Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
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Abstract (arXiv)
A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 May 2018.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Modelcited by 2
- 2021End-to-End Learning of Joint Geometric and Probabilistic Constellation Shapingcited by 2
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learningcited by 2
- 2022Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning8 Nov 2022
- 2021End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping9 Dec 2021
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model26 Jul 2021