A Machine Learning Method for Prediction of Multipath Channels

TL;DR

This paper introduces a convolutional neural network-based machine learning approach to predict mobile communication channel evolution, aiming to improve radio resource scheduling in cellular networks.

Contribution

It develops and evaluates a novel CNN-based method for predicting multipath channel behavior in simulated real-world scenarios, suitable for deployment in base station schedulers.

Findings

Predictor meets deployment requirements for radio resource scheduling

Simulated scenario validates prediction accuracy

Method applicable to modern cellular network measurements

Abstract

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.