Inverse System Design Using Machine Learning: The Raman Amplifier Case

A wide range of highly-relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the go...

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Bibliographic Details
Published inJournal of lightwave technology Vol. 38; no. 4; pp. 736 - 753
Main Authors Zibar, Darko, Rosa Brusin, Ann Margareth, de Moura, Uiara C., Da Ros, Francesco, Curri, Vittorio, Carena, Andrea
Format Journal Article
LanguageEnglish
Published New York IEEE 15.02.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Amplifier design
Amplifiers
C band
Communications systems
Continuity (mathematics)
Gain
Inverse problems
Inverse system design
Machine learning
Neural networks
optical amplification
Optical communication
Optical pumping
Optimization
Photonics
Pumps
System analysis and design
Systems design
Transfer functions
Wavelengths
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Summary:A wide range of highly-relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the goal is to determine the corresponding set of input parameters (usually a set of input voltages, currents, powers, and wavelengths). We present a novel method for inverse system design using machine learning and apply it to Raman amplifier design. Inverse system design for Raman amplifiers consists of selecting pump powers and wavelengths that would result in a targeted gain profile. This is a challenging task due to highly-complex interaction between pumps and Raman gain. Using the proposed framework, highly-accurate predictions of the pumping setup for arbitrary Raman gain profiles are demonstrated numerically in C and C+L-band, as well as experimentally in C band, for the first time. A low mean (0.46 and 0.35 dB) and standard deviation (0.20 and 0.17 dB) of the maximum error are obtained for numerical (C+L-band) and experimental (C-band) results, respectively, when employing 4 pumps and 100 km span length. The presented framework is general and can be applied to other inverse problems in optical communication and photonics in general.
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2019.2952179