Deep Learning Based Digital Back Propagation with Polarization State Rotation & Phase Noise Invariance
Metadata only
Datum
2020Typ
- Conference Paper
Abstract
A new deep learning training method for digital back propagation (DBP) is introduced. It is invariant to polarization state rotation and phase noise. Applying the method one gains more than 1 dB over standard DBP.
Publikationsstatus
publishedExterne Links
Buchtitel
Optical Fiber Communication Conference (OFC) 2020Zeitschrift / Serie
OSA Technical DigestSeiten / Artikelnummer
Verlag
OSA PublishingKonferenz
Organisationseinheit
02635 - Institut für Elektromagnetische Felder / Electromagnetic Fields Laboratory03974 - Leuthold, Juerg / Leuthold, Juerg
Förderung
670478 - Plasmonic-Silicon-Organic Hybrid – a Universal Platform for THz Communications (EC)