Multiplexing orbital angular momentum (OAM) modes enable high-capacity optical communication. However, the highly similar speckle patterns of adjacent OAM modes produced by strong mode coupling in common fibers prevent the utility of OAM channel demultiplexing. In this paper, we propose a machine learning-supported fractional OAM-multiplexed data transmission system to sort highly scattered data from up to 32 multiplexed OAM channels propagating through a commercial multi-mode fiber parallelly with an accuracy of >99.92%, which is the largest bit number of OAM superstates reported to date (to the best of our knowledge). Here, by learning limited samples, unseen OAM superstates during the training process can be predicted precisely, which reduces the explosive quantity of the dataset. To verify its application, both gray and colored images, encoded by the given system, have been successfully transmitted with error rates of <0.26%. Our work might provide a promising avenue for high-capacity OAM optical communication in scattering environments.

中文翻译：

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通过商用多模光纤实现具有多达 32 个复用轨道角动量通道的学习型数据传输

复用轨道角动量 (OAM) 模式可实现高容量光通信。然而，普通光纤中强模耦合产生的相邻 OAM 模式高度相似的散斑图案阻碍了 OAM 信道解复用的实用性。在本文中，我们提出了一种机器学习支持的部分 OAM 复用数据传输系统，用于对通过商用多模光纤并行传播的多达 32 个复用 OAM 通道中的高度分散的数据进行排序，准确度 >99.92%，这是迄今为止（据我们所知）报告的 OAM 超级状态的最大位数。这里，通过学习有限的样本，可以精确预测训练过程中未见过的OAM超级状态，从而减少数据集的爆炸量。为了验证其应用，由给定系统编码的灰度和彩色图像均已成功传输，错误率<0.26%。我们的工作可能为散射环境中的高容量 OAM 光通信提供一条有前途的途径。