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that both channel predictors have substantial gain over the outdated channel in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.

本文研究基于真实无线信道模型的大规模 MIMO 系统通道预测技术，提出并比较了基于向量卡尔曼滤波器和基于机器学习的通道预测方法，数值结果表明这两种方法均比传统方法具有更高的精确度和数据速率。