Laser & Optoelectronics Progress, Volume. 60, Issue 17, 1701001(2023)

Performance Research on Underwater Vortex Optical Multiplexing System Based on Back Propagation Neural Network Blind Equalization Algorithm

Mingjun Wang1,2、* and Sikai Tu1
Author Affiliations
  • 1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, Shaanxi , China
  • 2Shaanxi Civil-Military Integration Key Laboratory of Intelligence Collaborative Networks, Xi'an 710126, Shaanxi , China
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    Vortex optical multiplexing communication technology can effectively improve the channel capacity of a communication system in an underwater channel. However, ocean turbulence causes the inter modal crosstalk of the vortex beam, degrading the performance of the communication system. To alleviate the modal crosstalk problem, this study presents a blind equalization algorithm based on a back propagation (BP) neural network. Four channels of vortex light are used for multiplexing transmission, and the random phase screen method is used to simulate ocean turbulence. After the BP blind equalization algorithm is implemented, the improvement in the system bit error rate is simulated and analyzed under conditions of varying ocean turbulence intensities, transmission distances, and vortex light multiplexing modes. The simulation results show that the blind equalization algorithm using the BP neural network can effectively reduce the impact of ocean turbulence on the bit error rate of the system, and the system performance significantly improves when the multiplexing mode interval is 2.

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    Mingjun Wang, Sikai Tu. Performance Research on Underwater Vortex Optical Multiplexing System Based on Back Propagation Neural Network Blind Equalization Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(17): 1701001

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Aug. 21, 2022

    Accepted: Sep. 27, 2022

    Published Online: Aug. 29, 2023

    The Author Email: Wang Mingjun (wangmingjun@xaut.edu.cn)

    DOI:10.3788/LOP222356

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