Recognition of high-resolution optical vortex modes with deep residual learning
Zhou Jingwen, Yaling Yin, Jihong Tang, Ling Chen, Meng Cao, Luping Cao, Guanhua Liu, Jianping Yin, Yong Xia
Abstract
Optical vortex beams with fractional orbital angular momentum (OAM) can greatly enhance the channel capacity in free-space optical communication. However, high precision measurement of fractional OAM modes is always difficult, especially under the influence of atmospheric turbulence (AT). In this work, we identify the high-resolution OAM modes down to 0.01 using an improved residual neural network (ResNet) architecture based convolutional neural network (CNN). Experimentally, using a single cylindrical lens, the light intensity distribution can be readily converted into a diffraction pattern containing significant features trained into a CNN model. For the fractional OAM modes from 5.0 to 5.9 over a long propagation distance of 1500 m, at 0.1 resolution, our model's predicting accuracy is up to 99.07% under strong AT, ${C}_{\mathrm{n}}^{2}=1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}15}\phantom{\rule{4pt}{0ex}}{\mathrm{m}}^{\ensuremath{-}2/3}$. At 0.01 resolution, the accuracy is as high as 86.98% under intermediate AT, ${C}_{\mathrm{n}}^{2}=1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}16}\phantom{\rule{4pt}{0ex}}{\mathrm{m}}^{\ensuremath{-}2/3}$, and exceeds 73.78% under strong AT, ${C}_{\mathrm{n}}^{2}=1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}15}\phantom{\rule{4pt}{0ex}}{\mathrm{m}}^{\ensuremath{-}2/3}$. So, these results may have great implications in free-space optical communication.