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Linear Regression vs. Deep Learning for Signal Quality Monitoring 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, Digital Systems.ORCID iD: 0000-0003-3754-0265
Chalmers University of Technology, Sweden.
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2022 (English)In: IEEE Photonics Journal, E-ISSN 1943-0655, Vol. 14, no 4, article id 8643108Article in journal (Refereed) Published
Abstract [en]

Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. Vol. 14, no 4, article id 8643108
Keywords [en]
Deep learning, error vector magnitude, machine learning, optical fiber communication, optical performance monitoring, Adaptive optics, Feedforward neural networks, Light modulation, Light modulators, Optical fibers, Optical signal processing, Quadrature amplitude modulation, Error vector, Machine-learning, Network-based, Optical signal-processing, Optical-fiber communication, Symbol, Vector magnitude, Monitoring
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:ri:diva-60180DOI: 10.1109/JPHOT.2022.3193727Scopus ID: 2-s2.0-85135762511OAI: oai:DiVA.org:ri-60180DiVA, id: diva2:1699819
Note

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

Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2024-03-04Bibliographically approved

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Fan, YuchuanPang, XiaodanUdalcovs, AleksejsOzolins, Oskars

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