Experimental Study of Atmospheric Turbulence Detection Using an Orbital Angular Momentum Beam Via a Convolutional Neural Network

Atmospheric turbulence (AT) tends to impair the performance of free space optical (FSO) communication systems. Detecting AT strength is significant for turbulence effect mitigation, which can appropriately guide the selection of turbulence mitigation techniques and modulation formats. Orbital angular momentum (OAM) beam aberrations received through the turbulence channel are closely related to the turbulence strength. In this paper, we experimentally detect the AT strength using an OAM beam based on a convolutional neural network (CNN). We collect 8 kinds of superposed OAM beam intensity images after 5 levels of turbulence in the laboratory as datasets and test the AT detector performance with respect to the number of pixels, mode number of OAM beams, different AT sets and training set size. The results show that the AT detection accuracy is near 100% for 3 kinds of ATs, and the accuracy remains at approximately 85% for 5 kinds of ATs. In addition, using data augmentation methods or a hybrid dataset can improve the AT detection accuracy. The CNN-based method in this paper can help detect the AT strength in atmospheric channels and provide references for choosing appropriate techniques to mitigate turbulence effects and then enhance the OAM-FSO system performance.