Learning of Laser Dynamics using Bayesian Inference

Darko Zibar, Christian Schaeffer, Jesper Mørk

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Techniques from Bayesian machine learning and digital coherent detection are applied to perform frequency noise characterization. Significant advantages of the presented techniques are high-sensitivity and direct access to the uncertainty of the frequency noise measurement.
Original languageEnglish
Title of host publicationProceedings of 2018 Conference on Lasers and Electro-Optics (CLEO)
Number of pages2
PublisherOptical Society of America
Publication date2018
Pages1-2
ISBN (Print)9781943580422
DOIs
Publication statusPublished - 2018
Event2018 Conference on Lasers and Electro-Optics (CLEO) - San Jose Convention Center, San Jose, United States
Duration: 13 May 201818 May 2018

Conference

Conference2018 Conference on Lasers and Electro-Optics (CLEO)
LocationSan Jose Convention Center
CountryUnited States
CitySan Jose
Period13/05/201818/05/2018

Bibliographical note

From the session: Machine Learning for Communication (STh1C)

Keywords

  • Laser noise
  • Laser modes
  • Measurement by laser beam
  • Machine learning
  • Bayes methods
  • Photonics
  • Optical transmitters

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