Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Not on the map. Why?
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 2.
4 papers.
- 2023Diffusion Models for Accurate Channel Distribution Generationcited by 5
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Modelcited by 1
- 2022Data-Driven Estimation of Capacity Upper Boundscited by 0
- 2023Rateless Autoencoder Codes: Trading off Decoding Delay and Reliabilitycited by 0
- 2023Diffusion Models for Accurate Channel Distribution Generation19 Sep 2023
- 2023Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability28 Jan 2023
- 2022Data-Driven Estimation of Capacity Upper Bounds13 May 2022
- 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model26 Jul 2021