Physics-informed mode decomposition neural network for structured light in multimode fibers
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
We propose a novel approach for the referenceless mode decomposition of multimode fibers. A deep neural network with the interaction of a physical model achieves decomposition of 55 modes without pre-training. This paradigm shift is of great importance for space division multiplexing.
Details
| Original language | English |
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| Title of host publication | 2023 IEEE Photonics Conference, IPC 2023 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 1-2 |
| ISBN (electronic) | 979-8-3503-4722-7 |
| Publication status | Published - 2023 |
| Peer-reviewed | Yes |
Publication series
| Series | IEEE Photonics Conference (IPC) |
|---|---|
| ISSN | 2374-0140 |
Conference
| Title | 2023 IEEE Photonics Conference |
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| Abbreviated title | IPC 2023 |
| Duration | 12 - 16 November 2023 |
| Website | |
| Location | Hilton Orlando Buena Vista Palace |
| City | Orlando |
| Country | United States of America |
Keywords
ASJC Scopus subject areas
Keywords
- deep learning, multimode fiber, physics-driven, scattering, space division multiplexing