Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

Christian Häger, H.D. Pfister, Rick M. Bütler, Gabriele Liga, Alex Alvarado

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
Titel2020 Optical Fiber Communications Conference and Exhibition (OFC)
ISBN van elektronische versie978-1-9435-8071-2
StatusGepubliceerd - 13 mei 2020
Evenement2020 Optical Fiber Communications Conference and Exhibition, OFC 2020 - San Diego, Verenigde Staten van Amerika
Duur: 8 mrt 202012 mrt 2020

Congres

Congres2020 Optical Fiber Communications Conference and Exhibition, OFC 2020
LandVerenigde Staten van Amerika
StadSan Diego
Periode8/03/2012/03/20

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  • Citeer dit

    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 2020 Optical Fiber Communications Conference and Exhibition (OFC) https://ieeexplore.ieee.org/document/9082974