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

Add code
Sep 17, 2018
Shen Li, Christian Häger, Nil Garcia, Henk Wymeersch
Figure 1 for Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Figure 2 for Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Figure 3 for Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Figure 4 for Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

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.

* 3 pages, 4 figures, fixed typos, revised layout  
View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon