Classical linear statistical models, like the first-order auto-regressive (AR) model, are commonly used as channel model in high-mobility scenarios. However, compared to sub-6G, the effect of Doppler frequency shifts is more significant at millimeter wave (mmWave) frequencies, and the effectiveness of the statistical channel model in high-mobility mmWave scenarios should be reconsidered. In this paper, we investigate the channel estimation for mmWave multiple-input multiple-output-(MIMO) orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, with the focus on the comparison between the instantaneous channel model and the statistical channel model. For the instantaneous model, by leveraging the low-rank nature of mmWave channels and the multidimensional characteristics of MIMO-OFDM signals across space, time, and frequency, the received signals are structured as a fourth-order tensor fitting a low-rank CANDECOMP/PARAFAC (CP) model. Then, to solve the CP decomposition problem, an estimation of signal parameters via rotational invariance techniques (ESPRIT)-type decomposition based channel estimation method is proposed by exploring the Vandermonde structure of factor matrix, and the channel parameters are then estimated from the factor matrices. We analyze the uniqueness condition of the CP decomposition and develop a concise derivation of the Cramer-Rao bound (CRB) for channel parameters. Simulations show that our method outperforms the existing benchmarks. Furthermore, the results based on the wireless environment generated by Wireless InSite verify that the channel estimation based on the instantaneous channel model performs better than that based on the statistical channel model. Therefore, the instantaneous channel model is recommended for designing channel estimation algorithm for mmWave systems in high-mobility scenarios.

翻译：经典线性统计模型，如一阶自回归（AR）模型，通常被用作高移动性场景下的信道模型。然而，与6GHz以下频段相比，多普勒频移在毫米波（mmWave）频率下的影响更为显著，统计信道模型在高移动性毫米波场景中的有效性值得重新审视。本文研究了高移动性场景下毫米波多输入多输出（MIMO）正交频分复用（OFDM）系统的信道估计，重点比较了瞬时信道模型与统计信道模型。对于瞬时模型，通过利用毫米波信道的低秩特性以及MIMO-OFDM信号在空间、时间和频率维度上的多维特性，将接收信号构建为符合低秩CANDECOMP/PARAFAC（CP）模型的四阶张量。随后，为求解CP分解问题，通过利用因子矩阵的范德蒙结构，提出了一种基于旋转不变技术信号参数估计（ESPRIT）型分解的信道估计方法，并从因子矩阵中估计出信道参数。我们分析了CP分解的唯一性条件，并推导了信道参数克拉美-罗界（CRB）的简洁表达式。仿真结果表明，所提方法优于现有基准方案。此外，基于Wireless InSite生成的无线环境所得结果验证了基于瞬时信道模型的信道估计性能优于基于统计信道模型的方案。因此，在高移动性场景下设计毫米波系统的信道估计算法时，推荐采用瞬时信道模型。