Wireless Sensor Network, 2010, 2, 381-389
doi:10.4236/wsn.2010.24050 Published Online May 2010 (http://www.SciRP.org/journal/wsn)
Copyright © 2010 SciRes. WSN
A New Method to Improve Performance of Cooperative
Underwater Acoustic Wireless Sensor Networks via
Frequency Controlled Transmission Based on Length of
Data Links
Vahid Tabataba Vakily, Mohammadjavad Jannati
Iran University of science and technology, Tehran, Iran
E-mail: vakily@iust.ac.ir, mjannati@ee.iust.ac.ir
Received March 25 , 20 1 0; revised April 10, 2010; accepted April 19, 2010
In this paper a new method to improve performance of cooperative underwater acoustic (UWA) sensor net-
works will be introduced. The method is based on controlling and optimizing carrier frequencies which are
used in data links between network nods. In UWA channels Pathloss and noise power spectrum density (psd)
are related to carrier frequency. Therefore, unlike radio communications, in UWA Communications signal to
noise ratio (SNR) is related to frequency besides propagation link length. In such channels an optimum fre-
quency in whole frequency band and link lengths cannot be found. In Cooperative transmission, transmitter
sends one copy of transmitted data packets to relay node. Then relay depending on cooperation scheme, am-
plifies or decodes each data packet and retransmit it to destination. Receiver uses and combines both re-
ceived signals to estimate transmitted data. This paper wants to propose a new method to decrease network
power consumptions by controlling and sub-optimizing transmission frequency based on link length. For this
purpose, underwater channel parameters is simulated and analyzed in 1 km to 10 km lengths (midrange chan-
nel). Then link lengths sub categorized and in each category, optimum frequency is computed. With these
sub optimum frequencies, sensors and base station can adaptively control their carrier frequencies based on
link length and decrease network’s power consumptions. Finally Different Cooperative transmission
schemes “Decode and Forward (DF)” and “Amplify and Forward (AF)”, are simulated in UWA wireless
Sensor network with and without the new method. In receiver maximum ratio combiner (MRC) is used to
combining received signals and making data estimations. Simulations show that the new method, called AFC
cooperative UWA communication, can improve performance of underwater acoustic wireless sensor net-
works up to 40.14%.
Keywords: Underwater Acoustic Communications, Wireless Sensor Networks, Cooperative Transmission,
Decode and Forward, Amplify and Forward
1. Introduction
Recently, underwater acoustic wireless sensor networks
(UWA-WLSN) become a hot topic in acoustic commu-
nications zone. Major difference between this kind of
sensor networks and traditional ones is their special
physical layer which effects on acoustic waves used to
transmit data. Using acoustic waves is not only but the
best manner to achieve sufficient range and data rate in
underwater environment. The problem is that radio
waves will be absorbed soon in water and can not support
sufficient rang and data rate. Moreover, light experiences
high dispersion in underwater environment and again
cannot support sufficient range and rate. Unlike them,
new progresses in under water acoustic communications
make reliable data links for several kilometers conceiv-
able. So that researchers are effectively encouraged go-
ing ahead in underwater acoustic communication.
This strange kind of physical layer has several influ-
ences on channel parameters. Firstly, acoustic waves
move slowly, about 1500 m/s, in water which is one fifth
of radio waves speed in atmosphere [1]. So that acoustic
waves have large delay spreads. In radio channels Path-
loss only depends on link length. But acoustic waves
experience frequency and link length dependent Path-
losses in underwater environment. Therefore, Link’s
carrier frequency effects on its total performance. Be-
cause of suspended particles and small bubbles, acoustic
waves are dispersed widely in underwater environment.
Furthermore, reflections from surface and bottom of sea
increase channel fading. All points mentioned before
most be considered in design of underwater acoustic
wireless systems. Themes mentioned show that, like ra-
dio communication, in UWA communication range and
bandwidth are important bottlenecks.
Observed noise in the ocean is categorized into two
groups, man-made noise and ambient noise [2]. In deep
ocean man made noise is ignorable, whereas, in presence
of shipping activities or besides shore man made noise
increases level of total noise power. On the other hand,
geysers, earthquakes, heat and some kinds of marine
animals can be considered as major sources of ambient
noise. Total noise in underwater acoustic environment is
related to signal carrier frequency. In part 3.1, there are
further descriptions and statistical model of underwater
acoustic noise.
Since pathloss and noise power are frequency de-
pendent, SNR in underwater acoustic communications is
related to frequency. Therewith, like all wireless chan-
nels, in UWA channels, SNR is a function of link length.
