Performance Evaluation of Various Training Algorithms for ANN Equalization in Visible Light Communications with an Organic LED

Zahra Nazari Chaleshtori, Paul A. Haigh, Petr Chvojka, Stanislav Zvanovec, Zabih Ghassemlooy

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

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

This paper evaluates the effect of training algorithms in an artificial neural network (ANN) equalizer for a feedforward multi-layer perceptron configuration in visible light communication systems using a low bandwidth organic light source. We test the scaled conjugate-gradient, conjugate-gradient backpropagation and Levenberg-Marquardt back propagation (LM) algorithms with 5, 10, 20, 30, and 40 neurons. We show that, LM offers superior bit error rate performance in comparison to other training algorithms based on the mean square error. The training methods can be selected based on the trade-off between complexity and performance.
Original languageEnglish
Title of host publicationThe 2nd West Asian Colloquium on Optical Wireless Communications (WACOWC2019)
Subtitle of host publication27-28 April, Shahid Beheshti University, Tehran, Iran
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages11-15
ISBN (Electronic)9781728137674
ISBN (Print)9781728137681
DOIs
Publication statusE-pub ahead of print - 25 Jul 2019
Event2019 2nd West Asian Colloquium on Optical Wireless Communications (WACOWC) - Tehran, Iran
Duration: 27 Apr 201928 Apr 2019

Conference

Conference2019 2nd West Asian Colloquium on Optical Wireless Communications (WACOWC)
Period27/04/1928/04/19

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