Hu, P., Zhu, L., Zhu, J., Ren, J., Chen, S., Ma, J., Gao, R., Jing, Z., Li, Z., Hu, S., Tian, B., Wang, X., Wang, F., Xu, Q., Tian, Q., Chang, H., Xin, X., & Liu, B. (2024). Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications. Optics Express, 32(27), 47936-47958. https://doi.org/10.1364/OE.541188
Hu, Peng ; Zhu, Lei ; Zhu, Jianping et al. / Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications. In: Optics Express. 2024 ; Vol. 32, No. 27. pp. 47936-47958.
@article{b86f602bbc6f49caa4a60a0c43a187f5,
title = "Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications",
abstract = "Orbital angular momentum (OAM) multiplexing is emerging as a critical technique for achieving high data capacity in underwater wireless optical communications (UWOC). Nonetheless, wavefront distortions induced by underwater turbulence compromise the orthogonality of OAM modes. In this paper, we introduce a physics-driven untrained learning approach for adaptive optics that operates independently of extensive amplitude datasets. Without iterative processing and pre-trained datasets, the underwater turbulence characteristics can be retrieved accurately by only relying on a one-shot distorted probe beam and a priori known amplitude of the probe beam. By leveraging a single distorted diffraction pattern and a priori known amplitude of the probe beam, the characteristics of underwater turbulence can be accurately retrieved without iterative processing or pre-trained datasets. Furthermore, by implementing a hybrid input/output alternating projection algorithm with a square constraint area, the retrieved underwater turbulence phase screen beyond the [0, 2π] range aligns with the target pattern. This consistency indicates that the proposed wavefront recovery technology is validated across a broad range of turbulence strengths. As a demonstration of feasibility, numerical simulations, and optical experiments were conducted to validate the compensation of OAM beams. Furthermore, the theoretical bit error rate (BER) and channel capacity were inferred based on the purity of OAM modes and the level of crosstalk.",
author = "Peng Hu and Lei Zhu and Jianping Zhu and Jianxin Ren and Shuaidong Chen and Jianxin Ma and Ran Gao and Zexuan Jing and Zhipei Li and Shanting Hu and Bo Tian and Xishuo Wang and Fei Wang and Qi Xu and Qinghua Tian and Huan Chang and Xiangjun Xin and Bo Liu",
note = "Publisher Copyright: {\textcopyright} 2024 Optica Publishing Group.",
year = "2024",
month = dec,
day = "30",
doi = "10.1364/OE.541188",
language = "English",
volume = "32",
pages = "47936--47958",
journal = "Optics Express",
issn = "1094-4087",
publisher = "Optica Publishing Group",
number = "27",
}
Hu, P, Zhu, L, Zhu, J, Ren, J, Chen, S, Ma, J, Gao, R, Jing, Z, Li, Z, Hu, S, Tian, B, Wang, X, Wang, F, Xu, Q, Tian, Q, Chang, H, Xin, X & Liu, B 2024, 'Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications', Optics Express, vol. 32, no. 27, pp. 47936-47958. https://doi.org/10.1364/OE.541188
Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications. / Hu, Peng; Zhu, Lei; Zhu, Jianping et al.
In:
Optics Express, Vol. 32, No. 27, 30.12.2024, p. 47936-47958.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications
AU - Hu, Peng
AU - Zhu, Lei
AU - Zhu, Jianping
AU - Ren, Jianxin
AU - Chen, Shuaidong
AU - Ma, Jianxin
AU - Gao, Ran
AU - Jing, Zexuan
AU - Li, Zhipei
AU - Hu, Shanting
AU - Tian, Bo
AU - Wang, Xishuo
AU - Wang, Fei
AU - Xu, Qi
AU - Tian, Qinghua
AU - Chang, Huan
AU - Xin, Xiangjun
AU - Liu, Bo
N1 - Publisher Copyright:
© 2024 Optica Publishing Group.
PY - 2024/12/30
Y1 - 2024/12/30
N2 - Orbital angular momentum (OAM) multiplexing is emerging as a critical technique for achieving high data capacity in underwater wireless optical communications (UWOC). Nonetheless, wavefront distortions induced by underwater turbulence compromise the orthogonality of OAM modes. In this paper, we introduce a physics-driven untrained learning approach for adaptive optics that operates independently of extensive amplitude datasets. Without iterative processing and pre-trained datasets, the underwater turbulence characteristics can be retrieved accurately by only relying on a one-shot distorted probe beam and a priori known amplitude of the probe beam. By leveraging a single distorted diffraction pattern and a priori known amplitude of the probe beam, the characteristics of underwater turbulence can be accurately retrieved without iterative processing or pre-trained datasets. Furthermore, by implementing a hybrid input/output alternating projection algorithm with a square constraint area, the retrieved underwater turbulence phase screen beyond the [0, 2π] range aligns with the target pattern. This consistency indicates that the proposed wavefront recovery technology is validated across a broad range of turbulence strengths. As a demonstration of feasibility, numerical simulations, and optical experiments were conducted to validate the compensation of OAM beams. Furthermore, the theoretical bit error rate (BER) and channel capacity were inferred based on the purity of OAM modes and the level of crosstalk.
AB - Orbital angular momentum (OAM) multiplexing is emerging as a critical technique for achieving high data capacity in underwater wireless optical communications (UWOC). Nonetheless, wavefront distortions induced by underwater turbulence compromise the orthogonality of OAM modes. In this paper, we introduce a physics-driven untrained learning approach for adaptive optics that operates independently of extensive amplitude datasets. Without iterative processing and pre-trained datasets, the underwater turbulence characteristics can be retrieved accurately by only relying on a one-shot distorted probe beam and a priori known amplitude of the probe beam. By leveraging a single distorted diffraction pattern and a priori known amplitude of the probe beam, the characteristics of underwater turbulence can be accurately retrieved without iterative processing or pre-trained datasets. Furthermore, by implementing a hybrid input/output alternating projection algorithm with a square constraint area, the retrieved underwater turbulence phase screen beyond the [0, 2π] range aligns with the target pattern. This consistency indicates that the proposed wavefront recovery technology is validated across a broad range of turbulence strengths. As a demonstration of feasibility, numerical simulations, and optical experiments were conducted to validate the compensation of OAM beams. Furthermore, the theoretical bit error rate (BER) and channel capacity were inferred based on the purity of OAM modes and the level of crosstalk.
UR - http://www.scopus.com/inward/record.url?scp=85213818492&partnerID=8YFLogxK
U2 - 10.1364/OE.541188
DO - 10.1364/OE.541188
M3 - Article
AN - SCOPUS:85213818492
SN - 1094-4087
VL - 32
SP - 47936
EP - 47958
JO - Optics Express
JF - Optics Express
IS - 27
ER -
Hu P, Zhu L, Zhu J, Ren J, Chen S, Ma J et al. Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications. Optics Express. 2024 Dec 30;32(27):47936-47958. doi: 10.1364/OE.541188