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Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0001-5783-8996
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0003-3754-0265
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0003-4906-1704
Chalmers University of Technology, Sweden.
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2021 (English)In: Journal of Optical Communications and Networking, ISSN 1943-0620, E-ISSN 1943-0639, Vol. 13, no 4, p. B12-B20, article id 9326316Article in journal (Refereed) Published
Abstract [en]

We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks for error vector magnitude (EVM) estimation in coherent optical communications. We build a regression model to extract EVM information from complex signal constellation diagrams using a small number of received symbols. For the additive-white-Gaussian-noise-impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase-shift keying, 2.2% for 16-Ary quadrature amplitude modulation (16QAM), and 1.1% for 64QAM signals, requiring only 100 symbols per constellation cluster in each observation period. Therefore, it can be used as a low-complexity alternative to conventional bit-error-rate estimation, enabling solutions for intelligent optical performance monitoring. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. Vol. 13, no 4, p. B12-B20, article id 9326316
Keywords [en]
Bit error rate, Complex networks, Convolutional neural networks, Errors, Optical communication, Quadrature amplitude modulation, Regression analysis, White noise, 16-ary Quadrature Amplitude Modulation (16QAM), Additive White Gaussian noise, Coherent communication, Coherent optical communications, Error vector magnitude, Observation Period, Optical performance monitoring, Signal quality monitoring, Gaussian noise (electronic)
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Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-52198DOI: 10.1364/JOCN.409704Scopus ID: 2-s2.0-85099886784OAI: oai:DiVA.org:ri-52198DiVA, id: diva2:1528097
Available from: 2021-02-12 Created: 2021-02-12 Last updated: 2024-03-04Bibliographically approved

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Fan, YuchuanUdalcovs, AleksejsPang, XiaodanOzolins, Oskars

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