陕西师范大学学报（自然科学版）2024，Vol.52Issue(2)：111-120,10.DOI:10.15983/j.cnki.jsnu.2024309
基于简单无参注意力卷积神经网络的涡旋光束模态识别
Modes recognition algorithm of vortex beam based on simple parameter-free attention convolutional neural networks
摘要
Abstract
When vortex beam propagates in the atmosphere,phase distortion is generated due to the influence of atmospheric turbulence,which makes it difficult to detect the mode at the receiving end and reduces the reliability of the communication system.In order to improve the accuracy of vortex beam mode recognition,a simple parameter-free attention convolution neural network(S-ConvNeXt)is proposed.Results show that this proposed network can effectively focus on key bright spot features.When the transmission distance is 2 km,the accuracy of eigenstate recognition can reach 100%,98.8%,96.4%,89.7%,the accuracy of superposition state recognition can reach 100%,99.8%,98.8%,96.5%,via weak turbulence,medium turbulence,strong turbulence and stronger turbulence respectively.Under strong turbulence,the eigenstate recognition accuracy of S-ConvNeXt is 5.7%,3%and 1.2%higher than that of ResNet50,ShuffleNetV2 and Conv NeXt,and the superposition state recognition accuracy of S-Conv NeXt is 5.7%,4%and 0.9%higher than that of ResNet50,ShuffleNetV2 and ConvNeXt respectively.S-Conv NeXt can effectively improve the accuracy of mode recognition,especially in strong turbulence.关键词
涡旋光束/Conv NeXt网络/大气湍流/模态识别/注意力机制Key words
vortex beam/ConvNeXt/atmospheric turbulence/mode recognition/attention mech-anisms分类
信息技术与安全科学引用本文复制引用
魏冬梅,刘芳宁,杜乾,王珂,赵曰峰..基于简单无参注意力卷积神经网络的涡旋光束模态识别[J].陕西师范大学学报（自然科学版）,2024,52(2):111-120,10.基金项目
国家自然科学基金(42271093) （42271093）
山东省本科教学改革研究项目(M2021235) （M2021235）
