1

Short-Range Underwater Wireless Communication

Using Visible Light LEDs

Yousuke Ito, Nonmember, Shinichiro Haruyama, Member,

and Masao Nakagawa, Fellow, IEEE

Abstract

In this paper, we propose an underwater wireless communication technique using visible light LEDs for use

in short range. As an alternative system against conventional acoustic systems, optical communication can achieve

high data rate. We analyzed the system performance by modeling the channel based on underwater optics. Through

this analysis, we show that a single color LED is very weak in a wavelength dependent underwater channel. To

overcome this problem, in this paper we propose a multi-wavelength adaptive scheme combined with rate adaptive

transmission. The proposed system can adapt to the channel by considering the change in power for each wavelength

band, and controlling the data rate. Lastly, an experiment has been conducted to confirm the analyzed results with

practical cases by measuring the received power and BER in turbid water tank. We have shown that the numerical

analyses are reliable, and the possibility of such system.

Index Terms

Underwater Communication, Visible Light Communication, LED, Rate Adaptive Transmission.

I. INTRODUCTION

NOWADAYS, acoustic technology is mostly used for establishing wireless communication link

among divers and ships, or sending long range remote signals. This is because sound waves travel

through water faster than in air, receiving very little attenuation. However, due to frequency attenuation

characteristic of acoustic waves in water, it is difficult to expand its bandwidth. Therefore, acoustic

Manuscript created March 1st, 2006

The authors are with the Faculty of Science and Technology, Keio University.

2

approach can not achieve high data rate, and also portable communication devices are difficlut to be

designed at a low cost.

However, there are demands for high quality, high speed communication links even for short distances

within few meters. For example, such demands are in high quality voice communication between divers,

and in data transmission of video streaming and sensing datas. Optical communication, which can be

operated at over hundreds of MHz, can meet these demands. Also, it is known that light in the visible

region has the least attenuation in water absorption. A voice communicating system utilizing laser beams

has been proposed[1], however it is rather unrealistic and dangerous to implent a laser in a man-use

devices underwater, as the optical axis needs to be fixed all the time. Moreover, laser diodes are an

expensive device. Therefore, in this paper, we propose a system utilizing a modulated visible light LED

(Light Emitting Diode) for underwater wireless communication. LEDs have low power consumption for

a given power input, low operating voltage, long lifetime, and low cost. Also they can be modulated at

high speed providing bit rate up to 100Mbps. Thus, by using LEDs as a modulated source, a reliable high

speed communication system can be implemented in a portable device at a very low cost.

The purpose of this paper is to understand the underwater optical channel, and its effect on the

communication link. For this analysis, we apply Mie scattering theory[2]. After gaining knowledge

of the channel, a new system is proposed to adapt to several problems among underwater wireless

optical channel. As a new system, we propose a multi-wavelength adaptive scheme combined with

rate adaptive transmission using plural LEDs. Also, an experimental investigation has been conducted

to compare the analyzed results with practical cases, in turbid water. Note that this paper is not interested

in accurately modeling the underwater optical channel, but focusing on bonding the two areas; optical

wireless communication theory and underwater optics.

II. CHANNEL MODEL

In this section, underwater wireless optical channel model based on underwater optics is presented.

3

A. Wireless Optical Channel

An equivalent model of the proposed system is presented in Figure 1.

DATA LED Lens Water Channel Lens PD DATA

Fig. 1. Model of the System

The system is a LOS (Line of Sight) model, having a LED as the transmitter and a PD (Photo Detector)

as the receiver, each equipped with a lens. We assume the noise as AWGN (Additive White Gaussian

Noise). In optical channels, the quality of transmission is typically dominated by shot noise. When little or

no ambient light is present, the dominant noise source is receiver pre-amplifier noise, which is also signal-

independent and Gaussian. Accordingly, the wireless optical channel model is expressed as follows[3]:

Y (t) = RX(t) ⊗ h(t) + N(t) (1)

where Y (t) represents the received signal current at certain time t, R represents the receiver’s optical-

electrical efficiency, X(t) represents the transmitted optical pulse, h(t) denotes the impulse response, N(t)

denotes the AWGN, and the symbol ⊗ means convolution. The average transmitted optical power Pt is

given by

Pt = limT→∞

1

2T

∫ T

−T

X(t)dt (2)

The optical power loss expressed by the channel DC gain is H(0) =∫ ∞∞ h(t)dt, where the average

received power at is Pr = H(0)Pt.

Next, for LED characteristics, the radiation pattern can be approximated as Lambertian. Let φ be the

radiation angle, then transmitted power Ptr can be written as

4

Ptr(φ) =m + 1

2πPt cosm(φ)

m = − ln2

ln cos Φ1/2

(3)

where m denotes the directivity of the radiation pattern, which is defined by the semi-angle at half

power Φ1/2 of the LED.

