Deep-learning-based Location Recognition Method for Detecting Multi-beam Multi-OAM Vortex Waves
2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT)(2023)
摘要
Orbital angular momentum (OAM) has attracted attention since it could provide infinite orthogonal channels and effectively improve spectrum utilization in free-space optical communications. Although OAM pattern recognition widely utilizes deep learning techniques, the advanced identification (equipped with precise localization) of multi-beams remains challenging. In response to this challenge, we propose a new method that employs transfer learning based on a convolutional neural network (CNN). The approach features the capabilities of localization and recognition of various vortex beams. We train the model using phase distribution images of OAM beams of 9 types, rather than the amplitude distributions. Through simulation and experimentation, we demonstrate that the proposed method reduces the training time and improves the detection accuracy by up to 98%. These results contribute to the demultiplexing system of free-space optical OAM systems.
更多查看译文
关键词
Convolutional Neural Network (CNN),Free Space Optics (FSO) communication,Orbital Angular Momentum (OAM)
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要