Vision Transformers Motivating Superior OAM Mode Recognition in Optical Communications
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影响因子：3.8
DOI码：10.1364/OE.504841
发表刊物：Optics Express
关键字：transformer; deep learning; OAM recognition; turbulence; wireless communication
摘要：Orbital angular momentum (OAM) has recently obtained tremendous research interest in free-space optical communications (FSO). During signal transmission within the free-space link, atmospheric turbulence (AT) poses a significant challenge as it diminishes the signal strength and introduce intermodal crosstalk, significantly reducing OAM mode detection accuracy. This issue directly impacts the performance of OAM-based communication systems and leads to a reduction in received information. To address this critical bottleneck of low mode recognition accuracy in OAM-based FSO-communications, a deep learning method based on vision transformers (ViT) is proposed for what we believe is for the first time. Designed carefully by numerous experts, the advanced self-attention mechanism of ViT captures more global information from the input image. To train the model, pretraining on a large dataset, named IMAGENET is conducted. Subsequently, we performed fine-tuning on our specific dataset, consisting of OAM beams that have undergone varying AT strengths. The computer simulation shows that based on ViT method, the multiple OAM modes can be recognized with a high accuracy (nearly 100%) under weak-to-moderate turbulence and with almost 98% accuracy even under long transmission distance with strong turbulence (C N2=1×10-14). Our findings highlight that leveraging ViT enables robust detection of complex OAM beams, mitigating the adverse effects caused by atmospheric turbulence.
合写作者：Bingyi Liu,Zhixiang Li
第一作者：Badreddine Merabet,Jinlong Tian,Kai Guo
论文类型：期刊论文
通讯作者：Syed Afaq Ali Shah,Zhongyi Guo
文献类型：J
卷号：31
期号：23
页面范围：38958-38969
是否译文：否
发表时间：2023-11-01
收录刊物：SCI
发布期刊链接：https://opg.optica.org/oe/fulltext.cfm?uri=oe-31-23-38958&id=541366