Performance-enhanced Amplified O-band WDM Transmission using Machine Learning based Equalization

Yang Hong; Stavros Deligiannidis; Natsupa Taengnoi; Kyle R. H. Bottrill; Naresh K. Thipparapu; Yu Wang; Jayanta K. Sahu; David J. Richardson; Charis Mesaritakis; Adonis Bogris; Periklis Petropoulos

doi:10.1364/CLEO_SI.2021.STh1F.3

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper STh1F.3
•https://doi.org/10.1364/CLEO_SI.2021.STh1F.3

Performance-enhanced Amplified O-band WDM Transmission using Machine Learning based Equalization

Yang Hong, Stavros Deligiannidis, Natsupa Taengnoi, Kyle R. H. Bottrill, Naresh K. Thipparapu, Yu Wang, Jayanta K. Sahu, David J. Richardson, Charis Mesaritakis, Adonis Bogris, and Periklis Petropoulos

CLEO: Science and Innovations 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Network Management and Machine Learning (STh1F)

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Y. Hong, S. Deligiannidis, N. Taengnoi, K. R. H. Bottrill, N. K. Thipparapu, Y. Wang, J. K. Sahu, D. J. Richardson, C. Mesaritakis, A. Bogris, and P. Petropoulos, "Performance-enhanced Amplified O-band WDM Transmission using Machine Learning based Equalization," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper STh1F.3.

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Abstract

We investigate the performance of a machine learning-based equalization in an amplified 4×50-Gb/s O-band WDM system. The results show that the scheme offers significant receiver sensitivity improvements over decision-feedback equalization, especially at more dispersive wavelengths.

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