# Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning

arXiv: https://arxiv.org/abs/1804.07675 · HTML: https://mlsanity.com/paper/1804.07675/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model](https://mlsanity.com/paper/2107.12320/) (2021)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [Data-Driven Estimation of Capacity Upper Bounds](https://mlsanity.com/paper/2205.06471/) (2022)
- [Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability](https://mlsanity.com/paper/2301.12231/) (2023)

## Abstract (arXiv)

Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.