# End-to-end Learning for GMI Optimized Geometric Constellation Shape

arXiv: https://arxiv.org/abs/1907.08535 · HTML: https://mlsanity.com/paper/1907.08535/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Hybrid Neural Coded Modulation: Design and Training Methods](https://mlsanity.com/paper/2202.01972/) (2022)
- [Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication](https://mlsanity.com/paper/2204.07457/) (2022)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [Friendly Attacks to Improve Channel Coding Reliability](https://mlsanity.com/paper/2401.14184/) (2024)

## Abstract (arXiv)

Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of data rates and in the presence of transceiver impairments. The gains can be harvested with standard binary FEC at no cost w.r.t. conventional BICM.