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Learning Enabled Continuous Transmission of Spatially Distributed Information through Multimode Fibers
Laser & Photonics Reviews ( IF 11.0 ) Pub Date : 2021-02-24 , DOI: 10.1002/lpor.202000348 Pengfei Fan 1 , Michael Ruddlesden 1 , Yufei Wang 1 , Luming Zhao 1, 2 , Chao Lu 3 , Lei Su 1
Laser & Photonics Reviews ( IF 11.0 ) Pub Date : 2021-02-24 , DOI: 10.1002/lpor.202000348 Pengfei Fan 1 , Michael Ruddlesden 1 , Yufei Wang 1 , Luming Zhao 1, 2 , Chao Lu 3 , Lei Su 1
Affiliation
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.
中文翻译:
学习使能通过多模光纤连续传输空间分布信息
多模光纤(MMF)是高容量信道,并有望传输空间分布的信息,例如图像。但是,以高空间密度连续传输随机分布的信息仍然是一个挑战。在此,提出了一种针对MMF采用深度学习的高空间密度信息传输框架。提出了概念验证实验系统,以演示多达400个通道的同时数据传输,在不同类型,直径和长度的MMF上,其精度接近100%。提出了一种可扩展的半监督学习模型,以使卷积神经网络实时适应随时间变化的MMF信息通道,以克服实验室环境中的不稳定性。
更新日期:2021-04-11
中文翻译:
学习使能通过多模光纤连续传输空间分布信息
多模光纤(MMF)是高容量信道,并有望传输空间分布的信息,例如图像。但是,以高空间密度连续传输随机分布的信息仍然是一个挑战。在此,提出了一种针对MMF采用深度学习的高空间密度信息传输框架。提出了概念验证实验系统,以演示多达400个通道的同时数据传输,在不同类型,直径和长度的MMF上,其精度接近100%。提出了一种可扩展的半监督学习模型,以使卷积神经网络实时适应随时间变化的MMF信息通道,以克服实验室环境中的不稳定性。