Deep-Learning-Based Channel Estimation for Multi-wavelength Visible Light Communication System

Zhao Ma, Peiyu Jia, Dahai Han, Min Zhang, Zabih Ghassemlooy, Liqiang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a method for channel modeling of multi-wavelength Visible Light Communication (VLC) system, and compares the effects of long short-term memory (LSTM), gated recurrent unit (GRU), sparse autoencoder-s (SAEs) algorithms in channel modeling. The results show that the SAEs algorithm fits the best with a mean square error of only 3 ×10-6. Besides, we construct the multi-wavelength channel of the RGB tricolor LED light source and model it. Finally, after analyzing the influence of many factors on the channel, it is concluded that the modeling effect of the short-wavelength signal source is the best.

Original languageEnglish
Title of host publication2022 4th West Asian Symposium on Optical and Millimeter-wave Wireless Communications (WASOWC)
PublisherIEEE
Number of pages4
ISBN (Electronic)9781665409131
ISBN (Print)9781665409148
DOIs
Publication statusPublished - 12 May 2022
Event2022 4th West Asian Symposium on Optical and Millimeter-wave Wireless Communications - University of Tabriz, Tabriz, Iran, Islamic Republic of
Duration: 12 May 202213 May 2022
https://wasowc2022.tabrizu.ac.ir/en

Conference

Conference2022 4th West Asian Symposium on Optical and Millimeter-wave Wireless Communications
Abbreviated titleWASOWC 2022
Country/TerritoryIran, Islamic Republic of
CityTabriz
Period12/05/2213/05/22
Internet address

Keywords

  • channel modeling
  • deep learning
  • multi-wavelength communication

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