# End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

arXiv: https://arxiv.org/abs/2107.12320 · HTML: https://mlsanity.com/paper/2107.12320/

## 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.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Trainable Communication Systems: Concepts and Prototype](https://mlsanity.com/paper/1911.13055/) (2019)
- [Joint Learning of Geometric and Probabilistic Constellation Shaping](https://mlsanity.com/paper/1906.07748/) (2019)
- [Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model](https://mlsanity.com/paper/1912.05146/) (2019)
- [Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning](https://mlsanity.com/paper/1804.07675/) (2018)
- [End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information](https://mlsanity.com/paper/1912.05638/) (2019)
- [Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication](https://mlsanity.com/paper/2006.15027/) (2020)
- [End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theory](https://mlsanity.com/paper/2009.05324/) (2020)
- [End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment](https://mlsanity.com/paper/2103.15856/) (2021)
- [Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities](https://mlsanity.com/paper/1805.03785/) (2018)
- [Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing](https://mlsanity.com/paper/1910.01028/) (2019)

## Built on by (most cited first, 2 of 2)

- [End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping](https://mlsanity.com/paper/2112.05050/) (2021)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)

## 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.