This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. Previous channel predictors are based on theoretical channel models, which would be deviated from realistic channels. In this paper, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor using the realistic channels from the spatial channel model (SCM), which has been adopted in the 3GPP standard for years. First, we propose a low-complexity mobility estimator based on the spatial average using a large number of antennas in massive MIMO. The mobility estimate can be used to determine the complexity order of developed predictors. The VKF-based channel predictor developed in this paper exploits the autoregressive (AR) parameters estimated from the SCM channels based on the Yule-Walker equations. Then, the ML-based channel predictor using the linear minimum mean square error (LMMSE)-based noise pre-processed data is developed. Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. The ML-based predictor has larger overall computational complexity than the VKF-based predictor, but once trained, the operational complexity of ML-based predictor becomes smaller than that of VKF-based predictor.

中文翻译：

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大规模 MIMO 信道预测：卡尔曼滤波与机器学习

本文重点介绍大规模多输入多输出 (MIMO) 系统的信道预测技术。先前的通道预测器基于理论通道模型，这会偏离实际通道。在本文中，我们开发并比较了基于矢量卡尔曼滤波器 (VKF) 的信道预测器和基于机器学习 (ML) 的信道预测器，它们使用来自空间信道模型 (SCM) 的真实信道，该模型已在 3GPP 中采用多年的标准。首先，我们提出了一种基于空间平均的低复杂度移动性估计器，在大规模 MIMO 中使用大量天线。移动性估计可用于确定开发的预测器的复杂度顺序。本文开发的基于 VKF 的通道预测器利用了基于 Yule-Walker 方程从 SCM 通道估计的自回归 (AR) 参数。然后，开发了使用基于线性最小均方误差 (LMMSE) 的噪声预处理数据的基于 ML 的信道预测器。数值结果表明，在信道预测精度和数据速率方面，两个信道预测器都比过时的信道有显着的增益。基于 ML 的预测器比基于 VKF 的预测器具有更大的整体计算复杂度，但是一旦训练，基于 ML 的预测器的操作复杂度变得比基于 VKF 的预测器小。数值结果表明，在信道预测精度和数据速率方面，两个信道预测器都比过时的信道有显着的增益。基于 ML 的预测器比基于 VKF 的预测器具有更大的整体计算复杂度，但是一旦训练，基于 ML 的预测器的操作复杂度变得比基于 VKF 的预测器小。数值结果表明，在信道预测精度和数据速率方面，两个信道预测器都比过时的信道有显着的增益。基于 ML 的预测器比基于 VKF 的预测器具有更大的整体计算复杂度，但是一旦训练，基于 ML 的预测器的操作复杂度变得比基于 VKF 的预测器小。