Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
arXiv:2204.07457
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.
2 pages; accepted for oral presentation at CLEO 2022 in May 2022