model based machine learning for joint digital backpropagation and pmd compensation

• DOI: 10.48550/arxiv.2001.09277
• Publication Date: 2020-01-01
• Authors: Rick M. Butler; Christian Hager; Henry D. Pfister; Gabriele Liga; Alex Alvarado
• Keywords: Signal Processing (eess.SP); FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Information Theory; Information Theory (cs.IT); Communication Systems; 0211 other engineering and technologies; Machine Learning (stat.ML); 02 engineering and technology; (060.2330) Fiber optics communications; Machine Learning (cs.LG); (060.0060) Fiber optics and optical communications; Statistics - Machine Learning; Nonlinearity mitigation; Signal Processing; Telecommunications; 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; Transmissions; Optical fibers; Electrical Engineering and Systems Science - Signal Processing

We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs hardware-friendly DBP and distributed PMD compensation with performance close to the PMD-free case.