Now, we can define our system’s optical channel gain as

H(0) =

⎧⎪⎪⎨⎪⎪⎩

(m + 1)A

2πd2cosm(φ)Ts(ψ)g(ψ)cosψ 0 ≤ ψ ≤ Ψ

0 ψ > Ψ.

(4)

A represents the area of PD, d is the distance between the transmitter and the receiver, ψ represents

the incident angle, Ts(ψ) represents the optical filter gain, and g(ψ) represents the lens gain where Ψ is

the FOV (Field Of View). The lens gain g(ψ) can be expressed with its refractive index n as

g(ψ) =

{n2

sin ψ20 ≤ ψ ≤ Ψ (5)

B. Underwater Optics

As light travels through a medium, its intensity decreases due to absorption and scattering occurred by

water molecules and dissolved particles. If a light in wavelength λ has an intensity I(λ) and travels a

distance dr, letting dIλ be the attenuation in intensity, then Beer’s Law can be expressed as follows[1]:

dI(λ) = −K(λ)I(λ)dr (6)

Integrating distance r from 0 to d, and letting I0(λ) be the intensity of light at r = 0, Eq.6 can be

written asI(λ) = I0(λ) exp(−K(λ) · d) (7)

K(λ) is the attenuation coefficient, which in this paper defined as

K(λ) = aw(λ) + bp(λ)C (8)

5

where aw(λ) is the absorption of water, bp(λ) is the scattering of dissolved particles of light in

wavelength λ. Note that the scattering of water molecules and absorption of particles are neglected in

this paper, as they are small enough compared to the parameters in Eq.(8). Also in Eq.(8), C denotes the

particle concentration. In this paper, we define it ppm (parts per million) of the existing particles in unit

volume. We refer to this parameter as turbidity, since it determines the visiblity range in ocean water.

From past measurements, it is known that most particles dissolved in ocean water, are in the order

of μm in size. The scattering process of optical wavelengths close to this length can be completely

explained by Mie Scattering theory[2]. Therefore, for multiple scattering calculation we apply this theory.

Let us assume a particle of radius r with a refractive index of m(= mr −mii). As mentioned above, the

absorption of particles is neglected, thus mi equals 0. We define the scattering efficiency Qsca, as the ratio

of scattering cross-section and geometrical cross-section πr2. According to Mie Theory, the efficiency is

a function of the size parameter x = 2πr/λ and refractive index m of the particle. If we let the particle

size distribution be n(r)dr and assuming the radius range of r ∼ r + dr, the scattering coefficient bp(λ)

can be expressed as

bp(λ) =

∫ r2

r1

πr2Qsca(m,x)n(r)dr (9)

C. Signal to Noise Ratio

The communication quality of the system is determined by the SNR (Signal to Noise Ratio)[3].

SNR =R2P 2

r

N(10)

where N is the power spectral density of noise, which is defined by the variance as follows.

N = σ2shot + σ2

cir (11)

σ2shot denotes the shot noise in the received signal and the ambient light. In this paper we assume a

AWGN over the equivalent noise bandwidth B. B depends on the modulation scheme. Therefore, σ2shot

is expressed as

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σ2shot = 2qRPrB + 2qRPnI2B (12)

Pn = TsAn2

∫ λ1

λ2

pn(λ) exp(−Kλddep)dλ (13)

where q represents the electric charge (1.6 ×10−19), and the I2 is the noise factor due to the thermal

noise in the feedback curcuit. In Eq.13, Pn is the overall ambient light power, pn(λ) is the spectral

irradiance at sea level, ddep represents the depth from sea level. λ1 and λ2 are determined by the optical

filter bandwidth. Also in Eq.11, σ2cir represents the circuit noise, which consists of a thermal noise and the

amplifier noise. In this paper, we assume the use of a p-i-n/FET (Field Effect Transistor) transimpedance

receiver. We neglect the noise contributions from gate leakage current and 1/f noise. The circuit noise

variance is given as follows[3].

σ2cir =

4KT

G2πηAI2B

2 +4KTΓ

gm

(2πηA)2I3B3 (14)

K represents the Boltzmann’s constant, T is the absolute temperature, G is the open-loop voltage gain

of the amplifier, η is the fixed capacitance of photo detector per unit area, gm is the FET transconductance,

Γ represents the channel noise factor, and I3 represents the noise factor due to the thermal noise in the

channel of the FET[3].

Considering the wavelength dependency of the ocean water, the optical signal is attenuated differently

on each wavelength. Thus we can redefine Eq.10 as,

SNR =

{∫ λ1

λ2(R(λ)Pt(λ)H(0) exp(−K(λ)d)dλ

}2

∫ λ1

λ2N(λ)dλ

(15)

Pt(λ) and N(λ) represent the spectral distribution of the LED and received noise, respectively. From

Eq.13, the greater the depth, the greater the ambient light spectrum shows wavelength dependency.

Therefore, the overall noise can be considered as a function of wavelength.

