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Deep Learning Based Recognition of Different Mode Bases in Ring‐Core Fiber
Laser & Photonics Reviews ( IF 9.8 ) Pub Date : 2020-10-07 , DOI: 10.1002/lpor.202000249
Lulu Wang 1 , Zhengsen Ruan 1 , Hongya Wang 1 , Lei Shen 2 , Lei Zhang 2 , Jie Luo 2 , Jian Wang 1
Affiliation  

In fiber‐optic communications using diverse spatial modes for sustainable capacity scaling, the intelligent recognition of different mode bases is of great importance to enhance the flexiblity and compatibility of mode management. Here a convolutional neural network (CNN) model is introduced to recognize the four mode bases with the azimuthal index ℓ= 5, namely the LP5,1 mode group, the linearly and circularly polarized OAM±5,1 mode group, and the vector EH4,1 or HE6,1 mode group in a ring‐core fiber. A camera is first used to capture intensity profiles of mode bases as training and testing data sets of the neural network. The CNN‐based deep learning successfully recognizes different mode bases with an overall recognition rate of close to 100%. Furthermore, an alternative compact and cost‐effective approach is considered toward practical applications by replacing the camera with a photodetector (PD) array for intelligent mode bases recognition. A 1 × 5 PD array can perfectly recognize different mode bases with a recognition rate of close to 100%. Even a 1 × 2 PD array with only two PDs can obtain a high recognition rate of close to 93.3%. The demonstrations may open up new perspectives for deep learning enabled robust and intelligent optical communications exploiting spatial modes.

中文翻译:

基于深度学习的环芯光纤中不同模式基的识别

在使用多种空间模式进行可持续容量扩展的光纤通信中,不同模式基础的智能识别对于增强模式管理的灵活性和兼容性至关重要。这里引入了卷积神经网络(CNN)模型来识别方位指数ℓ= 5的四个模式基,即LP 5,1模式组,线性和圆极化OAM ±5,1模式组以及矢量EH 4,1或HE 6,1环芯光纤中的模式组。首先使用摄像头捕获模式库的强度轮廓,作为神经网络的训练和测试数据集。基于CNN的深度学习成功识别了不同的模式库,总体识别率接近100%。此外,在实际应用中考虑了一种替代的紧凑且经济高效的方法,即用光电检测器(PD)阵列代替相机以进行智能模式基准识别。1×5 PD阵列可以以接近100%的识别率完美识别不同的模式基础。即使只有两个PD的1×2 PD阵列也可以获得接近93.3%的高识别率。这些演示可以为利用空间模式进行深度学习的强大而智能的光通信打开新的视野。
更新日期:2020-11-12
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