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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.18277 |
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| _version_ | 1866912134814760960 |
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| author | Hu, Shun |
| author_facet | Hu, Shun |
| contents | In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functionalities such as adaptive modulation. We review existing methodologies for CSI estimation, emphasizing the shift from traditional to data-driven approaches. Then a novel framework for spatial CSI prediction using realistic environment information is proposed, and experimental results demonstrate the effectiveness. This research paves way for innovative strategies in managing wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18277 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Models Enabled Ubiquitous Wireless Sensing Hu, Shun Machine Learning In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functionalities such as adaptive modulation. We review existing methodologies for CSI estimation, emphasizing the shift from traditional to data-driven approaches. Then a novel framework for spatial CSI prediction using realistic environment information is proposed, and experimental results demonstrate the effectiveness. This research paves way for innovative strategies in managing wireless networks. |
| title | Large Models Enabled Ubiquitous Wireless Sensing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.18277 |