Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Feedforward Neural Network-Based EVM Estimation: Impairment Tolerance in Coherent Optical Systems
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. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0003-4906-1704
RISE Research Institutes of Sweden.ORCID iD: 0000-0003-3754-0265
Chalmers University of Technology, Sweden.
Show others and affiliations
2022 (English)In: IEEE Journal of Selected Topics in Quantum Electronics, ISSN 1077-260X, E-ISSN 1558-4542, Vol. 28, no 4, article id 6000410Article in journal (Refereed) Published
Abstract [en]

Error vector magnitude (EVM) is commonly used for evaluating the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques for EVM estimation extend the functionality of conventional optical performance monitoring (OPM). In this article, we evaluate the tolerance of our developed EVM estimation scheme against various impairments in coherent optical systems. In particular, we analyze the signal quality monitoring capabilities in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise. We use feedforward neural networks (FFNNs) to extract the EVM information from amplitude histograms of 100 symbols per IQ cluster signal sequence captured before carrier phase recovery. We perform simulations of the considered impairments, along with an experimental investigation of the impact of laser phase noise. To investigate the tolerance of the EVM estimation scheme to each impairment type, we compare the accuracy for three training methods: 1) training without impairment, 2) training one model for all impairments, and 3) training an independent model for each impairment. Results indicate a good generalization of the proposed EVM estimation scheme, thus providing a valuable reference for developing next-generation intelligent OPM systems. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. Vol. 28, no 4, article id 6000410
Keywords [en]
feedforward neural networks, monitoring, Optical communication, optical fiber communication, signal processing, Deep learning, Optical fibers, Phase noise, Quadrature amplitude modulation, Signal receivers, Error vector, Estimation schemes, Fiber-optics, Laser noise, Magnitude estimation, Optical noise, Optical-fiber communication, Phase-noise, Signal-processing, Vector magnitude
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-60551DOI: 10.1109/JSTQE.2022.3177004Scopus ID: 2-s2.0-85130826782OAI: oai:DiVA.org:ri-60551DiVA, id: diva2:1704675
Note

Funding details: China Scholarship Council, CSC, 201807930003; Funding details: 1.1.1.2/VIAA/4/20/660; Funding details: National Key Research and Development Program of China, NKRDPC, 2018YFB2201700, 2020-03506; Funding details: Vetenskapsrådet, VR; Funding details: Vetenskapsrådet, VR, 2019-05197, 2016-04510, P109599; Funding text 1: This work was supported in part by the China Scholarship Council under Grant 201807930003, in part by the Swedish Research Council (VR) projects under Grants 2019-05197 and 2016-04510, in part by the RISE SK funded project Optical Neural Networks under Grant P109599, in part by the ERDF-funded CARAT Project under Grant 1.1.1.2/VIAA/4/20/660, in part by the National Key Research and Development Program of China under Grant 2018YFB2201700, and in part by theVINNOVAfundedCELTIC-NEXT Project AI-NET PROTECT under Grant 2020-03506.

Available from: 2022-10-19 Created: 2022-10-19 Last updated: 2024-03-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Fan, YuchuanPang, XiaodanUdalcovs, AleksejsOzolins, Oskars

Search in DiVA

By author/editor
Fan, YuchuanPang, XiaodanUdalcovs, AleksejsOzolins, Oskars
By organisation
Industrial SystemsRISE Research Institutes of Sweden
In the same journal
IEEE Journal of Selected Topics in Quantum Electronics
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 14 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf