  This paper aims to predict radio channel variations over time by deep
learning from channel observations without knowledge of the underlying channel
dynamics. In next-generation wideband cellular systems, multicarrier
transmission for higher data rate leads to the high-resolution predicting
problem. By leveraging recent advances of deep learning in high-resolution
image processing, we propose a purely data-driven deep learning (DL) approach
to predicting high-resolution temporal evolution of wideband radio channels. In
order to investigate the effect of architectural design choices, we develop and
study three deep learning prediction models, namely, baseline, image
completion, and next-frame prediction models using UNet. Numerical results show
that the proposed DL approach achieves a 52% lower prediction error than the
traditional approach based on the Kalman filter (KF) in mean absolute errors.
To quantify impact of channel aging and prediction on precoding performance, we
also evaluate the performance degradation due to outdated and predicted channel
state information (CSI) compared to perfect CSI. Our simulations show that the
proposed DL approach can reduce the performance loss due to channel aging by
71% through adapting precoding vector to changes in radio channel while the
traditional KF approach only shows a 27% reduction.
