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Learning Enabled Continuous Transmission of Spatially Distributed Information through Multimode Fibers
Author(s) -
Fan Pengfei,
Ruddlesden Michael,
Wang Yufei,
Zhao Luming,
Lu Chao,
Su Lei
Publication year - 2021
Publication title -
laser and photonics reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.778
H-Index - 116
eISSN - 1863-8899
pISSN - 1863-8880
DOI - 10.1002/lpor.202000348
Subject(s) - transmission (telecommunications) , computer science , multi mode optical fiber , scalability , spatial analysis , channel (broadcasting) , data transmission , convolutional neural network , deep learning , information transmission , optical fiber , artificial intelligence , telecommunications , remote sensing , computer network , geology , database
Multimode fibers (MMF) are high‐capacity channels and are promising to transmit spatially distributed information, such as an image. However, continuous transmission of randomly distributed information at a high‐spatial density is still a challenge. Here, a high‐spatial‐density information transmission framework employing deep learning for MMFs is proposed. A proof‐of‐concept experimental system is presented to demonstrate up to 400‐channel simultaneous data transmission with accuracy close to 100% over MMFs of different types, diameters, and lengths. A scalable semi‐supervised learning model is proposed to adapt the convolutional neural network to the time‐varying MMF information channels in real‐time to overcome the instabilities in the lab environment. The preliminary results suggest that deep learning has the potential to maximize the use of the spatial dimension of MMFs for data transmission.