Suppr超能文献

使用人工神经网络进行紊流修正。

Turbulence correction with artificial neural networks.

出版信息

Opt Lett. 2018 Jun 1;43(11):2611-2614. doi: 10.1364/OL.43.002611.

Abstract

We design an optical feedback network making use of machine learning (ML) techniques and demonstrate via simulations its ability to correct for the effects of turbulent propagation on optical modes. This artificial neural network scheme relies only on measuring the intensity profile of the distorted modes, making the approach simple and robust. The network results in the generation of various mode profiles at the transmitter that, after propagation through turbulence, closely resemble the desired target mode. The corrected optical mode profiles at the receiver are found to be nearly identical to the desired profiles, with near-zero mean square error indices. We are hopeful that the present results combining the fields of ML and optical communications will greatly enhance the robustness of free-space optical links.

摘要

我们设计了一个利用机器学习(ML)技术的光反馈网络,并通过模拟演示了它纠正湍流对光模式影响的能力。该人工神经网络方案仅依赖于测量失真模式的强度分布,从而使该方法简单且鲁棒。该网络导致在发射器处生成各种模式分布,在经过湍流传播后,这些模式分布与期望的目标模式非常相似。在接收器处,校正后的光模式分布与期望的模式分布几乎相同,均方误差指数接近零。我们希望将 ML 和光通信领域的现有结果相结合,将极大地提高自由空间光链路的稳健性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验