Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes

Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes

We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regressi...

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Bibliographic Details
Main Authors: Josh W. Nevin, Eric Sillekens, Ronit Sohanpal, Lidia Galdino, Sam Nallaperuma, Polina Bayvel, Seb J. Savory
Format: Article
Language:English
Published: Cambridge University Press 2023-01-01
Series:Data-Centric Engineering
Subjects:
digital backpropagation
Gaussian processes
machine learning
optical networks
Online Access:https://www.cambridge.org/core/product/identifier/S2632673623000151/type/journal_article

Internet

https://www.cambridge.org/core/product/identifier/S2632673623000151/type/journal_article

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