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III. SYSTEM PERFORMANCE

Here we discuss the system performance through numerical analysis. Note that this paper does not

focus on an accurate modeling of the underwater optical channel. Therefore, we will use parameters to

define a general model to explain the further communication performance. Also, the communication model

presented in this paper can be tuned and approximated to different ocean optic models, by substituting

parameters defined in section 2.

First, we calculate the attenuation coefficient K(λ), as described in the previous chapter using Eq.(7)

through Eq.(9). We calculate the scattering parameters based on Mie theory.

The parameters are shown in Table I. For the absorption of water, aw, we use the measured data

from Pope and Fry[4]. Also we consider a log-normal particle size distribution and a uniform partical

concentration in space.

Figure 2 shows the calculation results of attenuation coefficient K(λ) for different tubidity. The K(λ)

for 0 ppm, is the measured results from Pope and Fry. We can see that the minimum attenuation is in the

range of 400[nm] to 450[nm]. But when turbidity increases, the range shifts toward longer wavelengths.

This is due to the scattering of particles, bp, in short wavelengths. Therefore, we can see that the channel

is wavelength dependent, and that turbidity changes its dependency.

Also, adjacent wavelength lightwave such as ultra-violet and infra-red, receives high attenuation in

water, that obviously it cannot be used for communication purposes. Moreover, another advantage using

visible light, besides its low attenuation characteristics, is that it can be designed to fulfill user friendly

devices. One can easily fix the optical axis within the visible light range.

A. Ambient Light

Next, we consider the ambient light noise, derived in Eq.(13). Figure 3 shows the spectral irradiance as

a function of depth and turbidity. We assume on a sunny day when the sun intensity is highest in the day,

the spectral irradiance at sea level is 2.5[W/m2/nm]. Also, we assume a spectraly uniform distribution,

8

TABLE I

PARAMTERS FOR MIE CALCULATION

Wavelength λ 400-700[nm]Refractive Index nwater 1.3

Refractive Index nparticle 1.05-1.40Particle Radius r 0.5-10.0 [μm]

Particle Size Distribution n(r) Log-NormalMean Raduis r 1.0,4.0[μm]

Standard Deviation 1.5[μm]

400 500 600 7000

0.2

0.4

0.6

0.8

1

1.2

Wavelength [nm]

Atte

nuat

ion

Coe

ffici

ent K

( )

[m ]

0.8

0.2

0.4

0.6

1.0

0

Turbidity [ppm]Turbidλ

−1

Fig. 2. Attenuation Coefficient (Mean Radius:1.0[μm])

but added wavelength dependency in depth as defined in Eq.13. From Figure 3, we can see that for pure

water, the longer wavelength has the most attenuation, therefore, the deeper into the ocean, the smaller

the irradiance. However, considering high turbidity, the shorter wavelength has an attenuation closer to

the longer wavelength. When we assume a bit rate below tens of mega bits per second, influence of

the ambient light induced shot noise becomes the dominant factor[3]. Therefore, we can say that it is

important to analyze the communication performance considering the wavelength dependency.

B. BER Performance

Here we analyze the BER (Bit Error Rate). The simulation parameters are shown in Table II. We

consider the spectral distribution of a Red, Green and Blue LED having the FWHM (Full Width at Half

Maximum) of 30[nm]. Its relative spectral distribution is shown in Figure 4. After the light from each LED

9

400 450 500 550 600 650 700

10−2

10−1

100

Irra

dian

ce [w

/m /n

m]

2

Wavelength [nm]

Depth 2.0[m] Depth 5.0[m]

(Pure water)

(Turbid Water)

0 [ppm]

1.0 [ppm]

Fig. 3. Spectral Irradiance at different Depth and Turbidity (Mean Radius:1.0[μm])

is transmitted and passes through the optical band pass filter at the receiver, it is converted to an electrical

signal by the PD. In this paper, we assume the spectral response of the detector to be constant. For

modulation scheme, we apply L-PPM (L-ary Pulse Position Modulation)[3]. L-PPM is a power efficient

modulation scheme, where L stands for the number of slots in a symbol. In this section, we fix L to 2.

In order to calculate the BER for a given transmit power, we first calculate the received power Pr. The

results are shown in Figure 5 and Figure 6. Figure 5 shows the received power vs propagation distance in

two types of water. Comparing the differnce in a 0[ppm] and 1.0[ppm] water, we can see that the received

power has 20dB difference at 5.0[m] distance. Also, the difference in each LED can be seen, as 450[nm]

LED is least attenuated in 0[ppm] water, but in 1.0[ppm] water, 550[nm] LED is least attenuated.

Figure 6 shows the received power when turbidity varies at a fixed distance of 1[m]. We can see that

the received power decreases as the turbidity increases. Also this figure shows that, in low turbid waters,

light from the Blue LED is the least attenuated, but in high turbid water, it is the most attenuated due to

particle scattering.

