Detecting Orbital Angular Momentum Modes of Vortex Beams Using Feed-Forward Neural Network

Detecting orbital angular momentum (OAM) modes with high-speed and -accuracy is always a challenge in the practical applications of vortex beams (VBs). Here, we design a multilayer feed-forward neural network (FNN) model to rapidly identify OAM modes. After trained with loads of learning samples influenced by the transmission distance, beam waist, and atmospheric turbulence, the model shows a good generalization ability in quickly identifying the OAM mode of VBs ranging from -25 to +25. The classification accuracy reaches 99.55% under the influence of moderate turbulence and achieves 68.53% even under strong turbulence. Furthermore, we construct a communication link to deliver a 100 × 100 pixels Lena gray image by encoding OAM modes. After free-space transmission, the OAM-SK signals are demodulated by the FNN model, and the Lena gray image is well reconstituted. With an i5-6500 CPU, the identification process takes only 7.5 × 10^-5 s per OAM mode. These numerical simulation results demonstrate that the proposed FNN model provides a feasible way to rapidly identify the OAM modes of VBs and may show great potential in the optical OAM communication, etc.