  To meet the ever-increasing demand for higher data rates, 5G and 6G
technologies are shifting transceivers to higher carrier frequencies, to
support wider bandwidths and more antenna elements. Nevertheless, this solution
poses several key challenges: i) increasing the carrier frequency and bandwidth
leads to greater channel frequency selectivity in time and frequency domains,
and ii) the greater the number of antennas the greater the the pilot overhead
for channel estimation and the more prohibitively complex it becomes to
determine the optimal precoding matrix. This paper presents two deep-learning
frameworks to solve these issues. Firstly, we propose a 3D convolutional neural
network (CNN) that is based on image super-resolution and captures the
correlations between the transmitting and receiving antennas and the frequency
domains to combat frequency selectivity. Secondly, we devise a deep
learning-based framework to combat the time selectivity of the channel that
treats channel aging as a distortion that can be mitigated through deep
learning-based image restoration techniques. Simulation results show that
combining both frameworks leads to a significant improvement in performance
compared to existing techniques with little increase in complexity.
