Model-based machine learning for joint digital backpropagation and PMD compensation
Publication
, Conference
Häger, C; Pfister, HD; Bütler, RM; Liga, G; Alvarado, A
Published in: Optics InfoBase Conference Papers
January 1, 2020
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.
Duke Scholars
Published In
Optics InfoBase Conference Papers
DOI
EISSN
2162-2701
Publication Date
January 1, 2020
Volume
Part F174-OFC 2020
Citation
APA
Chicago
ICMJE
MLA
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Häger, C., Pfister, H. D., Bütler, R. M., Liga, G., & Alvarado, A. (2020). Model-based machine learning for joint digital backpropagation and PMD compensation. In Optics InfoBase Conference Papers (Vol. Part F174-OFC 2020). https://doi.org/10.1364/OFC.2020.W3D.3
Häger, C., H. D. Pfister, R. M. Bütler, G. Liga, and A. Alvarado. “Model-based machine learning for joint digital backpropagation and PMD compensation.” In Optics InfoBase Conference Papers, Vol. Part F174-OFC 2020, 2020. https://doi.org/10.1364/OFC.2020.W3D.3.
Häger C, Pfister HD, Bütler RM, Liga G, Alvarado A. Model-based machine learning for joint digital backpropagation and PMD compensation. In: Optics InfoBase Conference Papers. 2020.
Häger, C., et al. “Model-based machine learning for joint digital backpropagation and PMD compensation.” Optics InfoBase Conference Papers, vol. Part F174-OFC 2020, 2020. Scopus, doi:10.1364/OFC.2020.W3D.3.
Häger C, Pfister HD, Bütler RM, Liga G, Alvarado A. Model-based machine learning for joint digital backpropagation and PMD compensation. Optics InfoBase Conference Papers. 2020.
Published In
Optics InfoBase Conference Papers
DOI
EISSN
2162-2701
Publication Date
January 1, 2020
Volume
Part F174-OFC 2020