In this paper, we consider the time-varying channel estimation in millimeter wave (mmWave) multiple-input multiple-output MIMO systems with hybrid beamforming architectures. Different from the existing contributions that considered single-carrier mmWave systems with high mobility, the wideband orthogonal frequency division multiplexing (OFDM) system is considered in this work. To solve the channel estimation problem under channel double selectivity, we propose a pilot transmission scheme based on 5G OFDM, and the received signals are formed as a fourth-order tensor, which fits the low-rank CANDECOMP/PARAFAC (CP) model. By further exploring the Vandermonde structure of factor matrix, a tensor-subspace decomposition based channel estimation method is proposed to solve the CP decomposition, where the uniqueness condition is analyzed. Based on the decomposed factor matrices, the channel parameters, including angles of arrival/departure, delays, channel gains and Doppler shifts are estimated, and the Cram\'{e}r-Rao bound (CRB) results are derived as performance metrics. Simulation results demonstrate the superior performance of the proposed method over other benchmarks. Furthermore, the channel estimation methods are tested based on the channel parameters generated by Wireless InSites, and simulation results show the effectiveness of the proposed method in practical scenarios.

翻译：本文针对采用混合波束赋形架构的毫米波多输入多输出（MIMO）系统中的时变信道估计问题展开研究。与现有针对高移动性单载波毫米波系统的研究不同，本文考虑宽带正交频分复用（OFDM）系统。为解决信道双选择性条件下的信道估计问题，我们提出了一种基于5G OFDM的导频传输方案，将接收信号构造成四阶张量，该张量满足低秩CANDECOMP/PARAFAC（CP）模型。通过进一步挖掘因子矩阵的Vandermonde结构，提出了一种基于张量子空间分解的信道估计方法来求解CP分解，并分析了其唯一性条件。基于分解得到的因子矩阵，估计了包括到达/离开角、时延、信道增益和多普勒频移在内的信道参数，并推导了Cramér-Rao界（CRB）作为性能指标。仿真结果表明，所提方法相较于其他基准方法具有优越性能。此外，基于Wireless InSites生成的信道参数对信道估计方法进行了测试，仿真结果验证了所提方法在实际场景中的有效性。