We generate an alphabet of spatially multiplexed Laguerre–Gaussian beams carrying orbital angular momentum, which are demultiplexed at reception by a convolutional neural network (CNN). In this investigation, a methodology for optimizing alphabet design for best classification rates is proposed, and three 256-symbol alphabets are designed for performance evaluation in optical turbulence. The beams were propagated in three environments: through underwater optical turbulence generated by Rayleigh–Bénard (RB) convection (C2n≅10−11m−2/3${C}_{n}^{2}\cong 1{0}^{-11}\;{\rm m}^{-2/3}$), through a simulated propagation path derived from the Nikishov spectrum (C2n≅10−13m−2/3${C}_{n}^{2}\cong 1{0}^{-13}\; {\rm m}^{-2/3}$), and through optical turbulence from a thermal point source located in a water tank (C2n≅10−10m−2/3${C}_{n}^{2}\cong 1{0}^{-10}\;{\rm m}^{-2/3}$). We report a classification accuracy of 93.1% for the RB environment, 99.99% in simulation, and 48.5% in the point source environment. The project demonstrates that the CNN can classify the complex alphabet symbols in a practical turbulent flow that exhibits strong optical turbulence, provided sufficient training data is available and testing data is representative of the specific environment. We find the most important factor in a high classification accuracy is a diversification in the intensity profiles of the alphabet symbols.

中文翻译：

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通过受控实验生成的光学湍流传播后携带轨道角动量的光束的神经网络分类

我们生成带有轨道角动量的空间复用拉盖尔-高斯光束字母表，这些光束在接收时由卷积神经网络（CNN）进行解复用。在这项研究中，提出了一种优化字母表设计以获得最佳分类率的方法，并设计了三个 256 符号字母表用于光学湍流中的性能评估。光束在三种环境中传播： 通过瑞利-贝纳德 (RB) 对流产生的水下光学湍流 ( C 2 n ≅ 1 0 − 11米− 2 / 3${C}_{n}^{2}\cong 1{0}^{-11}\;{\rm m}^{-2/3}$），通过从 Nikishov 谱导出的模拟传播路径 ( C 2 n ≅ 1 0 − 13米− 2 / 3${C}_{n}^{2}\cong 1{0}^{-13}\; {\rm m}^{-2/3}$），并通过位于水箱中的热点源的光学湍流 ( C 2 n ≅ 1 0 − 10米− 2 / 3${C}_{n}^{2}\cong 1{0}^{-10}\;{\rm m}^{-2/3}$）。我们报告 RB 环境中的分类准确率为 93.1%，模拟中的分类准确率为 99.99%，点源环境中的分类准确率为 48.5%。该项目证明，只要有足够的训练数据并且测试数据能够代表特定环境，CNN 就可以对具有强烈光学湍流的实际湍流中的复杂字母符号进行分类。我们发现高分类精度的最重要因素是字母符号强度分布的多样化。