Optical frequency comb noise characterization using machine learning
Paper in 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

Author

Giovanni Brajato

Technical University of Denmark (DTU)

Lars Lundberg

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Other publications Research

Victor Torres Company

Chalmers, Microtechnology and Nanoscience (MC2), Photonics

Other publications Research

D. Zibar

Technical University of Denmark (DTU)

IET Conference Publications

Vol. 2019 CP765

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

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing

More information

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1/3/2024 8

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