Data transmission with up to 100 orbital angular momentum modes via commercial multi-mode fiber and parallel neural networks

Abstract

This work presents an artificial intelligence enhanced orbital angular momentum (OAM) data transmission system. This system enables encoded data retrieval from speckle patterns generated by an incident beam carrying different topological charges (TCs) at the distal end of a multi-mode fiber. An appropriately trained network is shown to support up to 100 different fractional TCs in parallel with TC intervals as small as 0.01, thus overcoming the problems with previous methods that only supported a few modes and could not use small TC intervals. Additionally, an approach using multiple parallel neural networks is proposed that can increase the system’s channel capacity without increasing individual network complexity. When compared with a single network, multiple parallel networks can achieve the better performance with reduced training data requirements, which is beneficial in saving computational capacity while also expanding the network bandwidth. Finally, we demonstrate high-fidelity image transmission using a 16-bit system and four parallel 14-bit systems via OAM mode multiplexing through a 1-km-long commercial multi-mode fiber (MMF).