End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth

1 Jun 2021 · Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros, Darko Zibar ·

We propose an autoencoder-based geometric shaping that learns a constellation robust to SNR and laser linewidth estimation errors. This constellation maintains shaping gain in mutual information (up to 0.3 bits/symbol) with respect to QAM over various SNR and laser linewidth values.

PDF Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

No methods listed for this paper. Add relevant methods here

Edit Social Preview

End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods

Add Remove