Interferometric orbital angular momentum modedetection in turbulence with deep learning

2021 ◽  
Author(s):  
Mitchell Cox ◽  
Turgay Celik ◽  
Yuval genga ◽  
Alice Drozdov
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Orbital Angular Momentum

scholarly journals Performance Analysis of 3D Video Transmission over Multi-coded N-ary Orbital Angular Momentum FSO System using Deep-learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Shimaa A. El-Meadawy ◽  
Hossam M. H. Shalaby ◽  
Nabil A. Ismail ◽  
Ahmed E. A. Farghal ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Performance Analysis ◽  
Orbital Angular Momentum ◽  
Video Transmission ◽  
3D Video

Deep learning as a tool to distinguish between high orbital angular momentum optical modes

2016 ◽  
Author(s):  
E. M. Knutson ◽  
Sanjaya Lohani ◽  
Onur Danaci ◽  
Sean D. Huver ◽  
Ryan T. Glasser
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Orbital Angular Momentum ◽  
Optical Modes

scholarly journals Deep learning based atmospheric turbulence compensation for orbital angular momentum beam distortion and communication

2019 ◽  
Vol 27 (12) ◽  
pp. 16671 ◽  
Author(s):  
Junmin Liu ◽  
Peipei Wang ◽  
Xiaoke Zhang ◽  
Yanliang He ◽  
Xinxing Zhou ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Atmospheric Turbulence ◽  
Orbital Angular Momentum

A deep learning approach for trustworthy high-fidelity computational holographic orbital angular momentum communication

2021 ◽  
Vol 119 (4) ◽  
pp. 044104
Author(s):  
Hongqiang Zhou ◽  
Yongtian Wang ◽  
Xin Li ◽  
Zhentao Xu ◽  
Xiaowei Li ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Orbital Angular Momentum ◽  
Learning Approach ◽  
High Fidelity

scholarly journals High-Accuracy Recognition of Orbital Angular Momentum Modes Propagated in Atmospheric Turbulences Based on Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 159542-159551
Author(s):  
Yuan Hao ◽  
Lin Zhao ◽  
Tao Huang ◽  
Yi Wu ◽  
Ting Jiang ◽  
...  
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
Orbital Angular Momentum ◽  
High Accuracy

scholarly journals Adaptive demodulation by deep-learning-based identification of fractional orbital angular momentum modes with structural distortion due to atmospheric turbulence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngbin Na ◽  
Do-Kyeong Ko
Keyword(s):  
Angular Momentum ◽  
Deep Learning ◽  
High Resolution ◽  
Atmospheric Turbulence ◽  
Orbital Angular Momentum ◽  
Optical Communications ◽  
Recognition Performance ◽  
High Capacity ◽  
Great Success ◽  
Free Space Optical

AbstractSince the great success of optical communications utilizing orbital angular momentum (OAM), increasing the number of addressable spatial modes in the given physical resources has always been an important yet challenging problem. The recent improvement in measurement resolution through deep-learning techniques has demonstrated the possibility of high-capacity free-space optical communications based on fractional OAM modes. However, due to a tiny gap between adjacent modes, such systems are highly susceptible to external perturbations such as atmospheric turbulence (AT). Here, we propose an AT adaptive neural network (ATANN) and study high-resolution recognition of fractional OAM modes in the presence of turbulence. We perform simulations of fractional OAM beams propagating through a 1-km optical turbulence channel and analyze the effects of turbulence strength, OAM mode interval, and signal noise on the recognition performance of the ATANN. The recognition of multiplexed fractional modes is also investigated to demonstrate the feasibility of high-dimensional data transmission in the proposed deep-learning-based system. Our results show that the proposed model can predict transmitted modes with high accuracy and high resolution despite the collapse of structured fields due to AT and provide stable performance over a wide SNR range.

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