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.
Rate adaptation and reach increase by probabilistically shaped 64-QAM: An experimental demonstration
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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.