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Optical performance monitoring in transparent fiber-optic networks using neural networks and asynchronous amplitude histograms
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Xu, Jinsheng, Zhao, Jian, Li, Sheng and Xu, Tianhua (2022) Optical performance monitoring in transparent fiber-optic networks using neural networks and asynchronous amplitude histograms. Optics Communications, 517 . 128305. doi:10.1016/j.optcom.2022.128305 ISSN 0030-4018.
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WRAP-optical-performance-monitoring-transparent-fiber-optic-networks-using-neural-networks-asynchronous-amplitude-histograms-Xu-2022.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1904Kb) | Preview |
Official URL: https://doi.org/10.1016/j.optcom.2022.128305
Abstract
Recently, technologies such as artificial neural networks (ANNs) and asynchronous amplitude histograms (AAHs) are widely employed in the optical performance monitoring (OPM). In this paper, the number of layers and the optimization of the neural networks are investigated to complete a series of monitoring tasks in optical communication systems with a large range of chromatic dispersion (CD) and optical signal-to-noise ratio (OSNR). It is shown that the monitoring accuracy has been significantly improved with such implementations. Numerical simulations, which have been conducted for six different modulation formats, demonstrate a good monitoring performance, with an average OSNR monitoring error of 0.1064 dB (99.49% monitoring accuracy) for the OSNR range of 10–40 dB and an average CD monitoring error of 3.3324 ps/nm (99.45% monitoring accuracy) for the CD range of 170–1870 ps/nm, respectively. Furthermore, the CD monitoring and the modulation format identification (MFI) have also been investigated for the system with a large range of dispersion. An average CD error of 16.6832 ps/nm (99.45% monitoring accuracy) and a 100% MFI accuracy have been achieved for all six modulation formats. This scheme is also verified experimentally with OSNR errors of 0.41 dB and CD errors of 15.21 ps/nm, respectively. The identification accuracy of the three modulation formats in the experimental system is also 100%.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
SWORD Depositor: | Library Publications Router | ||||||||
Library of Congress Subject Headings (LCSH): | Optical fiber communication, Network performance (Telecommunication), Signal processing., Neural computers , Fiber optics , Neural networks (Computer science) | ||||||||
Journal or Publication Title: | Optics Communications | ||||||||
Publisher: | Elsevier BV * North Holland | ||||||||
ISSN: | 0030-4018 | ||||||||
Official Date: | 15 August 2022 | ||||||||
Dates: |
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Volume: | 517 | ||||||||
Article Number: | 128305 | ||||||||
DOI: | 10.1016/j.optcom.2022.128305 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 25 April 2022 | ||||||||
Date of first compliant Open Access: | 18 April 2023 |
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