A Scheme of Channel Prediction Based on Artificial Neural Network

TL;DR

This paper explores the use of various artificial neural networks to predict channel data in high mobility railway scenarios, aiming to improve modeling accuracy with limited measurement data.

Contribution

It compares different neural network types and identifies key factors affecting prediction accuracy, enhancing channel modeling in data-scarce environments.

Findings

Larger neural networks reduce prediction error significantly.

Proportion of training data has a minor impact on accuracy.

ANN-based prediction improves channel modeling when data is limited.

Abstract

Accurate channel modeling is the foundation of communication system design. However, the traditional measurement-based modeling approach has increasing challenges for the scenarios with insufficient measurement data. To obtain enough data for channel modeling, the Artificial Neural Network (ANN) is used in this paper to predict channel data. The high mobility railway channel is considered, which is a typical scenario where it is challenging to obtain enough data for modeling within a short sampling interval. Three types of ANNs, the Back Propagation Network, Radial Basis Function Neural Network and Extreme Learning Machine, are considered to predict channel path loss and shadow fading. The Root-Mean-Square error is used to evaluate prediction accuracy. The factors that may influence prediction accuracy are compared and discussed, including the type of network, number of neurons and proportion of training data. It is found that a larger number of neurons can significantly reduce prediction error, whereas the influence of proportion of training data is relatively small. The results can be used to improve modeling accuracy of path loss and shadow fading when measurement data is reduced.