摘要
在自由光空间通信领域,使用不同束腰半径组合的涡旋叠加光束可以在同一信道开销下传递更多信息。受大气湍流影响涡旋光束会发生相位扰动,进而影响其轨道角动量(OAM)模态识别。现有模型无法准确识别受随机大气湍流影响而发生扰动的OAM叠加光束模态。因此,提出一种基于注意力机制的深度学习识别方法。将注意力机制模块嵌入到VGG-16中,以提升模型对不同状态叠加光束模态的感知性能。另外为模拟湍流的真实状态,利用功率谱反演法模拟大气湍流,并使用次谐波补偿随机湍流屏的低频信息。同时,建立受到随机湍流影响发生相位扰动的OAM叠加光束数据集,利用该数据集训练所提模型。实验结果表明,在未知大气湍流强度条件下,对比传统方法,所提方法对OAM的识别准确率最高提升了4.46%。这表明了该模型对识别OAM叠加光束的有效性,以及良好的鲁棒性和较好的泛化能力,为识别OAM模态提供一种新的方法。
In free-space optical communication,vortex-superimposed beams with different radius combinations can transmit more information at the same channel overhead.However,the vortex beam undergoes phase disturbances owing to atmospheric turbulence,affecting the ability to identify its orbital angular momentum(OAM)modes.Existing models cannot precisely identify OAM superimposed beam modes perturbed by random atmospheric turbulence.Therefore,a deep learning recognition method based on attention mechanism is proposed.The attention mechanism module is embedded in VGG-16 to improve the perception performance of the model for superimposed beam modes in different states.In addition,the atmospheric turbulence is simulated using the power spectrum inversion method to simulate the actual state of turbulence,and subharmonics are used to compensate for the low-frequency information of the random turbulence screen.An OAM superimposed beam dataset affected by random turbulence is established,and the proposed model is trained using this dataset.The experimental results show that under the condition of unknown atmospheric turbulence intensity,the accuracy of the proposed method compared to those of traditional methods improves by up to 4.46%.This demonstrates the effectiveness of the model for identifying OAM superimposed beams.In addition,the proposed model exhibits good robustness and generalization ability.This study provides a new method for identifying OAM modes.
作者
周旭
陈纯毅
于海洋
倪小龙
胡小娟
Zhou Xu;Chen Chunyi;Yu Haiyang;Ni Xiaolong;Hu Xiaojuan(Key Laboratory of Photoelectric Measurement&Control and Optical Information Transfer Technology,Ministry of Education,Changchun University of Science and Technology,Changchun 130022,Jilin,China;School of Computer Science and Technology,Changchun University of Science and Technology,Changchun 130022,Jilin,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2023年第23期94-100,共7页
Laser & Optoelectronics Progress
基金
国家自然科学基金(62275033)
吉林省科技发展计划(YDZJ202101ZYTS151)
吉林省教育厅科学技术研究项目(JJKH20210844KJ)。
关键词
涡旋光束
轨道角动量
注意力机制
深度学习
大气湍流
vortex beams
orbital angular momentum
attention mechanism
deep learning
atmospheric turbulence
