基于卷积神经网络的涡旋光相干解复用
Convolutional neural network-based coherent demultiplexing of vortex beam
投稿时间:2020-02-18  修订日期:2020-02-18
DOI:
中文关键词: 卷积神经网络  涡旋光  相干解复用
英文关键词: CNN  vortex beam  coherent demultiplexing
基金项目:湖北省自然科学基金科技支撑计划项目(2015BCE048)、国家科技支撑计划课题(2015BAD29B01)、湖北省自然科学基金重点项目(2014CFA051)、中央高校基本科研业务费专项资金自科培育项目(CZP17026)
作者单位E-mail
杨春勇 中南民族大学 电子信息工程学院 cyyang@mail.scuec.edu.cn 
闪开鸽 中南民族大学 电子信息工程学院 547275992@qq.com 
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中文摘要:
      提出了一种基于卷积神经网络的相干解复用方法,用于实现复用涡旋光束的解复用。将卷积神经网络训练成模式分类器,用于识别并输出涡旋光束的轨道角动量模式。根据模式分类器输出的模式信息,选择特定的相位掩膜,并附加到高斯光束上,将高斯光束转换为本振光(携带特定轨道角动量模式的涡旋光束)。复用涡旋光束与本振光进行相干检测,可以实现复用涡旋光束的解复用。数值模拟结果表明,在中等强度大气湍流下,模式分类准确率及解复用准确率均达99%以上。
英文摘要:
      A novel coherent demultiplexing method based on convolutional neural network (CNN) is proposed to realize the demultiplexing of multiplexed vortex beams. The CNN is trained as a mode classifier for identifying and outputting orbital angular momentum (OAM) modes of vortex beams. Based on the mode information output by the mode classifier, a specific phase mask is selected. Then, the phase mask is attached to the Gaussian beam, and the Gaussian beam is converted into oscillating beam (vortex beam carrying a specific OAM mode). The multiplexed vortex beam is coherently detected with the oscillating beam to achieve demultiplexing of the multiplexed vortex beam. Numerical simulation results show that the classification accuracy (CA) and the demultiplexing accuracy (DA) can reach 99% under moderate-intensity atmospheric turbulence.
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