On the Noise Effect of Fingerprinting-Based Positioning Error in Underwater Visible Light Networks.

Marwan Hammouda,Anna Maria Vegni,Valeria Loscrí

doi:10.3390/s21165398

Abstract

This paper assesses the performance of a localization technique for underwater visible light networks. The proposed approach is based on a fingerprinting technique, collecting the channel impulse responses from different wireless optical signals in the visible range. A local database related to the power level distribution within a maritime environment is built and exploited to estimate user position, e.g., a diver moving in a given space for underwater fish monitoring. In this paper, we investigate on the noise effect on the localization accuracy in underwater scenarios and for different water turbidity coefficient and we demonstrate that the estimation error suffers on variable channel impulse responses. Different configuration parameters and environmental scenarios have been considered, showing that the LED transmitter deployment can be effective in the localization estimation. A comparison of the proposed localization approach to the traditional triangulation method has been finally carried out, showing the effectiveness of the fingerprinting-based solution for a lower number of LED transmitters.

Highlights

Great expectations are relied on future 6G networks for ensuring extremely high capacity and enabling new emerging applications [1]
Different scenarios have been evaluated in the case of varying (i) the number of LEDs deployed in the given environment, (ii) the additive noise and (iii) the water types
We can observe that—as expected—the Mean Square Error (MSE) is strongly affected by the number of LED transmitters covering the monitoring space, as well as by the additive white Gaussian noise (AWGN)

Summary

Introduction

Great expectations are relied on future 6G networks for ensuring extremely high capacity and enabling new emerging applications [1]. Some important challenges have to be accounted for in the underwater VLP systems such as the noise due to wave motion, sunlight rays penetrating through water and existing obstacles such as fishes that need to be assimilated to moving obstacles Another important factor to be accounted for is that the devices considered, i.e., LEDs and PhotoDiodes, are battery-powered and cannot rely on an existing infrastructure as in indoor scenarios.

System Model

Fingerprinting UVLP Algorithm

Results

Conclusions