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Conference paper

Learning of Laser Dynamics using Bayesian Inference

In Proceedings of 2018 Conference on Lasers and Electro-optics (cleo) — 2018, pp. 1-2

By Zibar, Darko^1,2,3; Schaeffer, Christian⁴; Mørk, Jesper²

From

Centre of Excellence for Silicon Photonics for Optical Communications, Centers, Technical University of Denmark¹

Department of Photonics Engineering, Technical University of Denmark²

Ultra-fast Optical Communication, Department of Photonics Engineering, Technical University of Denmark³

Helmut Schmidt Universität⁴

Abstract

Techniques from Bayesian machine learning and digital coherent detection are applied to perform frequency noise characterization. Significant advantages of the presented techniques are high-sensitivity and direct access to the uncertainty of the frequency noise measurement.

Language:	English
Publisher:	Optical Society of America
Year:	2018
Pages:	1-2
Proceedings:	2018 Conference on Lasers and Electro-Optics (CLEO)
ISBN:	1538657333 , 1943580421 , 9781538657331 and 9781943580422
Types:	Conference paper
DOI:	10.1364/CLEO_SI.2018.STh1C.1
ORCIDs:	Zibar, Darko and Mørk, Jesper

Keywords

Bayes methods Laser modes Laser noise Machine learning Measurement by laser beam Optical transmitters Photonics

Other keywords

Bayesian inference Bayesian machine learning digital coherent detection frequency noise characterization frequency noise measurement inference mechanisms laser dynamics learning learning (artificial intelligence) noise measurement semiconductor device noise

Learning of Laser Dynamics using Bayesian Inference

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