Next, we conduct a computer simulation to derive the BER performance, based on the SNR defined in

Eq.(15). Figure 7 shows the BER in function of turbidity for distances at 3.0[m] and 5.0[m]. The depth

is 1.0[m]. The figure shows the BER for each LED considered. Due to both signal and noise level shifts,

10

TABLE II

SIMULATION PARAMETERS.

Transmit Optical Power Pt 100[mW ]Semi-angle at Half Power Φ1/2 30[deg.]

FWHM of each LED 30[nm]Peak Wavelength of LED 450,550,650[nm]

FOV Ψ 40[deg.]Optical Filter Bandwidth Δλ 80[nm]

Optical Filter Gain Ts 1.0Detector Responsivity R 0.3[A/W ]

Detector Area A 1.0[cm2]Refractive Index of Lens n 1.5

Open loop Voltage Gain G 10Fixed Capacitance η 112[pF/cm2]FET Noise Factor Γ 1.5

Conductance gm 30[ms]Absolute Temperature T 256 [K]

Noise Factor I2, I3 0.562,0.0868

Spectral Irradiance at Sea Level Pn 2.5[W/m2/nm]Modulation Scheme L-PPM

Slot Duration T 0.5[μsec]

400 500 600 700

0

0.2

0.4

0.6

0.8

1

Wavelength [nm]

Rel

ativ

e S

pect

ral P

ower

Optical filter

Blue RedGreen

Fig. 4. Relative Spectral Distribution of LED Pt(λ)

the performance of each LED differs when the turbidity varies. For high turbidity, Red LED outperforms

the Blue LED. However, for longer distance, as water absorption becomes dominant, the Red LED has

the worst performance. Figure 8 shows the BER under the same condition as Figure 7, but the depth is

fixed at 2.0[m]. We can see that the difference in BER compared to Figure 7, is occured from the change

in ambient noise level.

11

1 2 3 4 5−60

−50

−40

−30

−20

Distance [m]

Rec

eive

d O

ptic

al P

ower

[dB

m]

450 [nm]550 [nm]650 [nm]

0 [ppm]

1.0 [ppm]

Fig. 5. Received Optical Power[dBm] vs Propagation Distance[m]

0 0.5 1 1.5 2

−28

−26

−24

−22

−20

Turbidity [ppm]

Rec

eive

d O

ptic

al P

ower

[dB

m] 450 [nm]

550 [nm]650 [nm]

Fig. 6. Received Optical Power[dBm] vs Turbidity[ppm]

Figure 9 shows the BER vs operating data rate, in two types of water. Comparing the performance in

0[ppm] and 1.0[ppm] water, at the BER of 10−6, the operating data rate for Blue and Green LEDs drops

to 1/10, while Red drops to 1/3. We can see that the the Red LED has the least influence of turbidity.

From all considered datas, we can see that the LED with the best performance varies when we define

different communication conditions, such as the turbidity of water, communication distance, and depth.

Therefore, we calculated the most efficient LED for each of these conditions shown in Figure 10. The

left figure is at a communication distance of 1.0[m], and the right figure, 5.0[m]. The vertical axis and

horizontal axis represent the depth and turbidity, respectively. From this figure, we can see that one type of

12

0 0.5 1 1.5 210−6

10−5

10−4

10−3

10−2

10−1

100

5.0[m]

3.0[m]

450 [nm]550 [nm]450 [nm]550 [nm]650 [nm]

Bit

Err

or R

ate

Turbidity [ppm]

Fig. 7. BER vs Turbidity (Depth:1.0[m])

0 0.5 1 1.5 210−6

10−5

10−4

10−3

10−2

10−1

100

5.0[m]

3.0[m]

450 [nm]550 [nm]450 [nm]550 [nm]650 [nm]

Bit

Err

or R

ate

Turbidity [ppm]

Fig. 8. BER vs Turbidity (Depth:2.0[m]

LED cannot fulfill overall communciation conditions. For example, a Red LED is most likely to be used

for diver communication. On the other hand, for long distances such as monitoring or data communication,

the Blue LED or Green LED is the preffered. Note that considering the spectral response distribution of

the detector, the optimal choice of LED may vary.

IV. ADAPTIVE SCHEME UTILIZING PLURAL LEDS

In this section, we propose an adaptive scheme for underwater communication. In the previous section,

we showed that the system depends on the underwater optical channel which is severely affected by

13

106 107 10810−6

10−5

10−4

10−3

10−2

10−1

100

Bit

Err

or R

ate

Data Rate [bps]

450[nm]550[nm]650[nm]

(Turbid)

(Pure Water)0 [ppm]

1.0 [ppm]

Power:100[mW]Depth:1.0[m]Distance:3.0[m]

Fig. 9. Bit Error Rate vs Data Rate [bps]

0 0.4 0.8 1.2 1.6 2.0 0 0.4 0.8 1.2 1.65.0

2.0

1.0

2.0

3.0

4.0

450[nm]

550[nm]

650[nm]

Dep

th [m

]

Turbidity [ppm]

Fig. 10. Optimum LED for various conditions (Left : Distance = 1.0[m], Right : Distance = 5.0[m])

turbidity. In underwater communication, these conditions are rather time varying resulting from change in

tide, wind and water velocity. However in communication enginnering, link stability is mostly important,

rather than achieving the highest data rate possible. Therefore, an adaptive scheme to overcome these

time-varying conditions is essential.

