半监督学习在多模光纤上实现了可扩展的高空间密度信道复用

Pengfei Fan, M. Ruddlesden, Yufei Wang, Luming Zhao, Chao Lu, Lei Su
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引用次数: 1

摘要

为了克服多模光纤(MMF)信息通道的高时间变异性,提出了一种半监督置信度学习方法(SCALA),并通过实验证明了高空间密度信息在不同MMF上的连续传输精度接近100%。
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Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers
We proposed a semi-supervised confidence-based learning approach (SCALA) to overcome the high-temporal-variability of multimode fiber (MMF) information channels, and experimentally demonstrated continuous transmission of high-spatial-density information with accuracy close to 100% over different MMFs.
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