mlsanityData to 7 Oct 2026

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

arXiv 2112.05050 ↗First posted 9 Dec 2021cs.IT cs.AI eess.SPcited by 1

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Abstract (arXiv)

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).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 9 Dec 2021.

Builds on

11 of 20 references.

References · 11

  1. 2017An Introduction to Deep Learning for the Physical Layercited by 227
  2. 2019Trainable Communication Systems: Concepts and Prototypecited by 22
  3. 2018An Overview on Application of Machine Learning Techniques in Optical Networkscited by 19
  4. 2018End-to-end Deep Learning of Optical Fiber Communicationscited by 16
  5. 2017Nonlinear Interference Mitigation via Deep Neural Networkscited by 13
  6. 2019Joint Learning of Geometric and Probabilistic Constellation Shapingcited by 13
  7. 2020Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systemscited by 5
  8. 2020Deep-learning Autoencoder for Coherent and Nonlinear Optical Communicationcited by 4
  9. 2019End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Informationcited by 3
  10. 2018Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearitiescited by 2
  11. 2021End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Modelcited by 1

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Friendly Attacks to Improve Channel Coding Reliabilitycited by 1