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

04/05/2022

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by   Vladislav Neskorniuk, et al.

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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.