  In this paper, a machine learning method for predicting the evolution of a
mobile communication channel based on a specific type of convolutional neural
network is developed and evaluated in a simulated multipath transmission
scenario. The simulation and channel estimation are designed to replicate
real-world scenarios and common measurements supported by reference signals in
modern cellular networks. The capability of the predictor meets the
requirements that a deployment of the developed method in a radio resource
scheduler of a base station poses. Possible applications of the method are
discussed.
