Optical frequency comb noise characterization using machine learning
Paper i proceeding, 2019

A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.

Frequency Combs

Machine-Learning

dissipative solitons

Phase Estimation

microresonators

comb and Wattles

Författare

Giovanni Brajato

Danmarks Tekniske Universitet (DTU)

Lars Lundberg

Chalmers, Mikroteknologi och nanovetenskap (MC2), Fotonik

Victor Torres Company

Chalmers, Mikroteknologi och nanovetenskap (MC2), Fotonik

D. Zibar

Danmarks Tekniske Universitet (DTU)

IET Conference Publications

Vol. 2019 CP765

45th European Conference on Optical Communication, ECOC 2019
Dublin, Ireland,

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

Mer information

Senast uppdaterat

2020-07-29