Learning to recognize misaligned hyperfine orbital angular momentum modes
Xiao Wang, Yufeng Qian, Jingjing Zhang, Guangdong Ma, Shupeng Zhao, Ruifeng Liu, Hongrong Li, Pei Zhang, Hong Gao, Feng Huang, Fuli Li
Abstract
Orbital angular momentum (OAM)-carrying beams have received extensive attention due to their high-dimensional characteristics in the context of free-space optical communication. However, accurate OAM mode recognition still suffers from reference misalignment of lateral displacement, beam waist size, and initial phase. Here we propose a deep-learning method to exquisitely recognize OAM modes under misalignment by using an alignment-free fractal multipoint interferometer. Our experiments achieve 98.35% recognizing accuracy when strong misalignment is added to hyperfine OAM modes whose Bures distance is 0.01. The maximum lateral displacement we added with respect to the perfectly on-axis beam is about <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mo form="prefix">±</mml:mo> <mml:mn>0.5</mml:mn> </mml:mrow> </mml:math> beam waist size. This work offers a superstable proposal for OAM mode recognition in the application of free-space optical communication and allows an increase of the communication capacity.