A study on the utilization of deep learning for identifying fractional orbital angular momentum beams in atmospheric turbulence media

Youngbin Na; Do-Kyeong Ko

doi:10.1117/12.2621104

20 May 2022 A study on the utilization of deep learning for identifying fractional orbital angular momentum beams in atmospheric turbulence media

Youngbin Na, Do-Kyeong Ko

Author Affiliations +

Proceedings Volume PC12138, Optics, Photonics and Digital Technologies for Imaging Applications VII; (2022) https://doi.org/10.1117/12.2621104
Event: SPIE Photonics Europe, 2022, Strasbourg, France

Conference Poster

Abstract

In this research, we propose the application of a deep-learning method for recognizing fractional orbital angular momentum (OAM) beams distorted by atmospheric turbulence (AT). In order to acquire data sets for model training and testing, we first simulate the propagation of fractional OAM beams through a 1000-m optical channel. Here, AT effects in the channel are modeled with five random phase screens equally spaced. Then, the effects of turbulence strength and OAM mode spacing on the recognition accuracy are analyzed. In particular, we compare the recognition accuracy for three types of OAM-encoding schemes, two single-mode sets (integer and fractional OAM) and one multiplexed fractional mode set. Despite the strong distortion, our designed model shows the ability to recognize transmitted OAM modes with high accuracy and high resolution. In addition to this, we investigate the generalization ability of deep learning to accommodate unknown turbulence environments.

Conference Presentation

Citation Download Citation

Youngbin Na and Do-Kyeong Ko "A study on the utilization of deep learning for identifying fractional orbital angular momentum beams in atmospheric turbulence media", Proc. SPIE PC12138, Optics, Photonics and Digital Technologies for Imaging Applications VII, (20 May 2022); https://doi.org/10.1117/12.2621104

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