A Machine Learning Method for Prediction of Multipath Channels

Julian Ahrens, Lia Ahrens, Hans Dieter Schotten

In: ZTE Communications: An International Journal Special Issue on Computational Radio Intelligence Seiten 1-10 Editorial Office of ZTE Communications 2020.


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.

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence