English

Rate Adaptive Geometric Constellation Shaping Using Autoencoders and Many-To-One Mapping

Signal Processing 2023-07-20 v1
Authors: Metodi P. Yankov , Ognjen Jovanovic , Darko Zibar , Francesco Da Ros
View on arXiv ↗ PDF ↗

Abstract

A many-to-one mapping geometric constellation shaping scheme is proposed with a fixed modulation format, fixed FEC engine and rate adaptation with an arbitrarily small step. An autoencoder is used to optimize the labelings and constellation points' positions.

Cite

@article{arxiv.2307.09897,
  title  = {Rate Adaptive Geometric Constellation Shaping Using Autoencoders and Many-To-One Mapping},
  author = {Metodi P. Yankov and Ognjen Jovanovic and Darko Zibar and Francesco Da Ros},
  journal= {arXiv preprint arXiv:2307.09897},
  year   = {2023}
}

Related papers

View all related →
Signal Processing · Electrical Eng. & Systems
Rate Adaptive Autoencoder-based Geometric Constellation Shaping
Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros, Darko Zibar
2023-01-04
Information Theory · Computer Science
Joint Learning of Geometric and Probabilistic Constellation Shaping
Maximilian Stark, Fayçal Ait Aoudia, Jakob Hoydis
2019-08-30
Information Theory · Computer Science
End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping
Vahid Aref, Mathieu Chagnon
2021-12-10
Signal Processing · Electrical Eng. & Systems
End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth
Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros, Darko Zibar
2022-04-26
Information Theory · Computer Science
Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning
Rasmus T. Jones, Tobias A. Eriksson, Metodi P. Yankov, Benjamin J. Puttnam +3
2018-10-02
Information Theory · Computer Science
End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Shrinivas Chimmalgi, Laurent Schmalen, Vahid Aref
2025-06-23
Machine Learning · Computer Science
Auto-Encoding Molecular Conformations
Robin Winter, Frank Noé, Djork-Arné Clevert
2021-01-06
Information Theory · Computer Science
Grassmannian Constellation Design for Noncoherent MIMO Systems Using Autoencoders
Xiaotian Fu, Didier Le Ruyet
2021-09-07
Signal Processing · Electrical Eng. & Systems
Geometric Constellation Shaping for Phase-noise Channels Using a Differentiable Blind Phase Search
Andrej Rode, Benedikt Geiger, Laurent Schmalen
2022-06-27
Optimization and Control · Mathematics
Multi-dimensional Joint Probabilistic and Geometric Shaping Strategies for Nonlinear Fiber Communications
Metodi P. Yankov
2019-11-06
Optimization and Control · Mathematics
Geometrical and Numerical Design of Structured Unitary Space Time Constellations
Guangyue Han, Joachim Rosenthal
2007-05-23
Information Theory · Computer Science
Deep Autoencoder-Based Constellation Design in Multiple Access Channels
Stepan Gorelenkov, Mojtaba Vaezi
2025-05-05
Information Theory · Computer Science
Geometrically-Shaped Constellation for Visible Light Communications at Short Blocklength
Jia-Ning Guo, Ru-Han Chen, Jian Zhang, Longguang Li +2
2024-04-30
Information Theory · Computer Science
Rotating Non-Uniform and High-Dimensional Constellations Using Geodesic Flow on Lie Groups
David A. Karpuk, Camilla Hollanti
2013-09-23
Signal Processing · Electrical Eng. & Systems
Joint Precoding and Probabilistic Constellation Shaping using Arithmetic Distribution Matching
Babaee Ramin, Oveis Gharan Shahab, Bouchard Martin
2025-05-27
Information Theory · Computer Science
Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
Rasmus T. Jones, Tobias A. Eriksson, Metodi P. Yankov, Darko Zibar
2018-05-11
Machine Learning · Computer Science
MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle
2015-06-08
Information Theory · Computer Science
A Simplified Method for Optimising Geometrically Shaped Constellations of Higher Dimensionality
Kadir Gümüş, Bin Chen, Thomas Bradley, Chigo Okonkwo
2023-07-12
Signal Processing · Electrical Eng. & Systems
Low-Complexity Geometric Shaping
Ali Mirani, Erik Agrell, Magnus Karlsson
2024-01-25
Information Theory · Computer Science
Constellation Design for Deep Joint Source-Channel Coding
Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan
2022-07-13
Machine Learning · Computer Science
Towards Composable Distributions of Latent Space Augmentations
Omead Pooladzandi, Jeffrey Jiang, Sunay Bhat, Gregory Pottie
2023-03-08
Machine Learning · Computer Science
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt
2019-03-08
Information Theory · Computer Science
Geometric constellation shaping for wireless optical intensity channels: An information-theoretic approach
Suhua Zhou, Tianqi Li, Zhaoxi Fang, Jing Zhou +1
2024-12-03
Applied Physics · Physics
Generative inverse design of multimodal resonant structures for locally resonant metamaterials
Sander Dedoncker, Christian Donner, Raphael Bischof, Linus Taenzer +1
2024-11-05
Information Theory · Computer Science
Increasing Achievable Information Rates via Geometric Shaping
Bin Chen, Chigo Okonkwo, Hartmut Hafermann, Alex Alvarado
2020-06-05
R2 v1 2026-06-28T11:34:31.252Z