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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.09462 |
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| _version_ | 1866913321934913536 |
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| author | Zhang, Minyang Chen, Dong-Xu Ruan, Pengxiang Liu, Jun Zhao, Jun-Long Yang, Chui-Ping |
| author_facet | Zhang, Minyang Chen, Dong-Xu Ruan, Pengxiang Liu, Jun Zhao, Jun-Long Yang, Chui-Ping |
| contents | The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre-Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application of OAM in free-space optical communication is restricted due to the experimentally limited OAM numbers and the complex OAM recognition methods. Here, we present a novel method that uses the advanced deep learning technique for LG modes recognition. By discretizing the spatial modes of structured light, we turn the OAM state regression into classification. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and high accuracy. By assigning each category a classical information, we further apply our approach to an image transmission task, demonstrating the ability to encode large data with low OAM number. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09462 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-learning-assisted optical communication with discretized state space of structured light Zhang, Minyang Chen, Dong-Xu Ruan, Pengxiang Liu, Jun Zhao, Jun-Long Yang, Chui-Ping Optics Quantum Physics The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre-Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application of OAM in free-space optical communication is restricted due to the experimentally limited OAM numbers and the complex OAM recognition methods. Here, we present a novel method that uses the advanced deep learning technique for LG modes recognition. By discretizing the spatial modes of structured light, we turn the OAM state regression into classification. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and high accuracy. By assigning each category a classical information, we further apply our approach to an image transmission task, demonstrating the ability to encode large data with low OAM number. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light. |
| title | Deep-learning-assisted optical communication with discretized state space of structured light |
| topic | Optics Quantum Physics |
| url | https://arxiv.org/abs/2403.09462 |