In this paper, we propose a system utilizing a multi-wavelength adaptive scheme combined with rate

adaptive transmission. For rate adaptation, we apply adative modulation using RCPC (Rate Compatible

Punctured Convolutional) encoded L-PPM[5]. In other words, here we add RCPC coding to L-PPM.

The system figure is shown in Figure 11. As shown in Figure 11, we propose two systems, one is the

wavelength selection scheme in which the most efficient LED is chosen. The latter is the multiplexing

14

R

G

B

Encoding &Modulation

Data

RateSelection

PD

PD

PD

ChannelEstimation

Demodulation& Decoding

Data

TransmitterTransmitter ReceiverReceiverChannelChannel

LED

LED

LED

R

G

B

Encoding &Modulation

Data

RateSelection

PD

PD

PD

ChannelEstimation

Data

TransmitterTransmitter ReceiverReceiverChannelChannel

Encoding &Modulation

Encoding &Modulation

S/P

Decoding &DeModulation

Decoding &DeModulation

Decoding &DeModulation

P/S

LED

LED

LED

Fig. 11. Diagram of Proposed Scheme (Upper:Proposed1, Lower:Proposed2)

scheme which transmits distinct data on each wavelength. We referr the former as Proposed 1, and the

latter as Proposed 2. Note that the two propsosed schemes are not for the same purpose. Proposed 1

expands the communication range at lower rate. On the other hand Proposed 2 challenges a high speed

communication.

A. Wavelength Selection & Multiplexing

For both proposed systems, the transmitter is installed with three wavelength LEDs: Red, Green, and

Blue. The receiver holds R, G, B optical filters each followed by three detectors having the highest response

in each wavelength region. Proposed 1 system selects the most power efficient LED to modulate, based

on the received SNR. This can simply be done by sampling the DC gain of a signal level and noise level.

The receiver feeds back the information of the selected LED to the transmitter.

On the other hand, Proposed 2 system holds an encoder and a modulator connected to each LED. By

multiplexing different data on each wavelength, a higher data rate than one LED can operate, can be

acheived with the same transmit power. Different from the system in Proposed 1, in Proposed 2, the SNR

at each PD is measured at the receiver. With feed back of the information including the three SNRs,

power control is done to keep the LEDs operating over equal SNR.

15

ConvolutionalEncoder

PunctureL-PPM

Modulation

Data

Channel

L-PPMDemodulation

ViterbiDecoder

Data

Data

Control

RCPC Encoder

RateSelection

Fig. 12. Diagram of Rate Adaptive Scheme

B. Adaptive Coding & Modulation

The proposed systems employ a rate adaptive scheme to ensure a certain level of communication quality

with adaptive coding and modulation. This is done by controlling the data rate according to the channel

condition. As the time variation in channel condition is much longer than the symbol duration, it need

not necessarily to perform a high speed adaptive control. The diagram of rate adaptive system control is

shown in Figure 12.

The data rate for this system is expressed as[5]

Rb =rlog2L

LT(16)

where r is the code rate, T is the slot duration, and L is the PPM modulation level, which is the

number of slots in one symbol. When we consider constant average transmit power with fixed PPM slot

duration, increase in L leads to increse in pulse levels. Therefore, we can gain more power for each

pulse slot, consequently achieving a higher performance for each modulated symbol. On the other hand,

from Eq.16, we can see that higher L leads to low data rate. Therefore, by only controlling L, we can

acheive a constant performance even at low SNR, sacrificing data rate. Also, RCPC encoder is applied

in the proposed system[6,7]. A family of RCPC codes can be obtained from a single mother code by

periodically puncturing some output digits. At the receiver, a Viterbi Decoder decodes the demodulated

bits. Thus, the advantage of applying RCPC is that we can achieve various data rates with a single

16

convolutional encoder. However, the Viterbi Algorithm is performed exactly as standard convolutional

codes, except that the decoder must know the puncturing matrix currently in use and should not update

branch metrics at positions occupied by punctured digits. Thus dummy bits are inserted according to the

puncturing matrix to generate the sequence before puncturing.

By punctering m bits along a 2× l puncturing matrix, the upper bound of the bit error probability with

a code rate R = l/(2l − m) is given by,

Pb ≤ 1

l

∞∑d=dfree

CdPd (17)

Pd = Q

(√dL log2 L

2

√SNR

)(18)

Here dfree is the free distance of the code and Cd is the total number of bit errors in all adversaries

having a Hamming weight d. Pd is the probability that an incorrect path with a Hamming weight d is

selected in the Viterbi decoding process. In this paper, we assume a rate 1/2 convolutional encoder with

a constraint length of 6.

