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Phrase:	`"water pump control"` (does not work with wildcards)
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Conference paper

Maxwell-Boltzmann PMF Design Using Machine Learning for Reconfigurable Optical Fiber Networks

In 2021 Conference on Lasers and Electro-optics (cleo) — 2021, pp. 1-2

By Hansen, Henrik Enggaard^1,2,3; Yankov, Metodi Plamenov^1,2,3,4; Oxenløwe, Leif Katsuo^1,3,4; Forchhammer, Søren^1,2,3

From

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

Coding and Visual Communication, Department of Photonics Engineering, Technical University of Denmark²

Department of Photonics Engineering, Technical University of Denmark³

Ultra-fast Optical Communication, Department of Photonics Engineering, Technical University of Denmark⁴

Abstract

A neural network is used to predict the optimal Maxwell-Boltzmann probabilistic constellation shaping for a nonlinear channel with inline dispersion-compensation. The network uses only system parameters available at the transmitter and thus requires no feedback.

Language:	English
Publisher:	OSA
Year:	2021
Pages:	1-2
Proceedings:	2020 Conference on Lasers and Electro-Optics Pacific Rim
ISBN:	1665447923 , 194358091X , 194358091x , 9781665447928 and 9781943580910
Types:	Conference paper
ORCIDs:	Hansen, Henrik Enggaard , Yankov, Metodi Plamenov , Oxenløwe, Leif Katsuo and Forchhammer, Søren

Keywords

Laser modes Lasers and electrooptics Machine learning Neural networks Optical fiber networks Probabilistic logic Shape

Maxwell-Boltzmann PMF Design Using Machine Learning for Reconfigurable Optical Fiber Networks

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