Bubbles-induced turbulence channel prediction mechanism based on machine vision in underwater wireless optical communication

Abstract

Bubbles-induced turbulence poses a significant challenge to the stability of underwater wireless optical communication (UWOC) system. Existing methods for understanding channel characteristics rely on the pilot information from the feed-back channel, which are ineffective and inaccurate due to the rapidly changing nature of the underwater channel. We propose a machine-vision-based channel prediction mechanism which contains three modules of motion judgment module, image processing module and scintillation index (SI) prediction module. The mechanism captures images of bubbles and calculates the bubble density. Subsequently, a relational function is applied to acquire the predicted SI which quantifies the impacts of bubbles on the channel. Experimental results validate the effectiveness of the proposed mechanism.