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Non-orthogonal optical multiplexing empowered by deep learning

Author

Listed:
  • Tuqiang Pan

    (Ministry of Education
    Guangdong University of Technology)

  • Jianwei Ye

    (Ministry of Education
    Guangdong University of Technology)

  • Haotian Liu

    (Ministry of Education
    Guangdong University of Technology)

  • Fan Zhang

    (Ministry of Education
    Guangdong University of Technology)

  • Pengbai Xu

    (Ministry of Education
    Guangdong University of Technology)

  • Ou Xu

    (Ministry of Education
    Guangdong University of Technology)

  • Yi Xu

    (Ministry of Education
    Guangdong University of Technology)

  • Yuwen Qin

    (Ministry of Education
    Guangdong University of Technology)

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.

Suggested Citation

  • Tuqiang Pan & Jianwei Ye & Haotian Liu & Fan Zhang & Pengbai Xu & Ou Xu & Yi Xu & Yuwen Qin, 2024. "Non-orthogonal optical multiplexing empowered by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45845-4
    DOI: 10.1038/s41467-024-45845-4
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    References listed on IDEAS

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    1. Kaiheng Zou & Kai Pang & Hao Song & Jintao Fan & Zhe Zhao & Haoqian Song & Runzhou Zhang & Huibin Zhou & Amir Minoofar & Cong Liu & Xinzhou Su & Nanzhe Hu & Andrew McClung & Mahsa Torfeh & Amir Arbabi, 2022. "High-capacity free-space optical communications using wavelength- and mode-division-multiplexing in the mid-infrared region," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Zhoutian Liu & Lele Wang & Yuan Meng & Tiantian He & Sifeng He & Yousi Yang & Liuyue Wang & Jiading Tian & Dan Li & Ping Yan & Mali Gong & Qiang Liu & Qirong Xiao, 2022. "All-fiber high-speed image detection enabled by deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. KyeoReh Lee & YongKeun Park, 2016. "Exploiting the speckle-correlation scattering matrix for a compact reference-free holographic image sensor," Nature Communications, Nature, vol. 7(1), pages 1-7, December.
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