Non-orthogonal optical multiplexing empowered by deep learning

Orthogonality among channels is a canonical basis for optical multiplexing featured with division multiplexing, which substantially reduce the complexity of signal post-processing in demultiplexing. However, it inevitably imposes an upper limit of capacity for multiplexing. Herein, we report on non-...

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Bibliographic Details
Published in:Nature communications 2024-02, Vol.15 (1), p.1580-1580, Article 1580
Main Authors: Pan, Tuqiang, Ye, Jianwei, Liu, Haotian, Zhang, Fan, Xu, Pengbai, Xu, Ou, Xu, Yi, Qin, Yuwen
Format: Article
Language:English
Subjects:
639/624
639/624/1107
Artificial neural networks
Channels
Coding
Deep learning
Demultiplexing
Humanities and Social Sciences
Ill posed problems
Luminous intensity
Machine learning
multidisciplinary
Multiplexing
Neural networks
Orthogonality
Polarization
Science
Science (multidisciplinary)
Signal processing
Wavelength
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Summary:Orthogonality among channels is a canonical basis for optical multiplexing featured with division multiplexing, which substantially reduce the complexity of signal post-processing in demultiplexing. However, it inevitably imposes an upper limit of capacity for multiplexing. Herein, we report on non-orthogonal optical multiplexing over a multimode fiber (MMF) leveraged by a deep neural network, termed speckle light field retrieval network (SLRnet), where it can learn the complicated mapping relation between multiple non-orthogonal input light field encoded with information and their corresponding single intensity output. As a proof-of-principle experimental demonstration, it is shown that the SLRnet can effectively solve the ill-posed problem of non-orthogonal optical multiplexing over an MMF, where multiple non-orthogonal input signals mediated by the same polarization, wavelength and spatial position can be explicitly retrieved utilizing a single-shot speckle output with fidelity as high as ~ 98%. Our results resemble an important step for harnessing non-orthogonal channels for high capacity optical multiplexing. Authors showcase that non-orthogonal optical multiplexing can be achieved over a multimode fiber utilizing deep learning, where information encoded in non-orthogonal input channels even with the same polarization, wavelength, and spatial region can be demultiplexed with high accuracy.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-45845-4