In high-mobility scenarios, orthogonal time frequency space (OTFS) exhibits a significant advantage over orthogonal frequency division multiplexing (OFDM) due to its sparsity in the delay-Doppler (DD) domain. However, challenges arise in the estimation of fractional paths due to the issue of low grid resolution, leading to notable modeling error in the sparse representation of the channel state information (CSI). Typically, the accuracy of CSI acquisition increases as grid points approach the actual paths, thus the distribution of grid points should be related to the distribution of actual paths in the DD domain. In this letter, we propose a 2D off-grid OTFS channel estimation method based on the equally distributed information quantity. The distribution of grid points is evolved according to the distribution of estimated paths in the delay and Doppler dimension in sequence. For each dimension, two processes, i.e., learning and fission, are carried to evolve the grid and estimate the channel parameters, alternatively. A coarse uniform grid will be evolved into a non-uniform denser grid which represents the CSI in DD domain more accurately. Simulation results indicate our proposed grid evolution method outperforms the off-grid method with uniform grid interval at the same level of complexity. Furthermore, it surpasses the 1D off-grid method and achieves estimation accuracy close to the 2D off-grid SBL combination method at a lower complexity.

中文翻译：

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基于等分布信息量网格演化的离网OTFS信道估计


在高迁移率场景中，由于在延迟多普勒 （DD） 域中的稀疏性，正交时频空间 （OTFS） 比正交频分复用 （OFDM） 具有显著优势。然而，由于网格分辨率低的问题，分数路径的估计出现了挑战，导致通道状态信息 （CSI） 的稀疏表示中存在明显的建模错误。通常，CSI 获取的准确性会随着网格点接近实际路径而增加，因此网格点的分布应与 DD 域中实际路径的分布有关。在本文中，我们提出了一种基于等分布信息量的二维离网 OTFS 信道估计方法。网格点的分布是根据估计路径在延迟和多普勒维度上的分布依次演变的。对于每个维度，进行两个过程，即学习和裂变，以交替进化网格和估计通道参数。粗略的均匀网格将演变成非均匀的密度网格，更准确地表示 DD 域中的 CSI。仿真结果表明，在相同复杂度水平上，我们提出的网格进化方法优于具有均匀网格间隔的离网方法。此外，它超越了一维离网方法，并以较低的复杂度实现了接近二维离网 SBL 组合方法的估计精度。