Performance analysis of modulating retro-reflector link based on orbital angular momentum coding in underwater channels

Zhaoyuan Zhang, Xiaoli Yin*, Xiaozhou Cui, Huan Chang, Xiangjun Xin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

The modulating retro-reflector (MRR) communication is developed to solve the problem of insufficient terminal power in underwater optical communication (UOC) where size, weight and power are severely restricted. Here, we propose a new MRR link scheme using the optical orbital angular momentum (OAM) for such scenarios to realize high-order coding. Furthermore, we simulate the OAM MRR link in the oceanic turbulence channel and use the convolutional neural network (CNN) to classify OAM patterns. The simulation results show that the OAM MRR link can realize error-free image transmission under weak turbulence strength of Cn2=10−15K2m−2/3 within 15 m distance using 8/16-ary OAM superposed modes, and with the increasing of the distance and the turbulence strength, the performance is decreasing, where the 16-ary format has a higher coding efficiency but lower performance than the 8-ary format. An experiment of the OAM MRR link using CNN to classify 16-ary OAM superposed modes is also demonstrated, where two space light modulators (SLMs) are used to simulate the short-distance oceanic turbulence channel and modulate the OAM beam, respectively. Our experiment result shows the recognition rate of 16-ary OAM set with different types of channel conditions. Our work may provide a novel way for high-order coding MRR links in underwater channels and hopefully increase the data rate of the MRR link in the future.

Original languageEnglish
Article number127903
JournalOptics Communications
Volume510
DOIs
Publication statusPublished - 1 May 2022

Keywords

  • Convolutional neural networks (CNN)
  • Modulating retro-reflector (MRR)
  • Oceanic turbulence
  • Orbital angular momentum (OAM)
  • Underwater optical communications (UOC)

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