End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping-Reference-Cited by-同舟云学术

End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping

Published:2022 Issue: Volume: Page:
ISSN:
Container-title:Optical Fiber Communication Conference (OFC) 2022
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Author:

Aref Vahid,Chagnon Mathieu

Abstract

We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems. It can maximize either the mutual information (for symbol-metric decoding) or the generalized mutual information (for bit-metric decoding).

Publisher

Optica Publishing Group

Reference6 articles.

1. An Optical Communication's Perspective on Machine Learning and Its Applications

2. Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial]

3. Fiber Nonlinearity Equalization with Multi-Label Deep Learning Scalable to High-Order DP-QAM

4. Nonlinear Interference Mitigation via Deep Neural Networks

5. End-to-End Deep Learning of Optical Fiber Communications

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