Application of Machine Learning Techniques for Amplitude and Phase Noise Characterization

Author

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

Author_Institution

Dept. of Photonics Eng., Tech. Univ. of Denmark, Lyngby, Denmark

Volume

33

Issue

7

fYear

2015

fDate

April1, 1 2015

Firstpage

1333

Lastpage

1343

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.

Keywords

Bayes methods; amplitude estimation; expectation-maximisation algorithm; filtering theory; learning (artificial intelligence); optical communication; parameter estimation; phase noise; Bayesian filtering; carrier synchronization; expectation maximization parameter estimation; laser amplitude characterization; machine learning technique; measurement noise; phase noise characterization; phase noise estimation; time-domain approach; Bayes methods; Kalman filters; Mathematical model; Phase noise; State-space methods; Vectors; Bayesian filtering; expectation maximization; optical communication; phase noise; synchronization;

fLanguage

English

Journal_Title

Lightwave Technology, Journal of

Publisher

ieee

ISSN

0733-8724

Type

jour

DOI

10.1109/JLT.2015.2394808

Filename

7017531