This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes.

中文翻译：

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OTFS系统的稀疏贝叶斯学习离网信道估计

本文提出了一种采用稀疏贝叶斯学习（SBL）框架的正交时频空间（OTFS）系统的离网信道估计方案。为了避免分数延迟和多普勒频移引起的信道扩展，并充分利用延迟多普勒（DD）域中的信道稀疏性，我们估计原始DD域信道响应，而不是通常采用的有效DD域信道响应。文学。OTFS 信道估计首先被表述为一个基于 DD 空间中定义的虚拟采样网格的一维 (1D) 离网稀疏信号恢复 (SSR) 问题，其中延迟的并网和离网分量和分离多普勒频移用于估计。尤其是，延迟和多普勒频移的并网分量由恢复的稀疏向量中具有重要值的条目索引共同确定。然后，相应的离网组件在所提出的 SBL 框架中被建模为超参数，可以通过期望最大化方法进行估计。为了在信道估计性能和计算复杂度之间取得平衡，我们通过解耦延迟和多普勒频移估计进一步提出了一个二维 (2D) 离网 SSR 问题。在我们开发的基于 1D 和 2D 离网 SBL 的信道估计算法中，超参数交替更新以计算信道的条件后验分布，可用于重建有效的 DD 域信道。与一维方法相比，所提出的 2D 方法的计算复杂度要低得多，而性能只会略有下降。仿真结果验证了所提出的信道估计方案优于现有技术方案的优越性能。