A set of laser beams carrying orbital angular momentum is designed with the objective of establishing an effective underwater communication link. Messages are constructed using unique Laguerre–Gauss beams, which can be combined to represent four bits of information. We report on the experimental results where the beams are transmitted through highly turbid water, reaching approximately 12 attenuation lengths. We measured the signal-to-noise ratio in each test scenario to provide characterization of the underwater environment. A convolutional neural network was developed to decode the received images with the objective of successfully classifying messages quickly. We demonstrate near-perfect classification in all scenarios, provided the training set includes some images taken under the same underwater conditions.

中文翻译：

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机器学习辅助的在高混浊水中传播的带有轨道角动量的光束的分类

为了建立有效的水下通信链路，设计了一组携带轨道角动量的激光束。消息是使用唯一的Laguerre-Gauss光束构造的，可以组合起来代表四位信息。我们报告的实验结果是，光束通过高度浑浊的水传输，达到大约12个衰减长度。我们测量了每种测试场景中的信噪比，以提供水下环境的特征。为了快速成功地对消息进行分类，开发了卷积神经网络来解码接收到的图像。如果训练集包括在相同水下条件下拍摄的一些图像，我们将在所有情况下展示近乎完美的分类。