Learning-based multiplexed transmission of scattered twisted light through a kilometer-scale standard multimode fiber

TL;DR

This paper presents a deep learning approach to reliably demultiplex and decode scattered twisted light OAM channels over a kilometer of standard multimode fiber, significantly enhancing data capacity and robustness in optical communications.

Contribution

It introduces a novel AI-based method for high-accuracy OAM mode identification and data decoding in long-distance fiber transmission without phase measurements.

Findings

Achieved over 99.9% accuracy in OAM mode demultiplexing

Decoded color images with only 0.13% error rate

Demonstrated effective multiplexed transmission over 1 km fiber

Abstract

Multiplexing multiple orbital angular momentum (OAM) modes of light has the potential to increase data capacity in optical communication. However, the distribution of such modes over long distances remains challenging. Free-space transmission is strongly influenced by atmospheric turbulence and light scattering, while the wave distortion induced by the mode dispersion in fibers disables OAM demultiplexing in fiber-optic communications. Here, a deep-learning-based approach is developed to recover the data from scattered OAM channels without measuring any phase information. Over a 1-km-long standard multimode fiber, the method is able to identify different OAM modes with an accuracy of more than 99.9% in parallel demultiplexing of 24 scattered OAM channels. To demonstrate the transmission quality, color images are encoded in multiplexed twisted light and our method achieves decoding the transmitted data with an error rate of 0.13%. Our work shows the artificial intelligence algorithm could benefit the use of OAM multiplexing in commercial fiber networks and high-performance optical communication in turbulent environments.