Identifying orbital angular momentum modes in turbulence with high accuracy via machine learning

Sun, Ridong; Guo, Lixin; Cheng, Mingjian; Yan, Xu

doi:10.1088/2040-8986/ab2586

Cited by 23 publications

(9 citation statements)

References 32 publications

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“…[66] 2016 SOM They achieved the transmission and detection of OAM modes over a distance of 143 km, which is the maximum distance achieved experimentally. [74] 2019 SVM The detection accuracy of a single SVM model in severe turbulent environments (1 × 10 −13 m −2/3 ) is close to 100% within 2500 m. [75] 2020 SVM A linear SVM was used to classify experimental images, giving rising to an average accuracy of 98%. [80] 2016 FNN This network offered a detecting accuracy of >74% for 110 OAM superposition states.…”

Section: Referencesmentioning

confidence: 99%

Orbital angular momentum optical communications enhanced by artificial intelligence

Li¹,

Luan²,

Li³

et al. 2022

J. Opt.

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Angular momentum of light can be divided into spin angular momentum (SAM) and orbital angular momentum (OAM). Due to the theoretically unlimited orthogonal states, the physical dimension of OAM provides a potential solution to boost the information capacity. The OAM multiplexing and modulation techniques have been implemented to meet the continuous growth of bandwidth requirements, resulting in the concept of OAM optical communication. However, the performances of the traditional optical OAM detection techniques degrade seriously in the practical application of OAM optical communications. Thanks to the powerful data analysis advantages, the cutting-edge machine learning (ML) algorithms have been widely used in the field of image processing, laying the technical foundation for OAM recognition. This paper reviews the recent advances on OAM optical communications that are enhanced by ML methods. More than the traditional OAM detection methods, the OAM demodulation methods based on multiple network architectures, including the support vector machine (SVM), self-organizing map (SOM), feed-forward neural network (FNN), convolutional neural network (CNN), and diffractive deep optical neural network (D2NN), have been summarized. We also discuss the development of the spiking neural network and on-chip D2NN, opening a possible way to facilitate the future ultra-low power and ultra-fast OAM demodulation technology.

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Section: Referencesmentioning

confidence: 99%

Orbital angular momentum optical communications enhanced by artificial intelligence

Li¹,

Luan²,

Li³

et al. 2022

J. Opt.

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“…The diffraction method detects OAM patterns by designing special diffractive optics and measuring the far-field diffraction pattern after the vortex beam passes through the diffractive element. In addition, the support vector machine learning model can achieve the recognition effect by extracting sample features [ 11 , 12 , 13 , 14 ], but when the sample size is large, the recognition effect is saturated. The spiral wavefront phase of the vortex beam is susceptible to turbulence, resulting in pattern dispersion and intensity distortion, which in turn leads to the distortion of interference or diffraction fringes.…”

Section: Introductionmentioning

confidence: 99%

Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network

Sun

Huang

et al. 2023

Sensors

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In underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficiency. In a case of the disturbance of a vortex beam by ocean turbulence, where a Laguerre–Gaussian (LG) beam carrying orbital angular momentum (OAM) is damaged by turbulence and distortion, which affects OAM pattern recognition, and the phase feature of the phase map not only has spiral wavefront but also phase singularity feature, the convolutional neural network (CNN) model can effectively extract the information of the distorted OAM phase map to realize the recognition of dual-mode OAM and single-mode OAM. The phase map of the Laguerre–Gaussian beam passing through ocean turbulence was used as a dataset to simulate and analyze the OAM recognition effect during turbulence caused by different temperature ratios and salinity. The results showed that, during strong turbulence Cn2=1.0×10−13K2m−2/3, when different ω = −1.75, the recognition rate of dual-mode OAM (ℓ = ±1~±5, ±1~±6, ±1~±7, ±1~±8, ±1~±9, ±1~±10) had higher recognition rates of 100%, 100%, 100%, 100%, 98.89%, and 98.67% and single-mode OAM (ℓ = 1~5, 1~6, 1~7, 1~8, 1~9, 1~10) had higher recognition rates of 93.33%, 92.77%, 92.33%, 90%, 87.78%, and 84%, respectively. With the increase in ω, the recognition accuracy of the CNN model will gradually decrease, and in a fixed case, the dual-mode OAM has stronger anti-interference ability than single-mode OAM. These results may provide a reference for optical communication technologies that implement high-capacity OAM.

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“…Both CNN and FNN have high accuracy, but there are limitations due to the quest for large training samples and high complexity. On the other hand, the machine learning technique has a good robustness with small datasets and it is capable of extracting the feature vector of trained samples [24][25][26]. Investigation shows that spatial distribution of OAM beams under ocean turbulence can be identified by analyzing the phase space distribution of the beams under turbulence.…”

Section: Introductionmentioning

confidence: 99%

Identification of Orbital Angular Momentum by Support Vector Machine in Ocean Turbulence

Huang

Sun

2022

JMSE

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With the advancement of underwater communication technology, the traditional modulation dimension has been introduced, developed and utilized. In addition, orbital angular momentum (OAM) is utilized as the modulation dimension for optical underwater communication to obtain larger spectrum resources. The OAM features are extracted using a histogram of oriented gradient and trained using the support vector machine method with a gradient direction histogram feature. The topological charge value of the OAM was used to identify the classification labels, and the ocean turbulence caused by different temperatures and salinity were analyzed. Experimentation results showed that the recognition accuracy for the OAM under the Laguerre–Gaussian beam rates of 1~5, 1~6, 1~7, 1~8, 1~9, and 1~10 was 98.93%, 98.89%, 97.33%, 96.66%, 95.40%, and 95.33%, respectively. The proposed method achieved a high recognition accuracy and performed efficiently under strong turbulence. Our research explored a new technique that provides a new idea for the demodulation of OAM in optical underwater communication.

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Identifying orbital angular momentum modes in turbulence with high accuracy via machine learning

Cited by 23 publications

References 32 publications

Orbital angular momentum optical communications enhanced by artificial intelligence

Orbital angular momentum optical communications enhanced by artificial intelligence

Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network

Identification of Orbital Angular Momentum by Support Vector Machine in Ocean Turbulence

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