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

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

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 informat...

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Hauptverfasser: Neskorniuk, Vladislav, Carnio, Andrea, Marsella, Domenico, Turitsyn, Sergei K, Prilepsky, Jaroslaw E, Aref, Vahid
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Information Theory
Computer Science - Learning
Mathematics - Information Theory
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Zusammenfassung: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.
DOI:10.48550/arxiv.2204.07457