Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Paper in proceeding, 2018
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.
Author
Shen Li
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Christian Häger
Duke University
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Nil Garcia
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Henk Wymeersch
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
European Conference on Optical Communication, ECOC
Vol. 2018-September 8535456
978-153864862-9 (ISBN)
44th European Conference on Optical Communication, ECOC 2018
Rome, Italy,
Rome, Italy,
Coding for terabit-per-second fiber-optical communications (TERA)
European Commission (EC) (EC/H2020/749798), 2017-01-01 -- 2019-12-31.
Subject Categories
Other Mechanical Engineering
Telecommunications
Communication Systems
DOI
10.1109/ECOC.2018.8535456