论文标题
多模纤维中的学习和避免混乱
Learning and avoiding disorder in multimode fibers
论文作者
论文摘要
在过去的十年中,多模光纤(MMF)在过去的十年中引起了新的兴趣,这是一种在当前基于单模光纤网络的预期饱和的背景下提高光学通信数据速率的一种方式。它们对内窥镜应用也很有吸引力,它提供了获得与多核纤维相似的信息内容的可能性,但足迹却较小,从而降低了内窥镜程序的侵入性。但是,这些进步受到了不可避免的疾病的存在的阻碍,这会影响MMF中光的传播并限制其实际应用。我们在这里介绍一个一般框架,以研究和避免疾病的影响。我们在实验上找到了一组几乎完整的光通道,这些光通道对强烈变形引起的疾病有弹性。这些变形原理模式仅通过利用弱扰动测量值来获得。我们通过证明,即使对于高度的疾病,MMF中光的传播也只能以少数关键特性的特征来解释这种效果。由于对光纤的模态传输矩阵的精确估算,该结果依赖于使用深度学习框架的基于模型的优化。
Multimode optical fibers (MMFs) have gained renewed interest in the past decade, emerging as a way to boost optical communication data-rates in the context of an expected saturation of current single-mode fiber-based networks. They are also attractive for endoscopic applications, offering the possibility to achieve a similar information content as multicore fibers, but with a much smaller footprint, thus reducing the invasiveness of endoscopic procedures. However, these advances are hindered by the unavoidable presence of disorder that affects the propagation of light in MMFs and limits their practical applications. We introduce here a general framework to study and avoid the effect of disorder. We experimentally find an almost complete set of optical channels that are resilient to disorder induced by strong deformations. These deformation principle modes are obtained by only exploiting measurements for weak perturbations. We explain this effect by demonstrating that, even for a high level of disorder, the propagation of light in MMFs can be characterized by just a few key properties. These results are made possible thanks to a precise and fast estimation of the modal transmission matrix of the fiber which relies on a model-based optimization using deep learning frameworks.