Accurate prediction of mmWave time-varying channels is essential for mitigating the issue of channel aging in complex scenarios owing to high user mobility. Existing channel prediction methods have limitations: classical model-based methods often struggle to track highly nonlinear channel dynamics due to limited expert knowledge, while emerging data-driven methods typically require substantial labeled data for effective training and often lack interpretability. To address these issues, this paper proposes a novel hybrid method that integrates a data-driven neural network into a conventional model-based workflow based on a state-space model (SSM), implicitly tracking complex channel dynamics from data without requiring precise expert knowledge. Additionally, a novel unsupervised learning strategy is developed to train the embedded neural network solely with unlabeled data. Theoretical analyses and ablation studies are conducted to interpret the enhanced benefits gained from the hybrid integration. Numerical simulations based on the 3GPP mmWave channel model corroborate the superior prediction accuracy of the proposed method, compared to state-of-the-art methods that are either purely model-based or data-driven. Furthermore, extensive experiments validate its robustness against various challenging factors, including among others severe channel variations and high noise levels.

翻译：准确预测毫米波时变信道对于缓解高用户移动性导致的复杂场景中的信道老化问题至关重要。现有信道预测方法存在局限性：经典的基于模型的方法由于专家知识有限，往往难以追踪高度非线性的信道动态；而新兴的数据驱动方法通常需要大量标注数据进行有效训练，且往往缺乏可解释性。为解决这些问题，本文提出一种新颖的混合方法，将数据驱动的神经网络集成到基于状态空间模型（SSM）的传统模型工作流程中，从而能够从数据中隐式追踪复杂的信道动态，而无需精确的专家知识。此外，本文还开发了一种新颖的无监督学习策略，仅使用未标注数据即可训练嵌入的神经网络。通过理论分析和消融实验，阐释了混合集成所带来的增强效益。基于3GPP毫米波信道模型的数值仿真证实，与最先进的纯模型驱动或纯数据驱动方法相比，所提方法具有更优越的预测精度。此外，大量实验验证了该方法对各种挑战性因素（包括剧烈的信道变化和高噪声水平等）的鲁棒性。