Therefore SNR is influenced from two major parameters,
link length and frequency. It means that, changes in
length can influences on optimum frequency of system.
In Section 4, a new method to increase system perform-
ance is described. In this method link lengths are sub-
categorized. Then, for each category, optimum frequency
is defined. Finally, using proposed adaptive algorithm in
chapter 4, all network nodes adjust their carrier frequen-
cies to optimize total network’s performance. In this pa-
per, mentioned algorithm is called adaptive controlled
frequency (ACF) method. Simulations of Section 4
shows that, in compare with traditional method, ACF can
increase system performance up to 9.7%
According to considerable progresses in radio com-
munications, researches try to improve UWA systems by
applying new schemes which are lent from radio com-
munications. One of these methods is cooperative com-
munication which is suitable to use in wireless sensor
networks. In chapter 5 two schemes of cooperative
communication, DF and AF, is adjusted, applied and
simulated in UWA-WLSN. Simulations show that Com-
pared with no cooperation method, AF and DF methods
can improve system performance up to 17 and 33.38
percents, respectively. (Authors of the paper published
their First works on UWA cooperative WLSN in [3]
which are summarized in chapter 5).
Using results of previous chapters, in chapter 6 a new
method to improve UWA communication is proposed. In
this method, which is called ACF cooperative UWA
communication, depending on link length between nodes
and by performing ACF algorithm, optimum frequency
for each path is defined. Then data packets are transmit-
ted on all paths. Finally using cooperative schemes and
MRC, received signals combined and data packets are
The reminder of this paper organized as follows. In
Section 2 a brief literature review is presented. Next sec-
tion assigned to descrip tion of UWA channel. AFC algo-
rithm is proposed in Section 4. Cooperative UWA wire-
less communication described and simulated in next sec-
tion. In Section 6 AFC Cooperative scheme in UWA-
WLSNs is proposed and simulated. Finally, whole work
is summarized and concluded in last section.
This paper’s Simulations show that ACF cooperative
UWA communication scheme can improve performance
of UWA-WLSNs up to 40.14%.
2. Literature Review
Leonardo Da Vinci was the first who tries to use under-
water acoustic information to detect ships. With a long
tube submerged under the sea, he listened to sounds
which were propagated from ships and detected them.
The first operational underwater acoustic (UWA) com-
munication system was an underwater telephone, devel-
oped in 1945 in the United States, for communication
with submarines. It used a single side-band suppressed
carrier modulation in the 8-11 kHz band, and could op-
erate over several kilometers [4]. The development of
digital communications for undersea applications dates
back to simple ping-based use of sonars that operate in
the audible band [5]. First works on UWA multipath
channels to increase data rate was reported by Ross W il-
liams and Henry Battestin in 1971 [6]. Through the
1980s phase coherent communication was used almost
exclusively for deep-water vertical links, but in the early
1990s phase coherent communication in multipath chan-
nels began to attract attention, as incoherent methods
were limited to a bandwidth efficiency of approximately
0.5 bits per Hz [4]. Since the publication of the special
issue on ocean acoustic data Telemetry in the IEEE
journal of oceanic engineering in 1991, fundamental ad-
vances have been made in this field [4]. Bandwidth-
efficient phase-coherent communications, previously not
considered feasible, were demonstrated to be a viable
way of achieving high-speed data transmission through
many of the underwater channels, including the severely
time-spread horizontal shallow water chan nels [7-9]. The
new generation of UWA communication systems, based
on the principles of phase-coherent detection techniques,
is capable of achieving raw data throughputs that are an
order of magnitude higher than those of the existing sys-
tems [10] which ar e based on noncoheren t detection me-
Copyright © 2010 SciRes. WSN
thods. These results open many new possibilities for ap-
plication of UWA communications. Notable among the
emerging applications is the concept of an autonomous
oceanographic sampling network (AOSN) [11]. This
network will provide exchange of data, such as control,
telemetry and video signals between many network
nodes. The network nodes, both stationary and mobile
ones, located on underwater vehicles and robots, will be
equipped with various oceanographic instruments, such
as hydrophones, current meters, seismometers, sonars
and video cameras. Major difficulties are encountered
due to the long propagation times in the underwater
channels [3]. First protocols for acoustic local area net-
works (ALAN) have been proposed in [12,13]. Through-
put the 1990s a number of additional systems were de-
veloped and commercialized using both coherent and
noncoherent modulation [5].
