Massive MIMO channel prediction via adaptive machine learning

Abstract

In this paper, we propose the adaptive channel prediction for massive multiple-input multiple-output (MIMO) systems. Accurate channel state information is crucial to fully exploit massive MIMO systems. However, wireless channels vary in time due to the mobility of user equipment, resulting in performance degradation in massive MIMO. Thus, we develop and compare a vector Kalman filter (VKF)-based channel predictor and a machine learning (ML)-based channel predictor. The VKF-based channel predictor developed in this paper exploits the autoregressive parameters 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 Numerical results reveal that both channel predictors have substantial gain over the outdated channel in terms of the channel prediction accuracy and data rate. However, both channel prediction techniques require large time overhead to obtain accurate prediction, which cannot effectively adapt to new environments. To accurately predict wireless channels for new environments with reduced training overhead, we propose a fast adaptive channel prediction technique based on a meta-learning algorithm for massive MIMO systems. We exploit the model-agnostic meta-learning (MAML) algorithm to achieve quick adaptation with a small amount of labeled data. Also, to improve the prediction accuracy, we adopt the denoising process for the training data by using deep image prior (DIP). Numerical results show that the proposed MAML-based channel predictor can improve the prediction accuracy with only a small number of fine-tuning samples.