Phase Retrieval Receiver Based on Deep Learning for Minimum-phase Signal Recovery

We propose a deep learning-based phase retrieval receiver for minimum-phase signal recovery. Simulation results show that the HD-FEC limit at BER 3.8e-3 is achieved with 2-dB lower CSPR and 1.6-dB better receiver sensitivity compared to a conventional four-fold upsampled Kramers-Kronig receiver in relevant system settings.

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