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

04/05/2022

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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.

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

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping

Semantic optical fiber communication system

Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication

Experimental Verification of Rate Flexibility and Probabilistic Shaping by 4D Signaling

Deep learning the high variability and randomness inside multimode fibres

End-to-end Learning for GMI Optimized Geometric Constellation Shape

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

Related Research

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping

Semantic optical fiber communication system

Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication

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