Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella, Sergei K., Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref
TL;DR
This paper introduces a deep learning approach using autoencoders to jointly optimize probabilistic and geometric constellation shaping, significantly improving data transmission efficiency in optical communication systems.
Contribution
It presents a novel autoencoder-based method for joint probabilistic and geometric shaping, outperforming traditional Maxwell-Boltzmann probabilistic distributions in optical fiber transmission.
Findings
Achieved 0.05 bits/4D-symbol higher mutual information.
Outperformed standard 256 QAM Maxwell-Boltzmann shaping.
Demonstrated effectiveness over 170 km SMF link at 64 GBd.
Abstract
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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Taxonomy
TopicsOptical Network Technologies · Advanced Photonic Communication Systems · Photonic and Optical Devices