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.
@article{arxiv.1909.04824,
title = {A Machine Learning Method for Prediction of Multipath Channels},
author = {Julian Ahrens and Lia Ahrens and Hans D. Schotten},
journal= {arXiv preprint arXiv:1909.04824},
year = {2020}
}