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One-shot learning for modulation format identification in evolving optical networks
Chalmers University of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, Acreo.ORCID iD: 0000-0003-3754-0265
Chalmers University of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, Acreo.ORCID iD: 0000-0001-9839-7488
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2017 (English)In: Optics InfoBase Conference Papers, OSA - The Optical Society , 2017Conference paper, Published paper (Refereed)
Abstract [en]

We report on the first successful application of one-shot machine learning scheme that identifies new modulation formats based on a single constellation diagram without re-training. 100% accuracy is achieved when expanding from 2 to 5 supported modulation formats. © 2019 The Author(s).

Place, publisher, year, edition, pages
OSA - The Optical Society , 2017.
Keywords [en]
Nanophotonics, Optical signal processing, Photonics, Constellation diagrams, Modulation formats, New modulation formats, One-shot learning, Modulation
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-42503Scopus ID: 2-s2.0-85077217282ISBN: 9781557528209 (print)OAI: oai:DiVA.org:ri-42503DiVA, id: diva2:1384756
Conference
Integrated Photonics Research, Silicon and Nanophotonics, IPRSN 2019, 29 July 2019 through 1 August 2017
Note

Conference code: 141568; Export Date: 9 January 2020; Conference Paper; Funding details: VINNOVA, 2017-01559; Funding details: Vetenskapsrådet, VR, 2016-04510; Funding text 1: This work was supported by VINNOVA within the project Centre for Software-Defined Optical Networks (2017-01559), by VR project PHASE (2016-04510), and by COST Action 15127 RECODIS. References

Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-22Bibliographically approved

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Udalcovs, AleksejsOzolins, Oskars

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