End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model
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Abstract (arXiv)
We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model. We jointly optimized constellation shaping and nonlinear pre-emphasis achieving mutual information gain of 0.18 bits/sym./pol. simulating 64 GBd dual-polarization single-channel transmission over 30x80 km G.652 SMF link with EDFAs.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 26 Jul 2021.
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13 of 21 references.
References · 13
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- 2019Trainable Communication Systems: Concepts and Prototypecited by 22
- 2019Joint Learning of Geometric and Probabilistic Constellation Shapingcited by 13
- 2019Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Modelcited by 6
- 2018Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learningcited by 5
- 2019End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Informationcited by 4
- 2020Deep-learning Autoencoder for Coherent and Nonlinear Optical Communicationcited by 4
- 2020End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theorycited by 4
- 2021End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairmentcited by 4
- 2018Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearitiescited by 3
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.