Machine Learning-Based Fast Integer and Fractional Vortex Modes Recognition of Partially Occluded Vortex Beams

In this work, a machine learning method is proposed to precisely classify partially occluded integer and fractional vortex modes for the first time in radio frequency (RF). Consequently, we introduce three training schemes, i.e., the direct recognition scheme with the phase data or the amplitude data (PD-DRS and AD-DRS), the phase data or amplitude data interpolated by nearest-neighbor interpolation algorithm (PD-NNI and AD-NNI), and the full data (FD) of the electric field with the NNI algorithm (FD-NNI), to recognize the topological charges. Based on the designed deep convolutional neural network (DCNN) models, the relationship between the test accuracy and the number of sampling points of the three schemes is presented. It is shown that
$3\times3$
sampling points are enough for FD-NNI to achieve the classification accuracy of 98.2%. To validate the robustness of the proposed models, we evaluate them on the sample carrying up to 50% Gaussian noise, separately. Besides, the effects of propagation distance and the occlusion angle are also investigated. The numerical results present that the interpolated data performs better in terms of accuracy compared with the pure sampled data, among which FD-NNI possesses better generalization ability, suggesting great potential in the practical application of radio vorticity communication.