Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

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

    Research output: Contribution to journalJournal articleResearchpeer-review


    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 languageEnglish
    JournalJournal of Lightwave Technology
    Issue number7
    Pages (from-to)1333-1343
    Publication statusPublished - 2015


    • 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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