Journal
JOURNAL OF LIGHTWAVE TECHNOLOGY
Volume 38, Issue 4, Pages 736-753Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2019.2952179
Keywords
Gain; Optimization; Neural networks; System analysis and design; Machine learning; Pumps; Optical pumping; Inverse system design; machine learning; optical amplification; optical communication; optimization
Funding
- European Research Council through the ERC-CoG FRECOM project [771878]
- European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant [754462]
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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.
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