C. Simulation Results

Computer simulation is conducted for L level=(2,4,8,16), and code rate at r=(1/2,2/3,3/4,4/5,5/6,6/7,7/8,uncoded).

We compare the proposed adaptive system with a single wavelength LED as in chapter 3, operating at

a fixed data rate (L=2, r=1/2). In figure 13, the maxmimum data rate achieving BER below 10−6 is

shown. For the proposed scheme, the code rate and modulation level for L-PPM is selected considering

the channel condition. We can see that with use of a single LED, enough quality at high turbidity can not

be obtained. On the other hand, the proposed scheme can adapt to the turbidity changes. For Proposed 1,

the communication link is stable even in high turbidity by controlling the data rate.

For Proposed 2, the transmit power for each LED is reduced; hence the SNR is low compared to the

other schemes. But when the water has low turbidity, multiplexing can be done due to high SNR. Figure

17

0 0.5 1 1.5 2 2.5 30

0.5

1

1.5

2

2.5

3

3.5

Max

imum

Dat

arat

e [M

bps]

[BE

R<

10 ]

450 [nm]550 [nm]650 [nm]

Proposed 1Proposed 2

−6

Turbidity [ppm] Turbid

Data Rate Fixed

Proposed 1

Wavelength &

Proposed 2w 450 [nm]

Proposed 2

Proposed 1Fixed Wavelength

Fig. 13. Maximum Data Rate Achieving BER< 10−6 for Different Turbidity

14 shows the coverage of overall underwater communicating conditions. Coverage rate is defined as the

ratio of times when BER is below 10−6 over all considered conditions. We analyzed the coverage rate for

depth range of 1.0-5.0[m], and turbidity range of 0[ppm]-2.0[ppm], at each communication distance. We

can see that the single LED cannot achieve enough coverage. This means that the communication link

will be affected severely by a change in turbidity or depth. The Proposed 1 can achieve about 20% more

coverage than schemes with single LED of fixed data rate. Also, in Figures 13 and 14, performance of

Proposed 1 without wavelength selection is shown. The wavelength is fixed to 450 [nm]. We can see the

improvement in wavelength selection.

In Figure 15, the effect in particle size distribution for Proposed 1 scheme is shown. This simulation is

to recognize the effect of the wavelength selecting scheme, under different types of ocean water in terms

of particle distribution. When we shift the mean radius in the particle size distribution, the channel shows

a different wavelength dependency. We can see that a system utilizing only a single LED can not tolerate

the channel change in neither turbidity nor in particle distribution. In an underwater environment, particle

distribution varies in time. Therefore the proposed system can cover much of the channel wavelength

variations.

18

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

Cov

erag

e [B

ER

<10

]

Distance [m]

450 [nm]550 [nm]650 [nm]Proposed 1

Proposed 2−6

Depth : 1.0−5.0[m]Turbidity : 0−2.0[ppm]

Wavelength &Data Rate Fixed

ProposedScheme

Proposed 1w 450[nm]

Wavelength Fixed

Fig. 14. Coverage Rate at Different Distance

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

Cov

erag

e [B

ER

<10

]

Distance [m]

450 [nm]Proposed 1

−6

Depth : 1.0−5.0[m]Turbidity : 0−2.0[ppm]Fixed Data Rate

Mean Diameter : 4.0 [ m]

Mean Diameter :1.0 [ m]

μ

μ

Fig. 15. Effect of Particle Distruibution to Coverage

V. EXPERIMENTAL INVESTIGATION

In this section, we show the results of experimental investigation to confirm the possibility of the

proposed system. This approach ensures the reliability of the previous analyses which is based on

scatttering theory.

19

A. Creating a Turbid Water

In this measurement, we used a 1 × 0.5[m] acrylic water tank in order to conduct an experiment. To

investigate an general communication model, which is the main focus in this paper, experiment using an

real ocean water is not an realistic approach. The reason is that it cannot be reproduced in further research,

and the most important approach is to bind the two areas; optical communication engineering and ocean

optics. Therefore, we added scatterers to create artificial turbid water. One method used to test turbidity

relies on adding colloidal silica(SiO2). The colloidal exhibits a broad variance in sphere diameter, which

scatters all wavelength, and therefore turns clear water into white. As the colloidal contains broad range

in scatter diameter, we can approximate the ocean water consisted of various type of particles in order of

μm. For measuring the turbidity, we used a turbidimeter that measures in NTU (Nephelometric Turbidity

Unit). The experimental parameters are shown in Table III and IV.

