Open Access768-ary Laguerre-Gaussian-mode shift keying free-space optical communication based on convolutional neural networks
Author(s) -
Haitao Luan,
Dongqing Lin,
Keyao Li,
Weijia Meng,
Miṅ Gu,
Xinyuan Fang
Publication year - 2021
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.420176
Subject(s) - convolutional neural network , keying , optics , optical communication , computer science , free space optical communication , artificial intelligence , physics , algorithm , telecommunications
Beyond orbital angular momentum of Laguerre-Gaussian (LG) modes, the radial index can also be exploited as information channel in free-space optical (FSO) communication to extend the communication capacity, resulting in the LG- shift keying (LG-SK) FSO communications. However, the recognition of radial index is critical and tough when the superposed high-order LG modes are disturbed by the atmospheric turbulences (ATs). In this paper, the convolutional neural network (CNN) is utilized to recognize both the azimuthal and radial index of superposed LG modes. We experimentally demonstrate the application of CNN model in a 10-meter 768-ary LG-SK FSO communication system at the AT of C n 2 = 1e-14 m -2/3 . Based on the high recognition accuracy of the CNN model (>95%) in the scheme, a colorful image can be transmitted and the peak signal-to-noise ratio of the received image can exceed 35 dB. We anticipate that our results can stimulate further researches on the utilization of the potential applications of LG modes with non-zero radial index based on the artificial-intelligence-enhanced optoelectronic systems.