Abstract
This article presents a novel deep linear and nonlinear compensation network (DLNCN) that effectively addresses linear and nonlinear distortion in conjunction with polarization-dependent loss (PDL), while also accounting for changes caused by the joint impact of PDL and time-varying rotation of the state of polarization (RSOP). To accomplish this, we introduce neural network layers dedicated for PDL compensation, and we devise a transfer learning approach that selectively updates weights in layers affected by the variations while keeping the remaining weights unchanged. To monitor RSOP with PDL, we employ a pilot-based acquisition and a pilot-aided decision-directed tracking technique. Our numerical tests demonstrate successful RSOP tracking in the presence of PDL impairments, outperforming state-of-the-art schemes by an average of over 0.75 dB in Q-factor for a dual-polarized 960 km 32 Gbaud 64-QAM transmission with a polarization linewidth of 3 kHz. These results highlight the effectiveness of our proposed deep neural network structure, which includes a dedicated layer for PDL compensation, and its ability to work seamlessly with RSOP tracking.
PDF Article
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription