Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
This work addresses a domain-specific problem in optical communications by enabling distributed PMD compensation without prior knowledge of PMD realizations, which is an incremental improvement over prior methods that typically required such knowledge.
The paper tackles the problem of compensating polarization-mode dispersion (PMD) and nonlinear effects in dual-polarization optical fiber systems by proposing a model-based machine learning approach called LDBP-PMD, which achieves a peak effective signal-to-noise ratio only 0.30 dB below the PMD-free case and converges within 1% of peak performance after 428 training iterations on average.
In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The resulting method combines hardware-friendly time-domain nonlinearity mitigation via the recently proposed learned digital backpropagation (LDBP) with distributed compensation of polarization-mode dispersion (PMD). We refer to the resulting approach as LDBP-PMD. We train LDBP-PMD on multiple PMD realizations and show that it converges within 1% of its peak dB performance after 428 training iterations on average, yielding a peak effective signal-to-noise ratio of only 0.30 dB below the PMD-free case. Similar to state-of-the-art lumped PMD compensation algorithms in practical systems, our approach does not assume any knowledge about the particular PMD realization along the link, nor any knowledge about the total accumulated PMD. This is a significant improvement compared to prior work on distributed PMD compensation, where knowledge about the accumulated PMD is typically assumed. We also compare different parameterization choices in terms of performance, complexity, and convergence behavior. Lastly, we demonstrate that the learned models can be successfully retrained after an abrupt change of the PMD realization along the fiber.