Conference paper
Joint Learning of Laser Relative Intensity and Frequency Noise from Single Experiment and Single Detected Quadrature
Machine Learning in Photonic Systems, Department of Photonics Engineering, Technical University of Denmark1
Department of Photonics Engineering, Technical University of Denmark2
Centre of Excellence for Silicon Photonics for Optical Communications, Centers, Technical University of Denmark3
Ultra-fast Optical Communication, Department of Photonics Engineering, Technical University of Denmark4
Bayesian inference framework, that considers laser-physics, is proposed and demonstrated for joint learning of laser static and dynamic parameters. Proof-of-concept experimental results demonstrating the main concepts are presented as well.
Language: | English |
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Publisher: | IEEE |
Year: | 2018 |
Pages: | 1-3 |
Proceedings: | 44th European Conference on Optical Communication |
ISBN: | 1538648628 , 1538648636 , 9781538648629 , 9781538648636 , 153864861X and 9781538648612 |
Types: | Conference paper |
DOI: | 10.1109/ECOC.2018.8535269 |
ORCIDs: | Zibar, Darko |
Bayes methods Bayesian inference framework Laser modes Laser noise Laser theory Mathematical model Measurement by laser beam Optical noise Photonics frequency noise inference mechanisms joint learning laser dynamic parameters laser relative intensity laser static parameters laser-physics learning (artificial intelligence) single detected quadrature