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Intra-channel nonlinearity mitigation in optical fiber transmission systems using perturbation-based neural network
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Ding, Jiazheng, Liu, Tiegen, Xu, Tongyang, Hu, Wenxiu, Popov, Sergei, Leeson, Mark S., Zhao, Jian and Xu, Tianhua (2022) Intra-channel nonlinearity mitigation in optical fiber transmission systems using perturbation-based neural network. IEEE Journal of Lightwave Technology, 40 (21). pp. 7106-7116. doi:10.1109/JLT.2022.3200827 ISSN 0733-8724.
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WRAP-Intra-channel-nonlinearity-mitigation-in-optical-fiber-transmission-systems-using-perturbation-based-neural-network-Xu-2023.pdf - Accepted Version - Requires a PDF viewer. Download (14Mb) | Preview |
Official URL: https://doi.org/10.1109/JLT.2022.3200827
Abstract
In this work, a perturbation-based neural network (P-NN) scheme with an embedded bidirectional long short-term memory (biLSTM) layer is investigated to compensate for the Kerr fiber nonlinearity in optical fiber communication systems. Numerical simulations have been carried out in a 32-Gbaud dual-polarization 16-ary quadrature amplitude modulation (DP-16QAM) transmission system. It is shown that this P-NN equalizer can achieve signal-to-noise ratio improvements of ~1.37 dB and ~0.80 dB, compared to the use of a linear equalizer and a single step per span (StPS) digital back propagation (DBP) scheme, respectively. The P-NN equalizer requires lower computational complexity and can effectively compensate for intra-channel nonlinearity. Meanwhile, the performance of P-NN is more robust to the distortion caused by equalization enhanced phase noise (EEPN). Furthermore, it is also found that there exists a tradeoff between the choice of modulation format and the nonlinear equalization schemes for a given transmission distance.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Optical fiber communication, Neural networks (Computer science), Equalizers (Electronics) | ||||||||||||||||||
| Journal or Publication Title: | IEEE Journal of Lightwave Technology | ||||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||||
| ISSN: | 0733-8724 | ||||||||||||||||||
| Official Date: | 22 August 2022 | ||||||||||||||||||
| Dates: |
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| Volume: | 40 | ||||||||||||||||||
| Number: | 21 | ||||||||||||||||||
| Page Range: | pp. 7106-7116 | ||||||||||||||||||
| DOI: | 10.1109/JLT.2022.3200827 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
| Date of first compliant deposit: | 22 August 2022 | ||||||||||||||||||
| Date of first compliant Open Access: | 25 August 2022 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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