Experimental research on intelligent channel equalization technology for optical wireless coherent communication

被引：0
|
作者：
Yang S. [1 ]
Ke X. [1 ,2 ]
Liang J. [1 ]
机构：
[1] School of Automation and Information Engineering, Xi′an University of Technology, Xi′an
[2] Shaanxi Civil-Military Integration Key Laboratory of Intelligence Collaborative Networks, Xi′an
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2023年 / 44卷 / 05期
关键词：
channel equalization; intermediate frequency signal; optical wireless coherent communication; wavefront distortion correction;
D O I：
10.19650/j.cnki.cjsi.J2210799
中图分类号：
学科分类号：
摘要：
Channel equalization is used to suppress the inter symbol interference caused by atmospheric turbulence in the broadband optical wireless coherent communication system. In this article, intermediate frequency signals in turbulent environment are used as training samples. Back propagation (BP) neural network and long short-term memory (LSTM) neural network are utilized for training. The trained stable network model is used as a channel equalizer, and the output intermediate frequency signal by equalizer is used as the evaluation index of system performance and compared with the adaptive optics wavefront distortion correction algorithm. The simulation results show that by using BP neural network channel equalization technology, LSTM neural network channel equalization technology, and wavefront distortion correction technology, the peak values of intermediate frequency signal histogram are located at 0. 49, 0. 38, and 0. 38 V, and the corresponding system bit error rate is 3. 79×10-5、1. 64×10-4 and 8.48×10-2, respectively. Compared with wavefront distortion correction technology, intelligent channel equalization technology has significantly improved on the random fluctuation of intermediate frequency signal amplitude and bit error rate. © 2023 Science Press. All rights reserved.
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页码：131 / 139
页数：8
相关论文
共 23 条
  • [1] KARANOV B, LAVERY D, BAYVEL P, Et al., End-to-end optimized transmission over dispersive intensity-modulated channels using bidirectional recurrent neural networks [J], Optics Express, 27, 14, pp. 19650-19663, (2019)
  • [2] HE H, LI G, JUANG B., Power of deep learning for channel estimation and signal detection in OFDM systems, IEEE Wireless Communications Letters, 7, 1, pp. 114-117, (2018)
  • [3] SCHAEDLER M, BCHERER G, PACHNICKE S., Softdemapping for short reach optical communication: A comparison of deep neural networks and Volterra series, Journal of Lightwave Technology, 39, 10, pp. 3095-3105, (2021)
  • [4] PATRA J C, MEHER P K, CHAKRABORTY G., Nonlinear channel equalization for wireless communication systems using Legendre neural networks, Signal Processing, 89, pp. 2251-2262, (2009)
  • [5] LALLA S, GAGNE C, ZENG M, Et al., Recurrent neural networks achieving MLSE performance for optical channel equalization [J], Optics Express, 29, 9, pp. 13033-13047, (2021)
  • [6] RAJBHANDARI S, GHASSEMLOOY Z, ANGELOVA M., Effective denoising and adaptive equalization of indoor optical wireless channel with artificial light using the discrete wavelet transform and artificial neural network, Journal of Lightwave Technology, 27, 20, pp. 4493-4500, (2009)
  • [7] ZHANG Y, WANG P, LIU T, Et al., Performance analysis of a LDPC coded OAM based UCA FSO system exploring linear equalization with channel estimation over atmospheric turbulence, Optics Express, 26, 17, pp. 22182-22196, (2018)
  • [8] YAO H F, NI X L, CHEN CH Y, Et al., Channel compensation based on pulse laser propagating in atmosphere, Acta Optica Sinica, 38, 1, (2018)
  • [9] ZHANG D, HAO S Q, ZHAO Q S, Et al., Differential phase-shift keying and channel equalization in free space optical communication system, Optical Engineering, 57, 1, (2018)
  • [10] ESMAIL M A, SAIF W S, RAGHEB A M, Et al., Free space optic channel monitoring using machine learning, Optics Express, 29, 7, pp. 10967-10981, (2021)
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