Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users’ data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

中文翻译：

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基于大规模 MIMO-OTFS 系统的上行链路辅助高移动性下行链路信道估计

尽管它经常用于正交频分复用 (OFDM) 系统，但在正交时频空间 (OTFS) 调制上应用大规模多输入多输出 (MIMO) 可能会在高移动性场景中遭受巨大的训练开销。在本文中，我们提出了一种用于大规模 MIMO-OTFS 网络的上行链路辅助高移动性下行链路信道估计方案。具体而言，我们首先制定了上行时域大规模 MIMO-OTFS 信号模型，并采用基于期望最大化的变分贝叶斯 (EM-VB) 框架来恢复上行信道参数，包括角度、延迟、多普勒频率和每个物理散射路径的通道增益。相应地，在快速贝叶斯推理的帮助下，构建了一种低复杂度的方法来克服 EM-VB 的瓶颈。然后，我们充分利用上下行链路之间的角度、延迟和多普勒互易性，并在基站重构下行链路海量信道的角度、延迟和多普勒频率。此外，我们检查了延迟多普勒角域上的下行链路大规模 MIMO 信道估计。OTFS 在延迟多普勒域上的信道色散经过仔细分析，如果任何用户的不同路径具有不同的延迟多普勒特征，则将其用于将一个给定的路径与一个特定的延迟多普勒网格相关联。此外，当任何用户的所有路径都可以在角度域上完美分离时，我们设计了有效的路径调度算法，将不同用户的数据映射到正交延迟多普勒角域资源中，实现并行、低复杂度的下行3D信道估计。对于一般情况，我们采用降维的最小二乘估计器来捕获下行延迟多普勒角信道。提供了各种数值例子来证实所提出方案的有效性和鲁棒性。