed, especially the design of new protocols for AI-enabled BM. This article provides a guideline for the study of AI/ML-based BM standardization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10575 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective Xue, Qing Guo, Jiajia Zhou, Binggui Xu, Yongjun Li, Zhidu Ma, Shaodan Information Theory Systems and Control In beamformed wireless cellular systems such as 5G New Radio (NR) networks, beam management (BM) is a crucial operation. In the second phase of 5G NR standardization, known as 5G-Advanced, which is being vigorously promoted, the key component is the use of artificial intelligence (AI) based on machine learning (ML) techniques. AI/ML for BM is selected as a representative use case. This article provides an overview of the AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AI-enabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy, reducing overhead and latency. Finally, the key challenges and open issues in the standardization of AI/ML for BM are discussed, especially the design of new protocols for AI-enabled BM. This article provides a guideline for the study of AI/ML-based BM standardization. |
| title | AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective |
| topic | Information Theory Systems and Control |
| url | https://arxiv.org/abs/2309.10575 |