融合数据与模型的毫米波信道预测，无需标签也能解释结果。
Hybrid Data-Driven SSM for Interpretable and Label-Free mmWave Channel Prediction
- 用状态空间模型结合神经网络，从无标签数据中学习信道变化。
- 在3GPP模型下误差低于现有方法，噪声大时仍稳定有效。
- 适合需要可解释性且缺乏标注数据的移动通信场景。
毫米波信道随时间变化剧烈，高移动性环境下尤其显著。现有方法存在两方面局限：基于模型的方法因依赖专家知识，难以捕捉高度非线性的信道动态；数据驱动方法需大量标注数据，且缺乏可解释性。本文提出一种新型混合方法，将数据驱动的神经网络嵌入传统基于状态空间模型（SSM）的框架中，无需精确专家知识即可隐式追踪复杂信道演化。同时设计一种新型无监督学习策略，仅使用无标签数据训练嵌入神经网络。理论分析与消融实验验证了混合结构带来的性能增益。基于3GPP毫米波信道模型的数值仿真表明，该方法在预测精度上优于当前最先进的纯模型或纯数据驱动方法。大量实验进一步验证其对严重信道波动和高噪声等挑战因素的鲁棒性。
原文摘要 · Abstract (English)
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.
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