深度学习辅助的具有离散化结构光状态空间的光通信
Deep-learning-assisted optical communication with discretized state space of structured light
AI总结：
本文提出利用深度学习对结构光的LG模式进行识别，通过离散化空间模式将OAM回归转为分类，实现小样本高精度分类，并应用于图像传输，展示了低OAM数下高容量光通信的潜力。
摘要：
结构光的横向空间模式的丰富结构促进了其在量子信息和光通信中的广泛应用。携带明确轨道角动量（OAM）的拉盖尔-高斯（LG）模式构成描述光横向空间模式的完备正交基。由于实验上有限的OAM数和复杂的OAM识别方法，OAM在自由空间光通信中的应用受到限制。在此，我们提出了一种新方法，利用先进的深度学习技术进行LG模式识别。通过离散化结构光的空间模式，我们将OAM状态回归转化为分类问题。我们还进行了原理验证实验，表明我们的方法能以少量训练样本和高精度有效地对OAM状态进行分类。通过为每个类别分配经典信息，我们进一步将我们的方法应用于图像传输任务，展示了以低OAM数编码大数据的能力。这项工作为基于结构光实现低OAM数的高容量光通信开辟了新途径。
英文摘要：
The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre-Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application of OAM in free-space optical communication is restricted due to the experimentally limited OAM numbers and the complex OAM recognition methods. Here, we present a novel method that uses the advanced deep learning technique for LG modes recognition. By discretizing the spatial modes of structured light, we turn the OAM state regression into classification. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and high accuracy. By assigning each category a classical information, we further apply our approach to an image transmission task, demonstrating the ability to encode large data with low OAM number. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light.
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