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