Machine learning enabled compensation of phase-to-amplitude distortion due to imperfect pump-dithering in optical phase conjugated transmission systems

We propose a machine learning-based digital signal processing technique to mitigate the impact of imperfect counter-phasing pump dithering in optical phase conjugated transmission systems. Contrary to state-of-the-art approaches that can deal only with the residual phase distortion, our scheme also tackles the corresponding phase to amplitude transformations that have occurred in the dispersive channel. With the use of an adaptive configuration, we first track and compensate the dither induced phase deviations on the received signal and subsequently extrapolate and remove their amplitude impact. Through extensive numerical we explore the operational margins of our approach in terms of system transmission distance, constellation order and pump-phase mismatch level, and demonstrate significant performance improvement against current schemes.