Multiplexing multiple orbital angular momentum (OAM) modes of light has proven to be an effective way to increase data capacity in fiber-optic communications. However, existing techniques for distributing the OAM modes rely on specially designed fibers or couplers. Direct transmission of multiplexed OAM modes through a long standard multimode fiber remains challenging because the strong mode coupling in fibers disables OAM demultiplexing. Here, we propose a deep-learning-based approach to recover the scattered data from multiplexed OAM channels without measuring any phase information. Over a 1-km-long standard multimode fiber, our method is able to identify different OAM modes with an accuracy of more than 99.9% in the parallel demultiplexing of 24 scattered OAM channels. To demonstrate the transmission quality, color images are encoded in multiplexed twisted light and our method achieves decoding the transmitted data with an error rate of 0.13%. Our work shows that the artificial intelligence algorithm could benefit the use of OAM multiplexing in commercial fiber networks and high-performance optical communication in turbulent environments.

中文翻译：

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支持学习的从通过长标准多模光纤传输的扭曲光中恢复散射数据

多重轨道角动量 (OAM) 光模式已被证明是增加光纤通信数据容量的有效方法。然而，分配 OAM 模式的现有技术依赖于专门设计的光纤或耦合器。通过长距离标准多模光纤直接传输复用 OAM 模式仍然具有挑战性，因为光纤中的强模式耦合会禁用 OAM 解复用。在这里，我们提出了一种基于深度学习的方法，可以在不测量任何相位信息的情况下从复用 OAM 通道中恢复分散数据。在 1 公里长的标准多模光纤上，我们的方法能够在 24 个分散的 OAM 信道的并行解复用中识别不同的 OAM 模式，准确率超过 99.9%。为了展示传输质量，彩色图像在多路复用扭曲光中编码，我们的方法实现了对传输数据的解码，错误率为 0.13%。我们的工作表明，人工智能算法有利于在商业光纤网络中使用 OAM 多路复用，以及在湍流环境中实现高性能光通信。