Channel Estimation for Visible Light Communications Using Neural Networks

TL;DR

This paper proposes a neural network-based method to predict VLC channel parameters from experimental data, addressing nonlinear challenges for reliable communication system design.

Contribution

It introduces a novel neural network approach trained on experimental measurements to estimate VLC channel taps under varying conditions.

Findings

Neural networks accurately predict channel taps in VLC.

Effective under different environmental conditions.

Potential to improve VLC system reliability.

Abstract

Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work, a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to train neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions.