Coherent all Optical Reservoir Computing for Equalization of Impairments in Coherent Fiber Optic Communication Systems

Reservoir computing (RC) is a bio-inspired framework suited for temporal data processing. Here we propose an all optical coherent RC for the equalization of the impairments of fiber optic (FO) system based on coherent detection. We compare the performances of the RC systems based on semiconductor saturable absorber mirror (SESAM) and highly nonlinear waveguide (HNLW). For a dispersion managed long haul FO system in which the nonlinear penalty is dominant, we find that the SESAM outperforms the HNLW due to its larger nonlinear memory. In contrast, for the equalization of dispersive impairments in a short haul FO system such as data center networks, we find that the HNLW has a much superior performance due to its larger linear memory.

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Publication Details

Journal
IEEE photonics journal
Published
2024-09-17
DOI
https://doi.org/10.1109/jphot.2024.3462438
Citations
4
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.39
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article

Coherent all Optical Reservoir Computing for Equalization of Impairments in Coherent Fiber Optic Communication Systems

Toshihiko Hirooka, Masataka Nakazawa, Shiva Kumar, Mahmoud M. T. Maghrabi et al.
4 citations
IEEE photonics journal
Neural Networks and Reservoir Computing
1.39
article

Coherent all Optical Reservoir Computing for Equalization of Impairments in Coherent Fiber Optic Communication Systems

Toshihiko Hirooka, Masataka Nakazawa, Shiva Kumar, Mahmoud M. T. Maghrabi, Mohamed H. Bakr
article en
4 citations

Abstract

Reservoir computing (RC) is a bio-inspired framework suited for temporal data processing. Here we propose an all optical coherent RC for the equalization of the impairments of fiber optic (FO) system based on coherent detection. We compare the performances of the RC systems based on semiconductor saturable absorber mirror (SESAM) and highly nonlinear waveguide (HNLW). For a dispersion managed long haul FO system in which the nonlinear penalty is dominant, we find that the SESAM outperforms the HNLW due to its larger nonlinear memory. In contrast, for the equalization of dispersive impairments in a short haul FO system such as data center networks, we find that the HNLW has a much superior performance due to its larger linear memory.

IEEE photonics journalVol. 16(5)
Tohoku University (JP), McMaster University (CA)
Openalex Percentile: Top 16%
Neural Networks and Reservoir Computing
1.39
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