Autoencoder-based end-to-end learning optimizes geometric constellation shapes and bit mappings, achieving up to 0.2 bits per QAM symbol GMI gain across data rates under transceiver impairments.
We show that in the operating regions of interest and with the application of modulation- format independent digital signal processing (DSP) chain, the penalty is the same
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End-to-end Learning for GMI Optimized Geometric Constellation Shape
Autoencoder-based end-to-end learning optimizes geometric constellation shapes and bit mappings, achieving up to 0.2 bits per QAM symbol GMI gain across data rates under transceiver impairments.