TY - JOUR
T1 - Performance analysis of modulating retro-reflector link based on orbital angular momentum coding in underwater channels
AU - Zhang, Zhaoyuan
AU - Yin, Xiaoli
AU - Cui, Xiaozhou
AU - Chang, Huan
AU - Xin, Xiangjun
N1 - Publisher Copyright:
© 2022
PY - 2022/5/1
Y1 - 2022/5/1
N2 - 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.
AB - 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.
KW - Convolutional neural networks (CNN)
KW - Modulating retro-reflector (MRR)
KW - Oceanic turbulence
KW - Orbital angular momentum (OAM)
KW - Underwater optical communications (UOC)
UR - http://www.scopus.com/inward/record.url?scp=85123679209&partnerID=8YFLogxK
U2 - 10.1016/j.optcom.2022.127903
DO - 10.1016/j.optcom.2022.127903
M3 - Article
AN - SCOPUS:85123679209
SN - 0030-4018
VL - 510
JO - Optics Communications
JF - Optics Communications
M1 - 127903
ER -