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

2023/07/19 by Yankov, Metodi P., Jovanovic, Ognjen, Zibar, Darko +1
#FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2307.09897

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.

Related

• Rate Adaptive Autoencoder-based Geometric Constellation Shaping
• End-to-end Learning for GMI Optimized Geometric Constellation Shape
• End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping
• BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling
• Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities