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

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Apr 05, 2022
Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref
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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.

* 2 pages; accepted for oral presentation at CLEO 2022 in May 2022  
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