  Channel state information (CSI) is of pivotal importance as it enables
wireless systems to adapt transmission parameters more accurately, thus
improving the system's overall performance. However, it becomes challenging to
acquire accurate CSI in a highly dynamic environment, mainly due to multi-path
fading. Inaccurate CSI can deteriorate the performance, particularly of a
massive multiple-input multiple-output (mMIMO) system. This paper adapts
machine learning (ML) for CSI prediction. Specifically, we exploit time-series
models of deep learning (DL) such as recurrent neural network (RNN) and
Bidirectional long-short term memory (BiLSTM). Further, we use NeuralProphet
(NP), a recently introduced time-series model, composed of statistical
components, e.g., auto-regression (AR) and Fourier terms, for CSI prediction.
Inspired by statistical models, we also develop a novel hybrid framework
comprising RNN and NP to achieve better prediction accuracy. The proposed
channel predictors (CPs) performance is evaluated on a real-time dataset
recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results
show that DL brings performance gain when used with statistical models and
showcases robustness.
