Orthogonal time frequency space (OTFS) modulation has demonstrated significant advantages in high-mobility scenarios in future 6G networks. However, existing channel estimation methods often overlook the structured sparsity and clustering characteristics inherent in realistic clustered delay line (CDL) channels, leading to degraded performance in practical systems. To address this issue, we propose a novel nonparametric Bayesian learning (NPBL) framework for OTFS channel estimation. Specifically, a stick-breaking process is introduced to automatically infer the number of multipath components and assign each path to its corresponding cluster. The channel coefficients within each cluster are modeled by a Gaussian mixture distribution to capture complex fading statistics. Furthermore, an effective pruning criterion is designed to eliminate spurious multipath components, thereby enhancing estimation accuracy and reducing computational complexity. Simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean squared error compared to existing methods.

翻译：正交时频空间（OTFS）调制在未来6G网络的高移动性场景中展现出显著优势。然而，现有信道估计方法常忽略实际簇状延迟线（CDL）信道固有的结构化稀疏性与聚类特性，导致实际系统性能下降。为解决该问题，本文提出一种新颖的非参数贝叶斯学习（NPBL）框架用于OTFS信道估计。具体而言，引入棍棒断裂过程以自动推断多径分量数量并将每条路径分配至对应簇。每个簇内的信道系数通过高斯混合分布建模，以捕捉复杂的衰落统计特性。此外，设计了有效的剪枝准则以消除虚假多径分量，从而提升估计精度并降低计算复杂度。仿真结果表明，所提方法在归一化均方误差方面较现有方法具有更优性能。