Statistical channel model for underwater wireless optical communication system under a wide range of air bubble populations

Singh, Mandeep; Singh, Maninder; Singh, Gurdial; Gill, Harpuneet Singh

doi:10.1117/1.oe.60.3.036111

Cited by 11 publications

(8 citation statements)

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“…Temperature disparities can cause beam wandering, which alters the strict alignment of the transmitter and the receiver [5]- [7]. Further, air bubbles deteriorate the quality of the received signal by increasing the prevalence of scattering events and blockages [8]- [10].…”

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confidence: 99%

The Impact of Vertical Salinity Gradient on Non-Line-of-Sight Underwater Optical Wireless Communication

et al. 2021

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The non-line-of-sight (NLOS) underwater communication can offer a viable route in signal propagation and coverage, thus mitigating the pointing, acquisition, and tracking difficulties in line-of-sight optical communication. However, implementing the NLOS link is non-trivial. While the NLOS technique relies on light scattering, i.e., channel turbulence can facilitate NLOS communication, the associated path-loss (PL) can be significant. Signal fading can degrade link robustness, which arises due to ocean water temperature and salinity fluctuation and gradients. To evaluate the robustness of NLOS in natural waters, we systematically measure the link metrics, such as the bit error ratio, PL, and signal-to-noise ratio (SNR), of water bodies of uniform and nonuniform vertical salinity ranging from 30-40‰ (part-per-thousand). We found that salinity-induced turbulence can establish NLOS communication with PL reduction of 2.35 dB/m and SNR increase by 32.5% for dynamic water. Furthermore, a strong correlation was obtained between the strength of signal fluctuations and the received SNR. Finally, we obtained a Gaussian distribution of the statistical scintillation behavior. These results demonstrated the benefit of using the NLOS regime for underwater wireless sensor networks for aiding designers and engineers.

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confidence: 99%

The Impact of Vertical Salinity Gradient on Non-Line-of-Sight Underwater Optical Wireless Communication

et al. 2021

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“…Theoretically, the outage probability is calculated from the cumulative distribution function (CDF) of the received signal at a specific threshold. It can be expressed as 42

P_{italicout_italicThe} goodbreak= P_{r} ([], f ((), I) < y_{th} ((), I)) goodbreak= F_{y} ((), y_{th} ((), I))

where

y ((), I)

is the received optical signal. Moreover, the experimental outage probability is estimated from the received observations using the relation:

P_{italicout_italicExp} goodbreak= \frac{italicno . italicof times italicSNR italicfalls below given threshold}{italicTotal italicno . italicof observations}

…”

Section: Resultsmentioning

confidence: 99%

“…GD is a univariate distribution that is used to represent real‐valued random variables. The PDF of the GD can be expressed as 42

f ((), I) goodbreak= a_{1} italicexp ({}, goodbreak- {(\frac{I - b_{1}}{c_{1}})}^{2})

where

I

represents the received intensity fluctuations,

a_{1} = 1 / \sqrt{c_{1} π}

represents the amplitude,

b_{1} = μ_{1}

represents the mean, and

c_{1} = σ_{1} \sqrt{2}

represents the variance of the GD. The scintillation index can be expressed as

σ_{I italicGaussian}^{2} goodbreak= \frac{{c_{1}}^{2} / 2 + {(b_{1} - μ)}^{2}}{μ} bold-italicgoodbreak- 1

where

μ = \int I . f ((), I) italicdI

is the mean of the standard Gaussian function.…”

Section: Modeling Of Turbulent Water Channelmentioning

confidence: 99%

“…In this paper, the mixture of three Gaussian functions is considered. GMM is the weighted sum of the three Gaussian functions, and its PDF can be given as 42

f ((), I) goodbreak= a_{1} italicexp ({}, goodbreak- {(\frac{I - b_{1}}{c_{1}})}^{2}) goodbreak+ a_{2} italicexp ({}, goodbreak- {(\frac{I - b_{2}}{c_{2}})}^{2}) goodbreak+ a_{3} italicexp ({}, goodbreak- {(\frac{I - b_{3}}{c_{3}})}^{2})

where

a_{1}

a_{2}

a_{3}

are the amplitudes,

b_{1}

b_{2}

b_{3}

are the location parameters, and

c_{1}

c_{2}

c_{3}

are the scale parameters of each Gaussian function. The generalized form of Equation () can be given as

f ((), I) goodbreak= \sum_{i = 1}^{3} W_{i} G_{i} (();,, I, μ_{i}, σ_{i})

where

G_{i}

denotes the i th Gaussian function;

W_{1} = a_{1} \sqrt{c_{1} π}

W_{2} = a_{2} \sqrt{c_{2} π}

, and

W_{3} = a_{3} \sqrt{c_{3} π}

signify the corresponding w...…”

Section: Modeling Of Turbulent Water Channelmentioning

confidence: 99%

“…To quantify the effect of turbulence in the UWOC system, some researchers have employed the Free Space Optics (FSO) channel model, that is, lognormal distribution 47,48 . Recent experimental observations expose that the turbulent ocean environment acts contrarily from the atmospheric channel 42,45,49,50 . The fluctuations in refractive index caused by temperature, salinity, air bubbles, or other dissolved organic and inorganic matter present in the ocean differ from those caused by temperature and pressure in the air.…”

Section: Introductionmentioning

confidence: 99%

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

Singh

Kaur

et al. 2022

Int J Communication

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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 R2 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.

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Investigations on spectrum sliced-wavelength-division multiplexed visible light communication transmission for underwater links under varying turbulent conditions

Janarthanan

Gauni

2022

Opt Quant Electron

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Statistical channel model for underwater wireless optical communication system under a wide range of air bubble populations

Cited by 11 publications

References 0 publications

The Impact of Vertical Salinity Gradient on Non-Line-of-Sight Underwater Optical Wireless Communication

The Impact of Vertical Salinity Gradient on Non-Line-of-Sight Underwater Optical Wireless Communication

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

Investigations on spectrum sliced-wavelength-division multiplexed visible light communication transmission for underwater links under varying turbulent conditions

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