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Non-orthogonal optical multiplexing empowered by deep learning

Engineering and Technology

Non-orthogonal optical multiplexing empowered by deep learning

T. Pan, J. Ye, et al.

This groundbreaking research conducted by Tuqiang Pan, Jianwei Ye, Haotian Liu, Fan Zhang, Pengbai Xu, Ou Xu, Yi Xu, and Yuwen Qin explores non-orthogonal optical multiplexing using a deep neural network to achieve an impressive fidelity of around 98%. This innovation paves the way for high-capacity optical multiplexing beyond traditional limits.

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Playback language: English
Abstract
Orthogonality among channels is a canonical basis for optical multiplexing, but it limits capacity. This paper reports on non-orthogonal optical multiplexing over a multimode fiber (MMF) using a deep neural network (SLRnet) to learn the mapping between multiple non-orthogonal input light fields and a single intensity output. Experiments show SLRnet effectively retrieves multiple non-orthogonal input signals with high fidelity (~98%), suggesting a path towards high-capacity optical multiplexing.
Publisher
Nature Communications
Published On
Feb 21, 2024
Authors
Tuqiang Pan, Jianwei Ye, Haotian Liu, Fan Zhang, Pengbai Xu, Ou Xu, Yi Xu, Yuwen Qin
Tags
optical multiplexing
non-orthogonal channels
multimode fiber
deep neural network
SLRnet
signal retrieval
high fidelity

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