  Channel state information (CSI) rapidly becomes outdated in high mobility
scenarios, degrading the performance of wireless communication systems. In
these cases, time series prediction techniques can be applied to combat the
effects of outdated CSI. Recently, it has been shown that recurrent neural
networks (RNNs) exhibit outstanding performance in time series prediction
tasks. In this paper, we investigate the performance of RNN and long short term
memory (LSTM) predictors in a simple Rayleigh flat-fading channel. We conduct
numerical experiments to evaluate whether these machine-learning (ML)-based
predictors can outperform the optimal linear minimum mean square error Wiener
predictor. Our simulation results indicate that the considered neural network
predictors outperform the Wiener predictor for small observation window lengths
and are more robust under weak channel correlation as well as in the presence
of noise. Furthermore, we show that simple shallow RNNs are sufficient to model
Rayleigh channels over a wide range of Doppler shifts.
