Toward AI-Enhanced VLC Systems for Industrial Applications

Wesley Da Silva Costa, Higor Camporez, Malte Hinrichs, Helder Rocha, Maria Pontes, Marcelo Segatto, Anagnostis Paraskevopoulos, Volker Jungnickel, Ronald Freund, J.A. Lima Silva

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In this paper, artificial intelligence tools are implemented in order to predict trajectory positions, as well as channel performance of an optical wireless communications link. Case studies for industrial scenarios are considered to this aim. In a first stage, system parameters are optimized using a hybrid multi-objective optimization (HMO) procedure based on the grey wolf optimizer and the non-sorting genetic algorithm III with the goal of simultaneously maximizing power and spectral efficiency. In a second stage, we demonstrate that a long short-term memory neural network (LSTM) is able to predict positions, as well as channel gain. In this way, the VLC links can be configured with the optimal parameters provided by the HMO. The success of the proposed LSTM architectures was validated by training and test root-mean square error evaluations below 1%.
Original languageEnglish
Article number9998061
Pages (from-to)1064-1076
Number of pages13
JournalJournal of Lightwave Technology
Volume41
Issue number4
Early online date23 Dec 2022
DOIs
Publication statusPublished - 15 Feb 2023

Keywords

  • light emitting diodes
  • nonlinear optics
  • optical filters
  • optical sensors
  • optical transmitters
  • optimization
  • visible light communication
  • artificial intelligence

Fingerprint

Dive into the research topics of 'Toward AI-Enhanced VLC Systems for Industrial Applications'. Together they form a unique fingerprint.

Cite this