Machine learning techniques in optical communication

Darko Zibar, Molly Piels, Rasmus Thomas Jones, C. G. Schaeffer

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


Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework for parameter tracking.
Original languageEnglish
Title of host publication2015 41st European Conference on Optical Communication (ECOC)
Number of pages3
Publication date2015
ISBN (Print)9788460817413
Publication statusPublished - 2015
EventOpto Electronics and Communications Conference 2015 - Shanghai Everbright Convention Center, Shanghai , China
Duration: 28 Jun 20152 Jul 2015


ConferenceOpto Electronics and Communications Conference 2015
LocationShanghai Everbright Convention Center


  • expectation-maximisation algorithm
  • lasers
  • learning (artificial intelligence)
  • nonlinear optics
  • optical communication
  • optical filters
  • optical noise
  • optical signal detection
  • phase noise
  • Communication, Networking and Broadcast Technologies
  • Photonics and Electrooptics
  • Bayes methods
  • Bayesian filtering
  • expectation maximization
  • laser characterization
  • machine learning techniques
  • Nonlinear optics
  • nonlinear phase noise
  • nonlinear state-space framework
  • Optical noise
  • Optical polarization
  • Optical signal processing
  • Optical variables measurement
  • parameter tracking
  • Phase noise
  • signal detection


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