End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth
arXiv:2106.00431 · doi:10.1109/ECOC52684.2021.9606031
Abstract
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.