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Paper detail

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 information for 64 GBd transmission over 170 km SMF link.

preprint2022arXivOpen access
Vladislav NeskorniukAndrea CarnioDomenico MarsellaSergei K. TuritsynJaroslaw E. PrilepskyVahid Aref
math.ITInformation Theoryeess.SPMachine Learning
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Open access6 authors4 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Vladislav NeskorniukAndrea CarnioDomenico MarsellaSergei K. TuritsynJaroslaw E. PrilepskyVahid Aref

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