Non-orthogonal optical multiplexing empowered by deep learning

Tuqiang Pan, Jianwei Ye, Haotian Liu, Fan Zhang, Pengbai Xu, Ou Xu, Yi Xu () and Yuwen Qin ()
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Tuqiang Pan: Ministry of Education
Jianwei Ye: Ministry of Education
Haotian Liu: Ministry of Education
Fan Zhang: Ministry of Education
Pengbai Xu: Ministry of Education
Ou Xu: Ministry of Education
Yi Xu: Ministry of Education
Yuwen Qin: Ministry of Education

Nature Communications, 2024, vol. 15, issue 1, 1-8

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-45845-4

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