Channel Estimation for Visible Light Communications Using Neural Networks
Neural and Evolutionary Computing
2018-05-22 v1 Information Theory
Signal Processing
math.IT
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.
Cite
@article{arxiv.1805.08060,
title = {Channel Estimation for Visible Light Communications Using Neural Networks},
author = {Anil Yesilkaya and Onur Karatalay and Arif Selcuk Ogrenci and Erdal Panayirci},
journal= {arXiv preprint arXiv:1805.08060},
year = {2018}
}