用大模型预测无线信道状态，提升5G网络性能。
Large Models Enabled Ubiquitous Wireless Sensing
- 用语言模型结合环境信息预测MIMO-OFDM系统的空间信道状态。
- 实验验证该方法能有效提升信道状态预测精度。
- 适合研究智能无线网络与大模型应用的学者参考。
在5G通信时代，信道状态信息（CSI）对于提升网络性能至关重要。本文探讨了利用语言模型在MIMO-OFDM系统中进行空间CSI预测的可行性。首先阐明了准确CSI对自适应调制等高级功能的重要性，并回顾了传统与数据驱动的CSI估计方法演进。随后提出一种基于真实环境信息的空间CSI预测新框架，实验结果证明其有效性。本研究为无线网络管理提供了创新策略。
原文摘要 · Abstract (English)
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.
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