Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

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 conventiona...

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Bibliographic Details
Published in:Journal of lightwave technology 2015-04, Vol.33 (7), p.1333-1343
Main Authors: Zibar, Darko, Gonzalez, Neil G., de Oliveira, Julio Cesar R. F., Monroy, Idelfonso Tafur, de Carvalho, Luis Henrique Hecker, Piels, Molly, Doberstein, Andy, Diniz, Julio, Nebendahl, Bernd, Franciscangelis, Carolina, Estaran, Jose, Haisch, Hansjoerg
Format: Article
Language:English
Subjects:
Amplitudes
Bayes methods
Bayesian analysis
Bayesian filtering
Communities
expectation maximization
Filtering
Filtration
Kalman filters
Machine learning
Mathematical model
Maximization
Noise
optical communication
Phase noise
State-space methods
synchronization
Vectors
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Summary: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.
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2015.2394808