Short Range Underwater Communication Using Visible Led
Short-Range Underwater Wireless Communication
Using Visible Light LEDs
Yousuke Ito, Nonmember, Shinichiro Haruyama, Member,
and Masao Nakagawa, Fellow, IEEE
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.
Underwater Communication, Visible Light Communication, LED, Rate Adaptive Transmission.
I. I NTRODUCTION
OWADAYS, 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.
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, 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. 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. C HANNEL M ODEL
In this section, underwater wireless optical channel model based on underwater optics is presented.
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:
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
Pt = lim X(t)dt (2)
T →∞ 2T −T
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
Ptr (φ) = Pt cosm (φ)
m = − (3)
ln cos Φ1/2
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
⎪ (m + 1)A
⎨ cosm (φ)Ts (ψ)g(ψ)cosψ 0 ≤ ψ ≤ Ψ
H(0) = 2πd2 (4)
⎩ 0 ψ > Ψ.
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) = 0≤ψ≤Ψ (5)
sin ψ 2
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:
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
I(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)
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. Therefore, for multiple scattering calculation we apply this theory.
Let us assume a particle of radius r with a refractive index of m(= mr − mi i). 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) = πr2 Qsca (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).
SN R = (10)
where N is the power spectral density of noise, which is deﬁned by the variance as follows.
N = σshot + σcir (11)
σshot 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, σshot
is expressed as
σshot = 2qRPr B + 2qRPn I2 B (12)
Pn = Ts An2 pn (λ) exp(−Kλ ddep )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, σcir 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.
2 4KT 4KT Γ
σcir = 2πηAI2 B 2 + (2πηA)2 I3 B 3 (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.
Considering the wavelength dependency of the ocean water, the optical signal is attenuated differently
on each wavelength. Thus we can redeﬁne Eq.10 as,
(R(λ)Pt (λ)H(0) exp(−K(λ)d)dλ
SN R = λ1
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.
III. S YSTEM P ERFORMANCE
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. 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,
PARAMTERS FOR M IE C ALCULATION
Wavelength λ 400-700[nm]
Refractive Index nwater 1.3
Refractive Index nparticle 1.05-1.40
Particle Radius r 0.5-10.0 [μm]
Particle Size Distribution n(r) Log-Normal
Mean Raduis r 1.0,4.0[μm]
Standard Deviation 1.5[μm]
Attenuation Coefficient K(λ) [m−1]
400 500 600 700
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. 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
0 0 [ppm]
Irradiance [w/m /nm]
−1 (Turbid Water)
400 450 500 550 600 650 700
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). 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
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,
S IMULATION 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 1.0
Detector Responsivity R 0.3[A/W ]
Detector Area A 1.0[cm2 ]
Refractive Index of Lens n 1.5
Open loop Voltage Gain G 10
Fixed 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 P n 2.5[W/m2 /nm]
Modulation Scheme L-PPM
Slot Duration T 0.5[μsec]
Relative Spectral Power
Blue Green Red
400 500 600 700
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.
0 [ppm] 550 [nm]
Received Optical Power [dBm]
1 2 3 4 5
Fig. 5. Received Optical Power[dBm] vs Propagation Distance[m]
Received Optical Power [dBm]
0 0.5 1 1.5 2
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
Bit Error Rate
10 450 [nm]
0 0.5 1 1.5 2
Fig. 7. BER vs Turbidity (Depth:1.0[m])
Bit Error Rate
−5 450 [nm]
10 550 [nm]
0 0.5 1 1.5 2
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. A DAPTIVE S CHEME U TILIZING P LURAL LED S
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
Bit Error Rate
10 0 [ppm]
10 6 7 8
10 10 10
Data Rate [bps]
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 1.6 2.0
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. 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
Transmitter Channel Receiver
LED R PD
Encoding & Demodulation
Data LED G PD Data
Modulation & Decoding
LED B PD
Transmitter Channel Receiver
Encoding & Decoding &
Modulation LED R PD DeModulation
Encoding & Decoding &
Data Modulation LED G PD DeModulation Data
Encoding & Decoding &
LED B PD
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
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.
Data Convolutional L-PPM
Data Viterbi L-PPM
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
Rb = (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
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 ≤ Cd Pd (17)
dL log2 L √
Pd = Q SN R (18)
Here df ree 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
3.5 450 [nm]
Maximum Datarate [Mbps] [BER<10 ]
3 650 [nm]
2.5 Proposed 2
w 450 [nm]
1.5 Proposed 1
1 Fixed Wavelength
Data Rate Fixed
0 0.5 1 1.5 2 2.5 3
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
1 650 [nm]
0.8 w 450[nm]
Coverage [BER<10 ]
Depth : 1.0−5.0[m]
Turbidity : 0−2.0[ppm]
Data Rate Fixed
1 2 3 4 5 6 7 8 9 10
Fig. 14. Coverage Rate at Different Distance
Mean Diameter :1.0 [μ m]
Coverage [BER<10 ]
Mean Diameter : 4.0 [μm]
Depth : 1.0−5.0[m]
0.4 Turbidity : 0−2.0[ppm]
Fixed Data Rate
1 2 3 4 5 6 7 8 9 10
Fig. 15. Effect of Particle Distruibution to Coverage
V. E XPERIMENTAL I NVESTIGATION
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
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
E XPERIMENTAL PARAMETERS
Data Rate 100[kbps]
Number of Bits 16000[Bits]
Sampling rate 50[M Sample/sec]
Electrical Filter 10[M Hz] LPF
Turbidity 0 - 20[N T U ]
D EVICE S PECIFICATION
LED Luxeon LXHLMB1B,MM1B,MD1B
Half-power Angle 5[deg.]
Detector Hamamatsu Si PIN-PD S3071
Peak Responsivity 920[nm]
Detetor Area 5.0[mm]
Cut Off Frequency 40[M Hz]
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[M Hz] 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
530 [nm] Simulated
0 650 [nm]
Relative Revied Optical Power [dB]
530 [nm] Measured
0 2 4 6 8 10 12
0 0.5 1.0 1.5
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
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.
Bit Error Rate
0 5 10 15
0 0.5 1.0 1.5 2.0
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. C ONCLUSION
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
underwater, providing high reliable link within short range, up to 5 to 10 meters.
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Yousuke Ito received B.E. degree in Information and Science Technology from Keio University, Yokohama, Japan
PLACE in 2004. She is currently enrolled in Graduate School of Science and Technology, School of Science for Open and
PHOTO Enviromental Systems, majoring in Information, Communication and Media Technologies in Keio University. He is
HERE expected to ﬁnish his M.E. degree in March 2006.
Shinichiro Haruyama is a professor at Department of Information and Computer Science, Faculty of Science
PLACE and Technology, Keio University,Yokohama, Japan. He received an M.S. in engineering science from University of
PHOTO California at Berkeley in 1983 and a Ph.D. in computer science from the University of Texas at Austin in 1990. He
HERE 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.
Masao Nakagawa was born in Tokyo, Japan in 1946. He received the B.E., M.E. and Ph.D. degrees in Electrical
PLACE Engineering from Keio Universty, Yokohama, Japan in 1969,1971 and 1974, respectively. Since 1973, he has been
PHOTO with the Department of Electrical Engineering, Keio University, where now he is Professor. His research interests are
HERE 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