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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 |
Issue Date: | 2019 | Source: | Applied sciences, Sept. 2019, v. 9, no. 18, 3907, p. 1-10 | 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. | Keywords: | neural networks Optical communications Optimization Equalizer Tap estimation |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Applied sciences | 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 |
| Appears in Collections: | Journal/Magazine Article |
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| Wang_Algorithms_Neural_Networks.pdf | 1.01 MB | Adobe PDF | View/Open |
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