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基于机器学习的轨道角动量光束模式探测技术研究进展

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文章导航 > 光电工程 > 2020 Vol. 47 > No. 3 > 190584

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尹霄丽，崔小舟，常欢，等. 基于机器学习的轨道角动量光束模式探测技术研究进展[J]. 光电工程，2020，47(3)：190584. doi: 10.12086/oee.2020.190584
引用本文: 尹霄丽，崔小舟，常欢，等. 基于机器学习的轨道角动量光束模式探测技术研究进展[J]. 光电工程，2020，47(3)：190584. doi: 10.12086/oee.2020.190584
shu
Yin X L, Cui X Z, Chang H, et al. Research progress of orbital angular momentum modes detecting technology based on machine learning[J]. Opto-Electron Eng, 2020, 47(3): 190584. doi: 10.12086/oee.2020.190584
Citation: Yin X L, Cui X Z, Chang H, et al. Research progress of orbital angular momentum modes detecting technology based on machine learning[J]. Opto-Electron Eng, 2020, 47(3): 190584. doi: 10.12086/oee.2020.190584
shu

基于机器学习的轨道角动量光束模式探测技术研究进展

    • 北京邮电大学电子工程学院天地互联与融合北京市重点实验室，北京 100876

  • 基金项目:
    国家自然科学基金资助项目(61575027)；北京市自然科学基金资助项目(4192041)
详细信息
    作者简介:
  • 尹霄丽(1970-)，女，博士，教授，主要从事光通信与信号处理技术的研究。E-mail: yinxl@bupt.edu.cn
    • 通讯作者: 尹霄丽, E-mail: yinxl@bupt.edu.cn

  • 中图分类号: TN929.1

Research progress of orbital angular momentum modes detecting technology based on machine learning

    • Beijing Key Laboratory of Space-Ground Interconnection and Convergence, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

  • Fund Project: Supported by National Natural Science Foundation of China (61575027) and the Natural Science Foundation of Beijing Municipality(4192041)
More Information

    摘要

  • 轨道角动量(OAM)复用和编码技术可有效提高光通信系统信道容量。近些年研究者提出将机器学习(ML)技术用于OAM模式探测以提高OAM光通信系统性能。本文对基于机器学习的OAM模式探测方案进行了综述，包括误差反向传播(BP)神经网络、自组织神经网络(SOM)、支持向量机(SVM)、卷积神经网络(CNN)、光束变换辅助的识别技术以及全光衍射深度神经网络(D2NN)，分析了各类机器学习OAM探测器在对抗大气、水下信道带来的干扰时展现出的性能差异以及各自优势。

    The orbital angular momentum (OAM) multiplexing and encoding technologies can effectively increase the channel capacity of the optical communication systems. In recent years, some researchers focus on using machine learning (ML) technology to detect OAM modes to improve the performance of OAM optical communication system. In this paper, the OAM modes detecting schemes based on ML technology are reviewed, including error back-propagating (BP) neural networks, self-organizing feature map (SOM), support vector machine (SVM), convolutional neural network (CNN), mode recognition techniques base on beam transformations and all-optics diffractive deep neural networks (D2NN). The performance, advantages and obstacles of each kind of the neural networks in atmosphere and underwater channels are analyzed.

    • Overview

  • Overview: The orbital angular momentum (OAM) modes have orthogonality in theory, thus using OAM multiplexing and encoding technologies can effectively increase the channel capacity of the optical communication systems. However, the phase distributions of OAM modes are sensitive to the channel distribution. The particles and turbulence in atmospheric and underwater channels would lead to the absorptions, scatterings and phase distortions of the beams and decrease the performance of the OAM optical communication system. In recent years, some researchers focus on using machine learning (ML) technology to detect OAM modes to improve the performance of OAM optical communication system. ML technologies have advantages in self-studying and are more tolerant to noise compared to the traditional image recognition technology. In this paper, the OAM modes detecting schemes based on ML technology are reviewed, including error back-propagating (BP) neural networks, self-organizing feature map (SOM), support vector machine (SVM), convolutional neural network (CNN), mode recognition techniques base on beam transformations and diffractive deep neural networks (D2NN). In general, artificial neural networks (ANN), such as BP-ANN, are the earliest ML methods to detecting OAM modes although the detecting accuracies are not high (with 8.33% error ratio in 143 km transmissions); while researches using SVM are not identifying the intensity distributions of OAM beams but the parameters of the beams. The CNN is mainly designed for image classifications thus it has natural advantages in detecting intensity images of OAM beams. The convolutional and pooling operating can make CNNs not sensitive to small offset and extract features by themselves. The research results show that with OAM intensity as the input images, decoding accuracies of LeNet and AlexNet structures can reach more than 99% in even strong atmospheric turbulence no matter with simulations and in lab environments, which are higher than the ANNs. Some improvements of the CNN structures are also made to increase the accuracies. Some researches focus on image transformation of the input pictures, such as angular spectrum transforming, R-CDT transforming, which can efficiently raise the accuracies. While one of the disadvantages of the all-electrical neural networks is the high time delay. In 2018, researchers proposed a kind of all-optical neural network called D2NN and used it as OAM detector, which can realize relative high accuracies without time delay. All in all, the OAM detectors using ML can achieve high detecting accuracies compared to traditional OAM sorting methods.

