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 ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngbin Na ◽  
Do-Kyeong Ko

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.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhe Zhao ◽  
Runzhou Zhang ◽  
Hao Song ◽  
Kai Pang ◽  
Ahmed Almaiman ◽  
...  

AbstractOrbital-angular-momentum (OAM) multiplexing has been utilized to increase the channel capacity in both millimeter-wave and optical domains. Terahertz (THz) wireless communication is attracting increasing attention due to its broadband spectral resources. Thus, it might be valuable to explore the system performance of THz OAM links to further increase the channel capacity. In this paper, we study through simulations the fundamental system-degrading effects when using multiple OAM beams in THz communications links under atmospheric turbulence. We simulate and analyze the effects of divergence, turbulence, limited-size aperture, and misalignment on the signal power and crosstalk of THz OAM links. We find through simulations that the system-degrading effects are different in two scenarios with atmosphere turbulence: (a) when we consider the same strength of phasefront distortion, faster divergence (i.e., lower frequency; smaller beam waist) leads to higher power leakage from the transmitted mode to neighbouring modes; and (b) however, when we consider the same atmospheric turbulence, the divergence effect tends to affect the power leakage much less, and the power leakage increases as the frequency, beam waist, or OAM order increases. Simulation results show that: (i) the crosstalk to the neighbouring mode remains < − 15 dB for a 1-km link under calm weather, when we transmit OAM + 4 at 0.5 THz with a beam waist of 1 m; (ii) for the 3-OAM-multiplexed THz links, the signal-to-interference ratio (SIR) increases by ~ 5–7 dB if the mode spacing increases by 1, and SIR decreases with the multiplexed mode number; and (iii) limited aperture size and misalignment lead to power leakage to other modes under calm weather, while it tends to be unobtrusive under bad weather.


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 ◽  
...  

2021 ◽  
Author(s):  
Mitchell Cox ◽  
Turgay Celik ◽  
Yuval genga ◽  
Alice Drozdov

2017 ◽  
Vol 26 (11) ◽  
pp. 114207 ◽  
Author(s):  
Xiao-zhou Cui ◽  
Xiao-li Yin ◽  
Huan Chang ◽  
Zhi-chao Zhang ◽  
Yong-jun Wang ◽  
...  

2018 ◽  
Vol 38 (12) ◽  
pp. 1227002
Author(s):  
朱卓丹 Zhu Zhuodan ◽  
赵尚弘 Zhao Shanghong ◽  
谷文苑 Gu Wenyuan ◽  
刘菁 Liu Jing ◽  
孙祥祥 Sun Xiangxiang

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