Deep Learning Based Recognition of Different Mode Bases in Ring‐Core Fiber. Issue 11 (7th October 2020)

• Record Type: Journal Article
• Authors: Wang, Lulu; Ruan, Zhengsen; Wang, Hongya; Shen, Lei; Zhang, Lei; Luo, Jie; Wang, Jian
• Is Part Of: Laser & photonics reviews. Volume 14:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-07
• Subjects: convolution neural networks -- deep learning -- machine learning -- mode bases -- orbital angular momentum -- ring‐core fibers -- vector modes; Lasers -- Periodicals; Photonics -- Periodicals; Lasers -- Périodiques; Photonique -- Périodiques; 621.36
• DOI: 10.1002/lpor.202000249
• Languages: English
• ISSNs: 1863-8880

Abstract

Abstract: 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. Abstract : The convolutional neural network (CNN)‐based deep learning is introduced to intelligently recognize four mode bases in a ring‐core fiber, namely the linearly polarized mode group, the linearly and circularly polarized orbital angular momentum (OAM) mode group, and the vector mode group. The demonstrations may pave the way for deep learning enabled robust and intelligent optical communications exploiting diverse spatial modes.