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• Chinese Optics Letters
• Vol. 20, Issue 2, 020601 (2022)
Lin Zhao1、2, Yuan Hao1, Li Chen1, Wenyi Liu1, Meng Jin1, Yi Wu1, Jiamin Tao1, Kaiqian Jie1, and Hongzhan Liu1、*
Author Affiliations
• 1Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, Guangzhou 510006, China
• 2School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China
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Fig. 1. (a) and (c) are the interference principles between Gaussian beams and VBs with l = +4 and l = −4, respectively. (b) and (d) are obtained interferograms corresponding to l = +4 and l = −4.
Fig. 2. Recognition principle of OAM mode: OAM mode equals the number of intersections between the average value and the falling edge of the waveform, and the mode sign is determined by comparing the phase difference of the waveform at the radii r and r + Δr.
Fig. 3. Schematic diagram of OAM-FSO communication system with VIR-GSF demodulation technique under the free-space AT channel.
Fig. 4. Filtering effect of GSF at different levels m, where m is set as 15 and 25. The comparison of waveforms after GSF operation and the original waveform is given.
Fig. 5. Intensity distribution and interferogram of the VBs when the transmission distance is 1 km, where (a), (b), and (c) represent the weak, medium, and strong-turbulence levels, respectively.
Fig. 6. Recognition accuracy of various turbulence levels when the transmission distance is 1 km. (a) shows the accuracy under no-turbulence, weak-turbulence, and medium-turbulence conditions. (b) shows that the recognition accuracy and average accuracy under the conditions of medium-strong and strong turbulence.
Fig. 7. (a) Recognition accuracy of the VIR-GSF scheme at the conditions of no turbulence, weak turbulence, and medium turbulence. The recognition accuracy and average accuracy under (b) medium-strong and (c) strong-turbulence conditions. (d) Comparison of performance of CNN, CNN-GS, and VIR-GSF schemes in a turbulent environment for transmission of 2 km.
 1: Input: intensity images F1(N×N) and interferograms F2(N×N) 2: Convert F1(N×N), F2(N×N) to grayscale image as F′1(N×N), F′2(N×N) 3: forR = $1:1:$N/2 do 4: Extract elements on circle with radius R from F′1(N×N) as MR 5:  A(R) = mean(MR) 6: end for 7: r = {R|A(R) = MAX{A}} 8: Extract elements on circle with radius r from F′2(N×N) as M′r 9: Extract elements on circle with radius (r + Δr) from F′2(N×N) as M′r+Δr 10: $M¯r′=GSF${M′r|m = 25} 11: $M¯r′+Δr=GSF${M′r+Δr|m = 25} 12: Compare the phase difference $φ$ between $M¯r′$ and $M¯r+Δr′$ 13: if$φ>0$then 14:  s = +1 15: else 16:  s = −1 17: end if 18: Calculate |l| by intersections between curves $M¯r′$ and A(r) 19: l = s × |l| 20: Return: OAM mode l
Table 1. The Proposed VIR-GSF Algorithm
Copy Citation Text
Lin Zhao, Yuan Hao, Li Chen, Wenyi Liu, Meng Jin, Yi Wu, Jiamin Tao, Kaiqian Jie, Hongzhan Liu. High-accuracy mode recognition method in orbital angular momentum optical communication system[J]. Chinese Optics Letters, 2022, 20(2): 020601