Deep Learning Based Recognition of Different Mode Bases in Ring‐Core Fiber

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 LP 5,1 mode group, the linearly and circularly polarized OAM ±5,1 mode group, and the vector EH 4,1 or HE 6,1 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.