5 research outputs found
Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning
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that both channel predictors have substantial gain over the outdated channel in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.Comment: Accepted to IEEE Transactions on Communication