arXiv 2604.040282026-04-05eess.SP

增强6G无线智能：LLMs能否用于CSI预测？

Enhancing 6G Wireless Intelligence: Do LLMs Work for CSI Prediction?

Technische Hochschule Ostwestfalen-Lippe (TH-OWL)（东下萨克森应用技术大学）；Institute for Industrial Information Technology (inIT)（工业信息技术研究所）

Mohsen Kazemian, Jürgen Jasperneite

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AI总结：

本文提出基于LLM的OTFS信道预测框架，结合物理描述符提升动态环境下的信道预测精度，实验表明其在NMSE上优于传统深度学习和无物理描述的LLM方法。

摘要：

在高机动性6G场景中，快速时变信道导致极短相干时间，使传统基于试点的信道状态信息（CSI）估计方法容易出现过时信息或过大的试点开销。因此，信道预测成为此类动态无线系统的关键。为解决这一挑战，大型语言模型（LLMs）作为一种新兴的学习框架，因其强大的序列建模能力和跨不同环境的泛化能力，最近引起了对CSI预测的关注。本文提出了一种基于LLM的信道预测框架，用于高机动性正交时频空间（OTFS）通信系统。在此工作中，我们开发了一种物理感知的LLM预测器，通过从历史信道观测中学习OTFS信道系数的时序演变，同时结合与机动相关的物理描述符（如最大多普勒频率），以在快速时变环境中实现对未来信道状态的准确预测。通过在用户速度从100到500 km/h的广泛模拟中评估所提框架的有效性。数值结果表明，所提方法在NMSE上始终优于传统深度学习预测器和无物理信道描述符的LLM预测器。这些结果展示了将与机动相关的信道知识与LLM序列建模相结合在高动态OTFS系统中进行信道预测的优势。

英文摘要：

In high-mobility 6G scenarios, rapidly time-varying channels lead to very short coherence times, which makes conventional pilot-based channel state information (CSI) estimation approaches prone to outdated information or excessive pilot overhead. Therefore, channel prediction becomes essential in such dynamic wireless systems. To address this challenge, large language models (LLMs) are emerging learning frameworks that have recently attracted attention for CSI prediction due to their strong sequence modeling capability and ability to generalize across different environments. This paper proposes an LLM-based framework for channel prediction in high-mobility orthogonal time frequency space (OTFS) communication systems. In this work, we develop a physics-aware LLM-based predictor that learns the temporal evolution of OTFS channel coefficients from historical channel observations while incorporating mobility-related physical descriptors (e.g., maximum Doppler frequency) to achieve accurate prediction of future channel states in rapidly time-varying environments. The effectiveness of the proposed framework is evaluated through extensive simulations under user velocities ranging from 100 to 500 km/h. Numerical results show that the proposed method consistently achieves lower normalized mean square error (NMSE) compared with both classical deep learning predictors and LLM-based predictors without physical channel descriptors. These results demonstrate the advantage of integrating mobility-related channel knowledge with LLM-based sequence modeling for channel prediction in highly dynamic OTFS systems.

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