Phase Retrieval Receiver Based on Deep Learning for Minimum-phase Signal Recovery

Daniele Orsuti; Cristian Antonelli; Alessandro Chiuso; Marco Santagiustina; Antonio Mecozzi; Andrea Galtarossa; Luca Palmieri

European Conference on Optical Communication (ECOC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper We2C.4

Phase Retrieval Receiver Based on Deep Learning for Minimum-phase Signal Recovery

Daniele Orsuti, Cristian Antonelli, Alessandro Chiuso, Marco Santagiustina, Antonio Mecozzi, Andrea Galtarossa, and Luca Palmieri

European Conference and Exhibition on Optical Communication 2022

Basel Switzerland
18–22 September 2022
ISBN: 978-1-957171-15-9

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Deep Learning for Optical Fiber Communications (We2C)

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D. Orsuti, C. Antonelli, A. Chiuso, M. Santagiustina, A. Mecozzi, A. Galtarossa, and L. Palmieri, "Phase Retrieval Receiver Based on Deep Learning for Minimum-phase Signal Recovery," in European Conference on Optical Communication (ECOC) 2022, J. Leuthold, C. Harder, B. Offrein, and H. Limberger, eds., Technical Digest Series (Optica Publishing Group, 2022), paper We2C.4.

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Abstract

We propose a deep learning-based phase retrieval receiver for minimum-phase signal recov-ery. Simulation results show that the HD-FEC limit at BER 3.8e-3 is achieved with 2-dB lower CSPR and 1.6-dB better receiver sensitivity compared to a conventional four-fold upsampled Kramers-Kronig receiver in relevant system settings.

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