Learning Enabled Continuous Transmission of Spatially Distributed Information through Multimode Fibers
Learning Enabled Continuous Transmission of Spatially Distributed Information through Multimode Fibers
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DOI：
10.1002/lpor.202000348
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发表时间：
2021-02-24
影响因子：
11
通讯作者：
Su, Lei
中科院分区：
文献类型：
--
作者：
Fan, Pengfei;Ruddlesden, Michael;Su, Lei
多模光纤（MMF）是一种大容量信道，有望用于传输空间分布信息，如图像。然而，在高空间密度下，随机分布信息的连续传输仍然是一个挑战。本文提出了一种基于深度学习的mmf高空间密度信息传输框架。提出了一种概念验证实验系统，可以在不同类型、直径和长度的mmf上演示多达400通道的同时数据传输，精度接近100%。提出了一种可扩展的半监督学习模型，使卷积神经网络实时适应时变的MMF信息通道，以克服实验室环境中的不稳定性。初步结果表明，深度学习有潜力最大限度地利用mmf的空间维度进行数据传输。
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.