Coded Beam Training
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914704390094848 |
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| author | Zheng, Tianyue Zhu, Jieao Yu, Qiumo Yan, Yongli Dai, Linglong |
| author_facet | Zheng, Tianyue Zhu, Jieao Yu, Qiumo Yan, Yongli Dai, Linglong |
| contents | In extremely large-scale multiple input multiple output (XL-MIMO) systems for future sixth-generation (6G) communications, codebook-based beam training stands out as a promising technology to acquire channel state information (CSI). Despite their effectiveness, when the pilot overhead is limited, existing beam training methods suffer from significant achievable rate degradation for remote users with low signal-to-noise ratio (SNR). To tackle this challenge, leveraging the error-correcting capability of channel codes, we introduce channel coding theory into hierarchical beam training to extend the coverage area. Specifically, we establish the duality between hierarchical beam training and channel coding, and the proposed coded beam training scheme serves as a general framework. Then, we present two specific implementations exemplified by coded beam training methods based on Hamming codes and convolutional codes, during which the beam encoding and decoding processes are refined respectively to better accommodate the beam training problem. Simulation results have demonstrated that the proposed coded beam training method can enable reliable beam training performance for remote users with low SNR while keeping training overhead low. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01673 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coded Beam Training Zheng, Tianyue Zhu, Jieao Yu, Qiumo Yan, Yongli Dai, Linglong Information Theory Signal Processing In extremely large-scale multiple input multiple output (XL-MIMO) systems for future sixth-generation (6G) communications, codebook-based beam training stands out as a promising technology to acquire channel state information (CSI). Despite their effectiveness, when the pilot overhead is limited, existing beam training methods suffer from significant achievable rate degradation for remote users with low signal-to-noise ratio (SNR). To tackle this challenge, leveraging the error-correcting capability of channel codes, we introduce channel coding theory into hierarchical beam training to extend the coverage area. Specifically, we establish the duality between hierarchical beam training and channel coding, and the proposed coded beam training scheme serves as a general framework. Then, we present two specific implementations exemplified by coded beam training methods based on Hamming codes and convolutional codes, during which the beam encoding and decoding processes are refined respectively to better accommodate the beam training problem. Simulation results have demonstrated that the proposed coded beam training method can enable reliable beam training performance for remote users with low SNR while keeping training overhead low. |
| title | Coded Beam Training |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2401.01673 |