TR2020-155

End-to-End Deep Learning for Phase Noise-Robust Multi-Dimensional Geometric Shaping


We propose an end-to-end deep learning model for phase noise-robust optical communications. A convolutional embedding layer is integrated with a deep autoencoder for multi-dimensional constellation design to achieve shaping gain. The proposed model offers a significant gain up to 2 dB.

 

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