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  • 图 1  拓扑荷数为l0的叠加态光束在发送端的光场强度分布图。(a)(h)对应的拓扑荷l0分别对应1至8

    Figure 1.  Superposed intensity distributions of OAM modes l0 at the transmitted terminal.(a)(h) the superposed OAM modes l0 from 1 to 8

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    图 2  BP神经网络结构示意图[32]

    Figure 2.  Schematic diagram of BP-ANN[32]

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    图 3  SOM神经网络结构示意图[32]

    Figure 3.  Schematic diagram of SOM[32]

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    图 4  (a) 维也纳市区OAM光束传输外场实验图[22]；(b) OAM模式串扰矩阵；(c)传输及接收的两幅图片

    Figure 4.  (a) The OAM transmitted experiments in Vienna[22]; (b) OAM crosstalk matrix; (c) Transmitted and received pictures

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    图 5  Canary岛屿间海洋性大气信道OAM光束传输实验示意图与外场照片[23]

    Figure 5.  Diagram and pictures of OAM transmitted experiments in marine atmospheric channels between Canary Islands[23]

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    图 6  灯塔上的接收光斑[23]

    Figure 6.  Captured intensity pictures at the lighthouse[23]

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    图 7  SVM分类的输入空间和特征空间原理图[32]

    Figure 7.  Schematic of input and feature space of SVM[32]

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    图 8  (a) 基于SVM的单态OAM模式识别系统原理图；(b)基于SVM的OAM模式识别系统在不同信道条件下识别率曲线[33]

    Figure 8.  a) Schematic of SVM-OAM detecting system; (b) Curves of detecting accuracies under different channels of SVM-OAM systems[33]

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    图 9  (a) CNN的工作流程(前馈运算)过程；(b)卷积操作示意图；(c)池化操作示意图

    Figure 9.  (a) Process of forward-propagation of CNNs; (b) Example of convolution operation; (c) Example of pooling operation

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    图 10  不同网络结构的OAM模式识别效果图[25]

    Figure 10.  OAM modes detecting accuracies of different neural network structures[25]

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    图 11  CNN-OAM解码和大气湍流识别系统结构示意图[36]

    Figure 11.  Schematic of decoding process and structure of CNN-OAM systems[36]

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    图 12  CNN-OAM模式识别系统方案图[24]

    Figure 12.  Schematic of CNN-OAM detecting system [24]

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    图 13  View-pooling层CNN系统示意图[37]

    Figure 13.  Schematic diagram of CNN with view-pooling layer[37]

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    图 14  μ取不同值时±l叠加光束光斑图样的锐利度曲线[40]

    Figure 14.  Sharpness curves of intensity distribution for different μ[40]

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    图 15  (a) 水下信道传输CNN-OAM系统示意图；(b)水下信道传输CNN-OAM系统实验装置图[42]

    Figure 15.  Schematic of underwater CNN-OAM systems; (b) Photo of underwater CNN-OAM experiment[42]

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    图 16  (a) 将非线性不易区分分类转换成线性可分分类示意图；(b) CCD采集到的贝塞尔高斯叠加光束在焦点处的强度图；(c)经过R-CDT变换后的输出图片[43]

    Figure 16.  (a) Schematic of transforming linearly un-separating to linearly separating; (b) Intensity distributions of BG beams at focus point; (c) Output pictures after R-CDT transformation[43]

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    图 17  (a) 基于相干光干涉探测的CNN-OAM模式识别系统结构图；(b) OAM叠加光束的干涉条纹；(c)受到大气湍流干扰的OAM叠加光束的干涉条纹[44]

    Figure 17.  (a) Structure of CNN-OAM detecting system based on coherent optical interference; (b) Interference fringes of OAM superposition beams; (c) Interference fringes of OAM superposition beams propagating in atmospheric turbulence channels[44]

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    图 18  (a) D2NN结构示意图；(b)识别数字示例[45]

    Figure 18.  (a) Structure of D2NN; (b) Process of identifying a digits[45]

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    图 19  D2NN-OAM模式识别系统示意图[46]

    Figure 19.  Schematic of D2NN-OAM detecting systems[46]

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