JOURNAL ARTICLE

Statistical channel model to characterize turbulence‐induced fluctuations in the underwater wireless optical communication links

Abstract

Summary Underwater wireless optical communication (UWOC) turns out to be a primary sought alternative to wireless technology, which can provide high data rate, enormous bandwidth, and low deployment cost and resolve the crowded radio frequency band issues. The performance of the UWOC link is significantly affected by the turbulent ocean environment. In this paper, the different turbulence scenarios such as salinity gradients, temperature gradients, and air bubbles are considered to model the turbulent nature of the ocean environment experimentally. These turbulent conditions induce severe intensity fluctuations in the received optical signal. These intensity fluctuations are modeled stochastically, and a novel turbulence channel model is proposed, which excellently fitted with the experimental data. The conformity of the proposed model is validated by performing a goodness of fit test in terms of R 2 and root mean square error (RMSE) coefficients. The results show that for all the considered turbulent scenarios, the Gaussian mixture model better approximates the experimental observations than the Weibull distribution. As the strength of the turbulence increases, the chances of link outage and the probability of bit errors increase. At a flow rate of 4 L/min, the bit error rate (BER) of 10 −20 , 10 −14 , and 10 −10 is recorded under clear water, salinity, and temperature gradient channel conditions, respectively. It is observed that the air bubbles have a significant impact on the propagating optical beam in comparison to temperature and salinity‐induced turbulence. Moreover, a close agreement between the proposed model and the experimental data is also observed, which makes the proposed model more appropriate to describe the turbulent ocean environment.

Keywords:
Turbulence Bit error rate Channel (broadcasting) Physics Underwater Turbulence kinetic energy Temperature salinity diagrams Computer science Optics Acoustics Environmental science Telecommunications Meteorology Geology Salinity

Metrics

6
Cited By
0.65
FWCI (Field Weighted Citation Impact)
47
Refs
0.64
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Optical Wireless Communication Technologies
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Underwater Vehicles and Communication Systems
Physical Sciences →  Engineering →  Ocean Engineering
UAV Applications and Optimization
Physical Sciences →  Engineering →  Aerospace Engineering
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