Convolutional Neural Network-Assisted Optical Orbital Angular Momentum Recognition and Communication
Peipei Wang,
Junmin Liu,
Lijuan Sheng,
Yanliang He,
Wenjie Xiong,
Zebin Huang,
Xinxing Zhou,
Ying Li,
Shuqing Chen,
Xiaomin Zhang,
Dianyuan Fan
Affiliations
Peipei Wang
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
Junmin Liu
College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, China
Lijuan Sheng
Synergetic Innovation Center for Quantum Effects and Applications, School of Physics and Electronics, Hunan Normal University, Changsha, China
Yanliang He
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
Wenjie Xiong
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
Zebin Huang
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
Xinxing Zhou
Synergetic Innovation Center for Quantum Effects and Applications, School of Physics and Electronics, Hunan Normal University, Changsha, China
Ying Li
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
Xiaomin Zhang
Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang, China
Dianyuan Fan
International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China
The identification of orbital angular momentum (OAM) modes with high-accuracy and -speed is always a difficult issue in practically applying optical vortex beams (OVs). In this work, we propose and experimentally investigate a convolutional neural network (CNN) method for optical OAM mode identification and shift-keying (SK) communications. The CNN model, including convolution and pooling layers, was designed to extract mode information from the diffraction patterns produced by diffracting the OVs with a cylindrical lens. After trained with loads of studying samples, the CNN model has a good generation ability in recognizing the OAM modes of OVs ranging from -15 to 15. The recognition accuracy reaches 99% with the turbulence intensity of Cn2 = 1 × 10-13m-2/3, Δz = 50 m. Even under the turbulence of Cn2 = 1 × 10-12 m-2/3, Δz = 50 m, the accuracy still exceeds 89%. By mapping and encoding a Lena gray image with the size of 100 × 100 pixels to two OAM channels, the OAM-SK signals with 900 modulation orders were successfully demodulated by the CNN model, and the image was well recovered after transmission. With an I5-8500 Central Processing Unit, this recognition process only takes 1 ×10-3 s per mode. It is anticipated that the CNN methods might provide an effective way for identifying OAM modes with high-accuracy and -speed, which may have great potentials in OAM communication, quantum information processing, and astronomical application, etc.