B. Experimental Results

We measured the received optical power using an optical power meter. The results are shown in Figure

17. The line shows the calculated results using parameters of Silica Collidal. The refractive index and

density of the Silica Colloid are, 1.46 and 2.2[g/cm3]. The horizontal axis shows the measured NTU and

the calculated ppm values obtained for Silica. The plotted are normalized measured optical power. Figure

17 shows that greater turbitity results in less received optical power. Also, from the previous theoretical

analysis, we know that shorter wavelengths in the visible light region penetrate water farther than longer

wavelengths in pure water, but has the the most attenuation in when particles are present. We can see this

phenomenon from the results in Figure 17, as the Red LED has over 10dB advantage when compared to

a Blue LED when the water is more turbid. Also, we can see that the measured plots and the theoretical

lines are very close. The difference between the three LEDs is also approximated to the theory. We can

say that the numerical analyses approximates the practical values.

Next, BER was measured by actually modulating pulse position modulated signals on LEDs. The

20

TABLE III

EXPERIMENTAL PARAMETERS

Modulation 2,4-PPMData Rate 100[kbps]

Number of Bits 16000[Bits]Sampling rate 50[MSample/sec]

Electrical Filter 10[MHz] LPFEnvironment Dark

Turbidity 0 - 20[NTU ]

TABLE IV

DEVICE SPECIFICATION

LED Luxeon LXHLMB1B,MM1B,MD1BHalf-power Angle 5[deg.]

Detector Hamamatsu Si PIN-PD S3071Responsivity 320-1060[nm]

Peak Responsivity 920[nm]Detetor Area 5.0[mm]

Cut Off Frequency 40[MHz]

Fig. 16. Experiment Setup (Photo taken at Keio University)

modulated signals travel through the water tank and the PD at the receiver measures the electrical level.

The measured datas are saved in the oscilloscope to demodulate the sampled signals afterwards. Also in

this experiment, we used a 10[MHz] Low Pass Filter to cut off noise. Figure 16 shows the experimental

setup. However, the experiment was conducted in a dark environment shutting out all the ambient noise,

in order to analayze the relationship between turbidity and BER.

The attenuation of the transmitted power due to turbidity leads to low-SNR, consequently resulting in a

high error probability. We conducted an experiment by generating L = 2, and L = 4 L-PPM signals. Both

21

0 2 4 6 8 10 12−15

−10

−5

0

Turbidity [NTU]

Rel

ativ

e R

evie

d O

ptic

al P

ower

[dB

]

450 [nm]530 [nm]650 [nm]

0 1.51.00.5Turbidity [ppm]

Simulated

450 [nm]530 [nm]650 [nm]

Measured

Fig. 17. Normalized Recieved Optical Power vs Turbidity (Simulated and Measured Results)

average transmit power are constant. The results are shown in Figure 18. From Figure 18, we can see that

the BER is affected by turbidity, due to loss in received power. The difference in received power dominates

the overall performance of each LED. From this figure, we can see that the red LED outperforms the

others. Note that, these results contain the spectral responsivity of the selected p-i-n photodiode, which

was simulated as constant in the previous numerical analyses.

In the previous chapter, we have shown that by changing the L factor for L-PPM , gain is achieved

under constant transmit power. From the results, we can see that the 4-PPM modulation outperforms the

2-PPM. The reason for the small difference between modulation scheme for the Blue LED, is that the

turbidity reduced the SNR to a small length value that in this scheme to be advantageous. Also in this

experiment we can only send certain number of information bits due to limitations of the oscilloscope

used in the experiment. Therefore, the reliable BER for this experiment can be observed for BER over

10−4.

From this experimental approach, we have confirmed that the system can operate at 100[kbps] at 1[m]

with small power consumption by controlling L and selecting the proper color LED at considered turbidity.

22

0 5 10 15

10−4

10−3

10−2

10−1

100

Turbidity [NTU]

Bit

Err

or R

ate

450[nm]530[nm]650[nm]

0 2.01.0 1.50.5

2−PPM4−PPM

Turbidity [ppm]

Fig. 18. BER vs Turbidity for 2-PPM and 4-PPM (Measured Values)

Therefore, by designing the communication link with certain power, and directivity with a proper lens,

the proposed system can be implemented to a portable device at low cost.

VI. CONCLUSION

In this paper, we proposed an underwater wireless communication using visible-light LEDs for use

within short range. We first explained the theoretical background of underwater optics, and its relation

with communication performance. With Mie scattering calculation, we showed that the underwater optical

channel is wavelength dependent. Also, we have shown that the BER performance varies with parameters

such as turbidity, depth, and distance. Therefore, we proposed a multi-wavelength adaptive scheme

combined with rate adaptive transmission. We have shown that the proposed system can tolerate particle

distribution and turbidity changes. Also, this system can adapt to various communciation environments,

maximizing the throughput at every considered point. Lastly we conducted an experiment in order to match

the analyzed results with practical cases. With this approach, we understood the reliability of the numerical

analyses. From overall consideration, we showed visible-light LEDs can be used for communicating

23

underwater, providing high reliable link within short range, up to 5 to 10 meters.

