We propose a deep learning-based phase retrieval scheme to recover the phase of a minimum-phase signal after single-photodiode direct-detection. We show that, by properly generating the training data for the deep learning model, the proposed scheme can jointly perform full-field recovery and compensate for propagation-related linear and nonlinear impairments. Simulation results in relevant transmission system settings show that the proposed scheme relaxes the carrier-To-signal power ratio (CSPR) requirements by 2.8-dB and achieves 1.8-dB better receiver sensitivity while being on average 6 times computationally faster than the conventional 4-fold upsampled Kramers-Kronig receiver aided with digital-back-propagation.

Deep learning-based Phase Retrieval Scheme for Minimum Phase Signal Recovery

Antonelli C.;Mecozzi A.;
2022

Abstract

We propose a deep learning-based phase retrieval scheme to recover the phase of a minimum-phase signal after single-photodiode direct-detection. We show that, by properly generating the training data for the deep learning model, the proposed scheme can jointly perform full-field recovery and compensate for propagation-related linear and nonlinear impairments. Simulation results in relevant transmission system settings show that the proposed scheme relaxes the carrier-To-signal power ratio (CSPR) requirements by 2.8-dB and achieves 1.8-dB better receiver sensitivity while being on average 6 times computationally faster than the conventional 4-fold upsampled Kramers-Kronig receiver aided with digital-back-propagation.
978-1-6654-8881-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/194795
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