Recurrent Optical Spectrum Slicing Receiver for Power Fading Mitigation in Highly Dispersive Links using Programmable Photonics

We experimentally validate our works in recurrent optical spectrum slicers for dispersion compensation in IM/DD links, exploiting programmable photonics. We equalize 50 km of 64 Gb/s PAM-4 transmission in C-band, with simple digital feedforward equalization, achieving BER below 1x10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> with two nodes.

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Published
2024-11-10
DOI
https://doi.org/10.1109/ipc60965.2024.10799740
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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article

Recurrent Optical Spectrum Slicing Receiver for Power Fading Mitigation in Highly Dispersive Links using Programmable Photonics

G. Sarantoglou, K. Sozos, Francesco Da Ros, Adonis Bogris et al.
Neural Networks and Reservoir Computing
article

Recurrent Optical Spectrum Slicing Receiver for Power Fading Mitigation in Highly Dispersive Links using Programmable Photonics

G. Sarantoglou, K. Sozos, Francesco Da Ros, Adonis Bogris, Stavros Deligiannidis, Charis Mesaritakis, Metodi P. Yankov
article en

Abstract

We experimentally validate our works in recurrent optical spectrum slicers for dispersion compensation in IM/DD links, exploiting programmable photonics. We equalize 50 km of 64 Gb/s PAM-4 transmission in C-band, with simple digital feedforward equalization, achieving BER below 1x10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> with two nodes.

University of West Attica (GR), Technical University of Denmark (DK), University of the Aegean (GR)
HORIZON EUROPE Framework Programme
Climate action
Openalex Percentile: Top 78%
Neural Networks and Reservoir Computing
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