English

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Signal Processing 2022-04-18 v1 Information Theory Machine Learning math.IT
Authors: Vladislav Neskorniuk , Andrea Carnio , Domenico Marsella , Sergei K. Turitsyn , Jaroslaw E. Prilepsky , Vahid Aref
View on arXiv ↗ PDF ↗

Abstract

Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.

Keywords

deep learning for wireless communications deep learning for image reconstruction automatic modulation classification

Cite

@article{arxiv.2204.07457,
  title  = {Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication},
  author = {Vladislav Neskorniuk and Andrea Carnio and Domenico Marsella and Sergei K. Turitsyn and Jaroslaw E. Prilepsky and Vahid Aref},
  journal= {arXiv preprint arXiv:2204.07457},
  year   = {2022}
}

Comments

2 pages; accepted for oral presentation at CLEO 2022 in May 2022

Related papers

View all related →
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
High-Cardinality Hybrid Shaping for 4D Modulation Formats in Optical Communications Optimized via End-to-End Learning
Vinícius Oliari, Boris Karanov, Sebastiaan Goossens, Gabriele Liga +5
2021-12-21
Signal Processing · Electrical Eng. & Systems
End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model
Vladislav Neskorniuk, Andrea Carnio, Vinod Bajaj, Domenico Marsella +3
2021-07-27
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
Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
Rasmus T. Jones, Tobias A. Eriksson, Metodi P. Yankov, Darko Zibar
2018-05-11
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
Machine Learning · Computer Science
Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
2025-07-23
Information Theory · Computer Science
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Rasmus T. Jones, Metodi P. Yankov, Darko Zibar
2019-07-22
Information Theory · Computer Science
Constellation Shaping for WDM systems using 256QAM/1024QAM with Probabilistic Optimization
Metodi P. Yankov, Francesco Da Ros, Edson P. da Silva, Søren Forchhammer +4
2016-09-13
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
Joint Learning of Probabilistic and Geometric Shaping for Coded Modulation Systems
Fayçal Ait Aoudia, Jakob Hoydis
2020-04-15
Signal Processing · Electrical Eng. & Systems
Joint Optimization of Geometric and Probabilistic Constellation Shaping for OFDM-ISAC Systems
Benedikt Geiger, Fan Liu, Shihang Lu, Andrej Rode +1
2025-01-22
Information Theory · Computer Science
End-to-end Learning of Probabilistic and Geometric Constellation Shaping with Iterative Receivers
Harindu Jayarathne, Dileepa Marasinghe, Nandana Rajatheva, Matti Latva-aho
2025-10-28
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
Information Theory · Computer Science
End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Shrinivas Chimmalgi, Laurent Schmalen, Vahid Aref
2025-06-23
Signal Processing · Electrical Eng. & Systems
Joint Transceiver Optimization for Wireless Communication PHY with Convolutional Neural Network
Banghua Zhu, Jintao Wang, Longzhuang He, Jian Song
2018-08-10
Information Theory · Computer Science
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models
Mehdi Letafati, Samad Ali, Matti Latva-aho
2023-11-17
Optimization and Control · Mathematics
Multi-dimensional Joint Probabilistic and Geometric Shaping Strategies for Nonlinear Fiber Communications
Metodi P. Yankov
2019-11-06
Signal Processing · Electrical Eng. & Systems
End-to-end Optimization of Constellation Shaping for Wiener Phase Noise Channels with a Differentiable Blind Phase Search
Andrej Rode, Benedikt Geiger, Shrinivas Chimmalgi, Laurent Schmalen
2023-04-25
Information Theory · Computer Science
Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach
Mehdi Letafati, Samad Ali, Matti Latva-aho
2023-09-19
Machine Learning · Computer Science
Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
2026-02-04
Machine Learning · Computer Science
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders
Ioannis A. Nellas, Sotiris K. Tasoulis, Vassilis P. Plagianakos, Spiros V. Georgakopoulos
2022-11-07
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
Information Theory · Computer Science
Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing
Boris Karanov, Gabriele Liga, Vahid Aref, Domaniç Lavery +2
2019-10-03
R2 v1 2026-06-24T10:49:10.465Z