Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 11 references that are in the map. First posted on arXiv 5 Apr 2022.
Builds on
7 of 11 references.
References · 7
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017An Introduction to Deep Learning for the Physical Layercited by 233
- 2019Joint Learning of Geometric and Probabilistic Constellation Shapingcited by 13
- 2019End-to-end Learning for GMI Optimized Geometric Constellation Shapecited by 4
- 2020Deep-learning Autoencoder for Coherent and Nonlinear Optical Communicationcited by 4
- 2021End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairmentcited by 4
- 2018Increasing Achievable Information Rates via Geometric Shapingcited by 1
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.