arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.01788physics.opticseess.SP

Learning Enabled Dense Space-division Multiplexing through a Single Multimode Fibre

Pengfei Fan, Michael Ruddlesden, Yufei Wang, Luming Zhao, Chao Lu, Lei Su

英文摘要:

Space-division multiplexing is a promising technology in optical fibre communication to improve the transmission capacity of a single optical fibre. However, the number of channels that can be multiplexed is limited by the crosstalks between channels, and the multiplexing is only applied to few-mode or multi-core fibres. Here, we propose a high-spatial-density channel multiplexing framework employing deep learning for standard multimode fibres (MMF). We present a proof-of-concept experimental system, consisting of a single light source, a single digital-micromirror-device modulator, a single detection camera, and a deep convolutional neural network (CNN) to demonstrate up to 400-channel simultaneous data transmission with accuracy close to 100% over MMFs of different types, diameters and lengths. A novel scalable semi-supervised learning model is proposed to adapt the CNN to the time-varying MMF information channels in real-time, to overcome the environmental changes such as temperature variations and vibrations, and to reconstruct the input data from complex crosstalks among hundreds of channels. This deep-learning based approach is promising to maximize the use of the spatial dimension of MMFs, and to break the present number-of-channel limit in space-division multiplexing for future high-capacity MMF transmission data links.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