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

Research output: Contribution to journalArticleAcademicpeer-review

49 Citations (Scopus)

Abstract

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.
Original languageDutch
Pages (from-to)1333-1343
Number of pages11
JournalJournal of Lightwave Technology
Volume33
Issue number7
DOIs
Publication statusPublished - 2015
Externally publishedYes

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