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arXiv · 2204.07457

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Vladislav Neskorniuk·Andrea Carnio·Domenico Marsella·Sergei K. Turitsyn·Jaroslaw E. Prilepsky·Vahid Aref
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Abstract

Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.

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Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref. 2022-04-05. Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication. https://arxiv.org/abs/2204.07457

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