Optical Communication Technology, Volume. 47, Issue 4, 58(2023)
OAM pattern recognition of high order composite LG beams based on convolutional neural networks
[1] [1] ARLT J, DHOLAKIA K, ALLEN L, et al. The production of multi-ringed Laguerre–Gaussian modes by computer-generated holograms[J]. Journal of Modern Optics, 1998, 45(6): 1231-1237.
[2] [2] WANG J, YANG J Y, FAZAL I M, et al. Terabit free-space data trans-mission employing orbital angular momentum multiplexing[J]. Nature pho-tonics, 2012, 6(7): 488-496.
[3] [3] XIE G, LI L, YAN Y, et al. Performance metrics for a free-space communi-cation link based on multiplexing of multiple orbital angular momentum beams with higher order radial indices[C]. OSA OLEO: Applications and Technology, May 10-15, 2015, San Jose, USA. San Jose: OSA, 2015: JTh2A.62-1-JTh2A.62-2.
[4] [4] XIE G, REN Y, HUANG H, et al. Experimental analysis of multipl-exing/demultiplexing Laguerre Gaussian beams with different radial index[C]. OSA Frontiers in Optics 2014, October 19-23, Tucson, USA. Tucson, OSA: 2014: FTh4B.6-1-FTh4B.6-2.
[5] [5] LI L, XIE G, YAN Y, et al. Power loss mitigation of orbital-angular-momentum-multiplexed free-space optical links using nonzero radial index Laguerre-Gaussian beams[J]. JOSA B, 2017, 34(1): 1-6.
[6] [6] WANG J, LIU J, LI S, et al. Orbital angular momentum and beyond in free-space optical communications[J]. Nanophotonics, 2022, 11(4): 645-680.
[7] [7] ANGUITA J A, NEIFELD M A, VASIC B V. Turbulence-induced cha-nnel crosstalk in an orbital angular momentum-multiplexed free-space opti-cal link[J]. Applied Optics, 2008, 47(13): 2414-2429.
[8] [8] DOSTER T, WATNIK A T. Machine learning approach to OAM beam demultiplexing via convolutional neural networks[J]. Applied Optics, 2017, 56(12): 3386-3396.
[9] [9] LI J, ZHANG M, WANG D, et al. Joint atmospheric turbulence detec-tion and adaptive demodulation technique using the CNN for the OAM-FSO communication[J]. Optics Express, 2018, 26(8): 10494-10508.
[10] [10] WANG Z, DEDO M I, GUO K, et al. Efficient recognition of the pro-pagated orbital angular momentum modes in turbulences with the convolu-tional neural network[J]. IEEE Photonics Journal, 2019, 11(3): 1-14.
[11] [11] DEDO M I, WANG Z, GUO K, et al. OAM mode recognition based on joint scheme of combining the Gerchberg–Saxton (GS) algorithm and convo-lutional neural network(CNN)[J]. Optics Communications, 2020, 456: 124696-1-124696-8.
[12] [12] NA Y, KO D K. Adaptive demodulation by deep-learning-based identi-fication of fractional orbital angular momentum modes with structural dis-tortion due to atmospheric turbulence[J]. Scientific Reports, 2021, 11(1): 1-12.
[13] [13] ALLEN L, BEIJERSBERGEN M W, SPREEUW R J C, et al. Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes[J]. Physical Review A, 1992, 45(11): 81-85.
[14] [14] TANG H, XU W, WU G. Average capacity of OAM-multiplexed FSO system with vortex beam propagating through non-Kolmogorov turbulence[J]. China Communications, 2016, 13(10): 153-159.
Get Citation
Copy Citation Text
SONG Zhiyi, JIN Zhaoxiang, WANG Yuyan, CHEN Jianfei, ZHANG Sheng. OAM pattern recognition of high order composite LG beams based on convolutional neural networks[J]. Optical Communication Technology, 2023, 47(4): 58
Received: Feb. 2, 2023
Accepted: --
Published Online: Feb. 2, 2024
The Author Email: