A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments

Clara Catanese; Reda Ayassi; Erwan Pincemin; Yves Jaouën

doi:10.1364/SPPCOM.2020.SpTh3I.1

OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)
OSA Technical Digest (Optica Publishing Group, 2020),
paper SpTh3I.1
•https://doi.org/10.1364/SPPCOM.2020.SpTh3I.1

A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments

Clara Catanese, Reda Ayassi, Erwan Pincemin, and Yves Jaouën

Signal Processing in Photonic Communications 2020

Washington, DC United States
13–16 July 2020
ISBN: 978-1-943580-79-8

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Signal Processing III (SpTh3I)

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C. Catanese, R. Ayassi, E. Pincemin, and Y. Jaouën, "A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments," in OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF), L. Caspani, A. Tauke-Pedretti, F. Leo, and B. Yang, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper SpTh3I.1.

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Abstract

We show here the efficiency of neural network to assist the linear DSP for mitigating 200G DP-16QAM transmission system impairments. We measure up to 1-dB Q-factor improvement in our Nx100-km G.652 fiber link experiment.

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