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.

Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Event Title: Telecommunications, Optics & Photonics (TOP) Conference 2023
Event Type: Other
Event Location: Etc.Venues
Event Dates: 2023-02-13 - 2023-02-14
Last Modified: 08 Dec 2023 13:00
Date Deposited: 02 Mar 2023 13:34
Full Text Link:
Related URLs:
PURE Output Type: Chapter (peer-reviewed)
Published Date: 2023-02-13
Authors: Nguyen, Long Hoang (ORCID Profile 0000-0003-2171-8380)
Boscolo, Sonia (ORCID Profile 0000-0001-5388-2893)
Ellis, Andrew D. (ORCID Profile 0000-0002-0417-0547)
Sygletos, Stylianos (ORCID Profile 0000-0003-2063-8733)



Version: Accepted Version

Access Restriction: Restricted to Registered users only

Export / Share Citation


Additional statistics for this record