REFERENCES

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vol25,no.4, Feb.1986.

[3] J.M.Kahn,J.R.Barry, ”Wireless Infrared Communications”, Proc.of the IEEE, pp.265-298, vol.85,no.2,February 1997.

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8723,vol.36, no.33, November 1997.

[5] M.Matsuo,T.Ohtsuki,I.Sasase, ”Rate-Adaptive Indoor Infrared Wireless Communication Systems Using Punctured Convolutional Codes

and Adaptive PPM”, IEEE., pp.693-697, 1998.

[6] J.Hagenauer, ”Rate-Compatible Punctured Convolutional Codes (RCPC Codes) and their Applications”,IEEE Trans.on Commun.,pp.389-

400, April 1988.

[7] D.Haccoun,G.Begin, ”High-Rate Punctured Convolutional Codes for Viterbi and Sequential Decoding”, IEEE Trans.on Commun.,

pp.1113-1125, vol.37,no.11, November.1989

[8] M.Stojanovic, ”Underwater Acoustic Communications”, IEEE Electro International, pp.435-440, Boston 1995.

[9] Y.Tanaka,T.Komine,S.Haruyama,M.Nakagawa, ”Indoor Visible Light Transmission System Utilizing White LED Lights”, IEICE

Trans.on Comm.,pp.2440-2454,vol.E86-B,August 2003.

[10] R.C.Smith,K.S.Baker, ”Optical Properties of the Clearest Natural Waters (200-800nm) ”, Applied Optics,pp177-184,vol.20,no.2, January

1981.

[11] M.Schroeder,H.Barth,R.Reuter: ”Effect of inelastic scattering on underwater daylight in the ocean: model evaluation, validation, and

first results” , Applied Optics, pp.4244-4260, vol.42, no.21, July 2003.

[12] D.J.Bogucki,J.A.Domaradzki,D.Stramski,J.R.Zaneveld, ”Comparision of near-forward light scattering on oceanic turbulence and

particles”, Applied Optics,pp.4669-4677, vol.37, no.21,July 1998.

[13] F.R.Geller,U.Bapst, ”Wireless in-house Data Communication via Diffuse Infrared Radiation”,Proc.IEEE, vol.67,no.11,pp.1474-

1486,1979.

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vol.1, June 1999.

24

PLACE

PHOTO

HERE

Yousuke Ito received B.E. degree in Information and Science Technology from Keio University, Yokohama, Japan

in 2004. She is currently enrolled in Graduate School of Science and Technology, School of Science for Open and

Enviromental Systems, majoring in Information, Communication and Media Technologies in Keio University. He is

expected to finish his M.E. degree in March 2006.

PLACE

PHOTO

HERE

Shinichiro Haruyama is a professor at Department of Information and Computer Science, Faculty of Science

and Technology, Keio University,Yokohama, Japan. He received an M.S. in engineering science from University of

California at Berkeley in 1983 and a Ph.D. in computer science from the University of Texas at Austin in 1990. He

worked for Bell Laboratories of AT&T and Lucent Technologies, U.S.A from 1991 to 1996, and for Sony Computer

Science Laboratories, Inc. from 1998 to 2002. His research interests include reconfigurable system, system design

automation, wireless communication, and visible light communication.

PLACE

PHOTO

HERE

Masao Nakagawa was born in Tokyo, Japan in 1946. He received the B.E., M.E. and Ph.D. degrees in Electrical

Engineering from Keio Universty, Yokohama, Japan in 1969,1971 and 1974, respectively. Since 1973, he has been

with the Department of Electrical Engineering, Keio University, where now he is Professor. His research interests are

in CDMA, OFDM, Consumer Communications, Mobile Communications, ITS (Intelligent Transportation Systems),

Wireless Home Networks and Visible Optical Communication. He received 1989 IEEE Consumer Electronics Society

Paper Award, 1999-Fall Best Paper Award in IEEE VTC, IEICE Achievement Award in 2000, IEICE Fellow Award in 2001 and Ericson

Telecommunications Award 2004. He was elected an IEICE Fellow in 2001 and an IEEE Fellow in 2006. He is a co-author of “TDD-CDMA

for Wireless Communication” published by Artech House in which the main ideas of TDD-CDMA were given by him. He was the Executive

Committee Chairman of International Symposium on Spread Spectrum Techniques and Applications in 1992 and the Technical Program

Commitee Chairman of ISITA (International Symposium of Information Theory and its Application) in 1994. He is an editor of Wireless

Personal Communications and was a guest editor of the special issues on “CDMA Networks, 1,2,3 and 4” published in IEEE JSAC in

1994(1 and 2) and 1996(3 and 4). He chairs the Wireless Home Link sub-commitee in MMAC(Multimedia Mobile Access Communication

Promotion Commitee). He was a general chair of WPMC (International Symposium on Wireless Personal Multimedia Communications) in

2003.