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arXiv 2601.03084eess.SP
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一种用于高机动6G OTFS网络信道预测的条件变分框架

A Conditional Variational Framework for Channel Prediction in High-Mobility 6G OTFS Networks

Technische Hochschule Ostwestfalen-Lippe（东威斯特法伦-利珀应用技术大学）

Mohsen Kazemian, Jürgen Jasperneite

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AI总结：

本文提出了一种基于条件变分自动编码器的信道预测方法，用于高机动6G OTFS网络，有效提升信道预测的准确性和鲁棒性。

摘要：

本文提出了一种基于机器学习（ML）的信道预测方法，用于高机动正交时频空（OTFS）信道。在这些场景中，多普勒扩展和时变多径传播引起的快速变化导致信道去相关，使传统基于导频的信道估计方法容易出现过时的信道状态信息（CSI）和过大的开销。因此，可靠的信道预测方法成为支持OTFS系统中稳健检测和解码的关键。本文提出了一种条件变分自动编码器用于信道预测（CVAE4CP）方法，该方法学习了给定物理系统和机动参数的OTFS时延多普勒信道系数的条件分布。通过将这些参数作为条件信息，所提出的方法能够在其实际实现之前预测未来的信道系数，同时通过低维潜在表示来考虑固有的信道不确定性。所提出的框架通过在高机动条件下进行广泛的仿真评估。数值结果表明，CVAE4CP在归一化均方误差（NMSE）方面始终优于竞争性的基于学习的基线方法，特别是在高多普勒频率和扩展预测时间范围内。这些结果证实了所提出方法在快速时变OTFS系统中进行信道预测的有效性和鲁棒性。

英文摘要：

This paper proposes a machine learning (ML) based method for channel prediction in high mobility orthogonal time frequency space (OTFS) channels. In these scenarios, rapid variations caused by Doppler spread and time varying multipath propagation lead to fast channel decorrelation, making conventional pilot based channel estimation methods prone to outdated channel state information (CSI) and excessive overhead. Therefore, reliable channel prediction methods become essential to support robust detection and decoding in OTFS systems. In this paper, we propose conditional variational autoencoder for channel prediction (CVAE4CP) method, which learns the conditional distribution of OTFS delay Doppler channel coefficients given physical system and mobility parameters. By incorporating these parameters as conditioning information, the proposed method enables the prediction of future channel coefficients before their actual realization, while accounting for inherent channel uncertainty through a low dimensional latent representation. The proposed framework is evaluated through extensive simulations under high mobility conditions. Numerical results demonstrate that CVAE4CP consistently outperforms a competing learning based baseline in terms of normalized mean squared error (NMSE), particularly at high Doppler frequencies and extended prediction horizons. These results confirm the effectiveness and robustness of the proposed approach for channel prediction in rapidly time varying OTFS systems.

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