Abstract
We investigate the generalization capabilities of neural network-based Raman amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber-specific models.
Original language | English |
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Title of host publication | Proceedings of 2021 Optical Fiber Communications Conference and Exhibition |
Publisher | IEEE |
Publication date | Jun 2021 |
Article number | 9489950 |
ISBN (Electronic) | 9781943580866 |
Publication status | Published - Jun 2021 |
Event | 2021 Optical Fiber Communications Conference and Exhibition - San Francisco, United States Duration: 6 Jun 2021 → 11 Jun 2021 |
Conference
Conference | 2021 Optical Fiber Communications Conference and Exhibition |
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Country/Territory | United States |
City | San Francisco |
Period | 06/06/2021 → 11/06/2021 |
Bibliographical note
Funding Information:We thank OFS Fitel Denmark for providing the fibers used in this work. This project has received funding from the European Research Council through the ERC-CoG FRECOM project (grant agreement no. 771878), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462 and the Villum Foundations (VYI OPTIC-AI grant no. 29344).
Funding Information:
We thank OFS Fitel Denmark for providing the fibers used in this work.
Publisher Copyright:
© 2021 OSA.