Wavefront reconstruction of vortex beam propagation in atmospheric turbulence based on deep learning

Wei, Hongyan; Xiaolei, Xue; Peng, Jia; Shi, Chenyin

doi:10.1016/j.ijleo.2023.170635

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Their technique and corresponding results on OAM modes recognition have certain significance for improving the reliability of the OAM-based optical communication system based on CNN in the future. Very recently, Hongyan and group prevented information loss and gradient explosion by introduction of a residual layer resulting in achieving obvious advantages in PSNR, ssim, and speed [120]. The structure diagram of the proposed beam restore model can be seen in figure 10(a), while the schematic of the experimental setup is given in figure 10(b) [120].…”

Section: Cnn With Novel Frameworkmentioning

confidence: 99%

“…Very recently, Hongyan and group prevented information loss and gradient explosion by introduction of a residual layer resulting in achieving obvious advantages in PSNR, ssim, and speed [120]. The structure diagram of the proposed beam restore model can be seen in figure 10(a), while the schematic of the experimental setup is given in figure 10(b) [120]. Zhao et al [121] combined CNN with residual blocks and the regression methods to theoretically predict, and experimentally demonstrate that the such a combination can be used to precisely and quickly reconstruct the OAM spectrum of a hybrid OAM mode irrespective of the same or different order indices in both the azimuthal and the radial direction for the consistent OAM modes.…”

Section: Cnn With Novel Frameworkmentioning

confidence: 99%

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Top three intelligent algorithms for OAM mode recognitions in optical communications

Wang,

Zhang,

Shah

et al. 2024

Eng. Res. Express

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Vortex optical communication employing orbital angular momentum (OAM) has been a hot research field in recent years. Thanks to the orthogonality of the OAM, several multiplexing and modulation techniques have been developed that can effectively improve communication capacity. However, to achieve this, accurate mode recognition in the OAM-based free-space optical (FSO) communication system is essential. Generally, perturbations in the free space link significantly affect the transmission efficiency and distort the helical phase-front of OAM beams, which will result in intermodal crosstalk and poses a critical challenge in the recognition of OAM modes. To date, artificial intelligence (AI) technologies have been widely applied to address the aforementioned bottleneck of insufficient accuracy of existing techniques for OAM mode detection. Therefore, a review paper that discusses the recent developments and challenges of the most widely used AI algorithms for OAM mode recognition schemes, i.e., feedforward neural network (FNN), convolutional neural network (CNN), and diffractive deep neural networks (D2NN) is urgently required. By elaborating on the principles of these algorithms and analyzing recent reports, encompassing both experimental and simulated results, we established their profound importance in enhancing the accuracy of OAM mode recognition. Moreover, this work provides an outlook on the recent trends in this newly developed field and the critical challenges faced in effectively using AI for improving the reliability of the OAM-based FSO communication system in near future.

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Section: Cnn With Novel Frameworkmentioning

confidence: 99%