Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81683
Title: Optimization algorithms of neural networks for traditional time-domain equalizer in optical communications
Authors: Wang, HD
Zhou, J
Wang, YZ
Wei, JL
Liu, WP
Yu, C 
Li, ZH
Keywords: neural networks
Optical communications
Optimization
Equalizer
Tap estimation
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Applied sciences, Sept. 2019, v. 9, no. 18, 3907, p. 1-10 How to cite?
Journal: Applied sciences 
Abstract: Neural networks (NNs) have been successfully applied to channel equalization for optical communications. In optical fiber communications, the linear equalizer and the nonlinear equalizer with traditional structures might be more appropriate than NNs for performing real-time digital signal processing, owing to its much lower computational complexity. However, the optimization algorithms of NNs are useful in many optimization problems. In this paper, we propose and evaluate the tap estimation schemes for the equalizer with traditional structures in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that adaptive moment estimation algorithm and batch gradient descent method perform well in the tap estimation of equalizer. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications.
URI: http://hdl.handle.net/10397/81683
ISSN: 2076-3417
DOI: 10.3390/app9183907
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Wang, H. D., Zhou, J., Wang, Y. Z., Wei, J. L., Liu, W. P., Yu, C. Y., & Li, Z. H. (2019). Optimization algorithms of neural networks for traditional time-domain equalizer in optical communications. Applied Sciences, 9(18), 3907, 1-10 is available at https://dx.doi.org/10.3390/app9183907
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