Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

Darko Zibar, Luis Henrique Hecker de Carvalho, Molly Piels, Andy Doberstein, Julio Diniz, Bernd Nebendahl, Carolina Franciscangelis, Jose Manuel Estaran Tolosa, Hansjoerg Haisch, Neil G. Gonzalez, Julio Cesar R. F. de Oliveira, Idelfonso Tafur Monroy

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

49 Citaten (Scopus)

Samenvatting

In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally.
Originele taal-2Nederlands
Pagina's (van-tot)1333-1343
Aantal pagina's11
TijdschriftJournal of Lightwave Technology
Volume33
Nummer van het tijdschrift7
DOI's
StatusGepubliceerd - 2015
Extern gepubliceerdJa

Trefwoorden

  • Communication, Networking and Broadcast Technologies
  • Photonics and Electrooptics
  • Bayes methods
  • Bayesian filtering
  • Expectation maximization
  • Kalman filters
  • Mathematical model
  • Optical communication
  • Phase noise
  • State-space methods
  • Synchronization
  • Vectors

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