Machine vision-based intelligent turbulence perception for underwater wireless optical communication

Turbulence induced by thermohaline gradient and air bubbles poses a significant challenge to the robustness of underwater wireless optical communication (UWOC) systems.It is imperative to accurately measure the turbulence intensity of the channel to guide the design of adaptive UWOC systems.However, current measurements based on pilot information consume additional spectral resources.We propose a machine vision-based intelligent turbulence perception (MV-ITP) mechanism to measure the turbulence intensity of the underwater channel.The MV-ITP mechanism utilizes the spatiotemporal intrinsic coupling correlation between optical imaging and optical communication to establish a precise quantitative relationship between the pixel intensity variation of the beam images and the scintillation index.We conduct experiments under different turbulence conditions induced by temperature, salinity, as well as air bubbles, and the experimental results demonstrate that the proposed mechanism can accurately measure the intensity of turbulence.