Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning

• URL: http://arxiv.org/abs/2009.09967v1
• Date: Mon, 21 Sep 2020 15:47:34 GMT
• Title: Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning
• Authors: Hwanjin Kim, Sucheol Kim, Hyeongtaek Lee, Chulhee Jang, Yongyun Choi, and Junil Choi
• Abstract summary: This paper focuses on channel prediction techniques for massive multiple-input multiple-output (MIMO) systems. We develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor.
• Score: 18.939010023327498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate.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.

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