摘要
相位失真是实现涡旋光束轨道角动量复用技术实际应用的主要挑战之一。本文提出了一种基于深度学习的复合贝塞尔高斯涡旋光束大气湍流效应补偿方法,以提高模态分离与检测准确度。设计的网络通过学习不同轨道角动量下畸变光束强度分布与湍流相位之间的映射关系,具备了适应未知湍流环境的泛化能力,可以有效地预测等效湍流相位屏。仿真结果表明,复合贝塞尔高斯光束在不同湍流强度下传输1000 m并经过相位补偿后,光强相关系数可提高至0.97以上;在强湍流下传输1500 m并经相位补偿后,拓扑荷数为10的模式纯度从2.43%提高至64.07%。该方法对畸变光束具有更强的特征提取能力,在快速准确预测等效湍流相位屏方面具有良好的泛化能力,有助于提高未来轨道角动量复用技术的可靠性。
Objective Atmospheric turbulence(AT)severely affects the transmission of vortex beams(VBs)transmitted in the atmosphere.Wavefront distortion,coherence destruction,and orthogonality destruction of multiplexed VBs are the main effects of AT,which directly increase crosstalk among channels and reduce communication performance.To improve the robustness of optical orbital angular momentum(OAM)communications,considerable efforts have been made to effectively compensate for the phase distortion of VBs.The adaptive optical method is widely used but requires multiple iterations and complicated hardware that is not affordable or easily operated by most researchers,causing tremendous difficulties for further study.Recently,taking advantage of powerful signal processing techniques,deep learning has been widely used in many fields such as image classification and optical communication,providing researchers with a new approach for addressing these problems.In this study,we propose a novel method of AT compensation based on a deep learning method to effectively correct the distorted composite Bessel-Gaussian(BG)vortex beam and improve the robustness of OAM multiplexing communication.Methods Using a deep learning method,we designed a new model called the phase extraction network(PhaNet),which combines a residual network with a feature pyramid for AT phase extraction(Fig.2).The PhaNet model can automatically learn the mapping relationship between the intensity distribution of the distorted beam and the turbulence phase under different orbital angular momenta.It contains seven convolutional layers,four residual layers,six deconvolution layers,and three feature fusion layers.A total of 96000 images of BG vortex beam intensity with a specified turbulence range were randomly generated,80000 of which were used as training data,with the remaining 16000 serving as test data.Following training with the loads of the studied samples,the PhaNet model was used to directly predict AT phase screens based on the intensity distribution of the di
作者
杜芊芊
韦宏艳
史晨寅
薛晓磊
贾鹏
Du Qianqian;Wei Hongyan;Shi Chenyin;Xue Xiaolei;Jia Peng(College of Optoelectronic Engineering,Taiyuan University of Technology,Taiyuan 030006,Shanxi,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2023年第22期111-117,共7页
Chinese Journal of Lasers
基金
国家自然科学基金(61805173)。
关键词
光通信
复合贝塞尔高斯光束
大气湍流
深度学习
相位补偿
optics communications
composite Bessel-Gaussian beam
atmospheric turbulence
deep learning
phase compensation
