  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.
