Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Paper in proceedings, 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 and Antenna Systems, Communication Systems

Other publications Research

Christian Häger

Duke University

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Other publications Research

Nil Garcia

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Other publications Research

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Other publications Research

2018 European Conference on Optical Communication (ECOC)

Vol. 2018-September 8535456

44th European Conference on Optical Communication, ECOC 2018
Rome, Italy,

Coding for terabit-per-second fiber-optical communications (TERA)

European Commission (Horizon 2020), 2017-01-01 -- 2019-12-31.

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Subject Categories

Other Mechanical Engineering

Telecommunications

Communication Systems

DOI

10.1109/ECOC.2018.8535456

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Latest update

7/9/2019 1

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