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

## TL;DR

This paper introduces an autoencoder-based geometric shaping method that optimizes bit mappings, achieving significant GMI gains across various data rates and impairments without additional coding costs.

## Contribution

It presents a novel end-to-end learning approach for geometric constellation shaping that includes bit mapping optimization, enhancing GMI performance.

## Key findings

- Up to 0.2 bits/QAM symbol GMI gain achieved
- Gains applicable across multiple data rates and impairments
- Compatible with standard binary FEC without extra costs

## Abstract

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.