Deep learning-based optical vector-eigenmode processing

被引:0
|
作者
Wen, Feng [1 ]
Li, Jian-Jun [1 ]
Diaol, Shui-Qiu [1 ]
Yang, Feng [2 ]
机构
[1] Univ Elect Sci & Technol China, Key Lab Opt Fiber Sensing & Commun Networks, Minist Educ, Chengdu 611731, Peoples R China
[2] Marolabs Co Ltd, Lab Holog Opt Sensing, Chengdu 610041, Peoples R China
关键词
deep-learning; optical vector-eigenmode decomposition; ResNet; generative adversarial network; MODE DECOMPOSITION; FIBERS;
D O I
10.1109/ICTON62926.2024.10647876
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Optical vector-eigenmode processing for no matter few-mode fibre- or free-space-based systems is crucial to the optical information processing (OIP). In this paper, we review our latest research on the application of deep-learning in laser communications. Firstly, we introduce the deep-learning approach into the vector-eigenmode decomposition of few-mode-based transmission systems, and investigate the decomposition performance through ResNet in detail. Subsequently, we explore the laser communication in free space, where the atmospheric turbulence causes the distortion of the received beam. We employ the generative adversarial network (GAN) to achieve the image restoration, resulting into an accurate reconstruction outcome.
引用
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页数:4
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