At high frequencies appropriate for shallow water
communications, ray theory provides the framework for
determining the coarse multipath structure of the channe l
[5]. As such a model does not capture the time-varying
nature of the channel, efforts have been made to augment
this model with a time-varying surface [14].
Some researchers model the shallow water channel as
a Rayleigh fading channel but others challenge that as-
sumption, especially when discrete arrivals can clearly
be seen in the channel response. There has been no con-
sensus among researchers on the model applicable in
shallow waters. Recently, a ray theory based multipath
model where the individual multipath arrivals are mod-
eled as Rayleigh stochastic processes has been shown to
describe the medium range very shallow water channel
accurately [15]. Studies of acoustic propagation through
anisotropic shallow water environments in the presence
of internal waves [16] may form the basis of future
physics-based channel modeling research.
An additive Gaussian noise assumption is used com-
monly in the development of most signal processing
and communication techniques. Although this assump-
tion is valid in many environments, some underwater
channels exhibit highly impulsive noise. Signal detec-
tion [17] and Viterbi decoding [18] techniques devel-
oped for impulsive noise models such as the symmetric
noise have been shown to perform better in
warm shallow waters dominated by snapping shrimp
noise [5].
A good review of underwater network protocols can
be found in [13]. A store-and-forward protocol was pro-
posed in [19] for shallow-water ALAN’s, where they use
a form of packet radio network (PRN) protocol [20] that
matches the shallow-water acoustic channel characteris-
tics. In [21], the authors presented a clustered topology
assuming full-duplex modems.
Further and more detailed information about recent
advances in UWA communications and networks can be
found in [5], where authors made comprehensive study
on recent theoretical advances, technologies and produc-
tion systems.
3. UWA Channel
To specify a special wireless channel like UWA channel
several parameters must be defined. In this chapter im-
portant parameters of UWA channels like noise psd,
pathloss and SNR is studied.
3.1. Noise in UWA Channels
Sources of Ambient noise in UWA channels can be ca-
tegorized and modeled in 4 groups. Noise power spec-
trum density (psd) in underwater channel is depended on
frequency, ,
and can be modeled as [21]:
NfNf Nf Nf Nf  (1)
10 10
40200.526log60log3 ,
Nf sff
 
10 10
507.520log40log0.4 ,
Nff f
15 20log
Nf f  and
17 30log.
Nf f
Nf, and
Nf are
noises caused by turbulence, shipping activities, wind
and heat, respectively.
is shipping activity factor,
whose value ranges between 0 and 1 for low and high ac-
tivity, respectively. And w is wind velocity (0-10 m/s).
Figure 1 shows simulated noise psd in 3 arbitrary cas-
es, 0, 0sw
; 0.5, 5sw
and , in
frequencies less than 100 kHz. All other cases slide be-
tween these graphs.
1, 10sw
Figure 1. Noise psd versus frequency in 3 arbitrary selec-
tions of s and w.
Copyright © 2010 SciRes. WSN
3.2. Pathloss and SNR in UWA Channels
Signals in UWA Channels experience frequency and link
length dependent pathloss which is more complicated
than radio channels and can be modeled as
10log 10Tr
where is link length and absorption coefficient,
is function of frequency.
22 4
0.11 44 2.75 100.003
 
Figure 2 shows
in frequencies less than 100
First part of (2) is similar to radio channels and stands
for power consumptions of signals which are transmit-
ting from source to destination in wireless channels.
Second part corresponds to mechanical absorptions of
traveling wave’s power in underwater environment
which is caused by mechanical nature of acoustic waves
and specifies UWA channels.
For an arbitrary signal power, by substituting (3) in (2),
received power in destination can be computed. There-
fore, with aid of (1) we have:
,10log 10log
SNR dfPTN
where is signal power, is total noise power in
transmission band and is link length.
In Figure 3 relative SNR for several link length be-
tween 5 and 100 km simulated and plotted. It is obvious
that 3dB bandwidth has inverse ratio with link length.
Figure 3 is an endorsement for dependency of opti-
mum frequency to link lent. It means that, an optimum
frequency cannot be found for whole frequencies and
ranges. In next section this problem is studied.
Figure 2. Absorption coefficient
in frequencies less
than 100 kHz.
Figure 3. Relative SNR versus Frequency in 5-100 km.
4. AFC Algorithm
As it mentioned before, based on link length between
transmitter and receiver nodes, optimum frequency dif-
fers. Depending on range of transmission, optimum fre-
quency can be com puted fr om
 
max,max 10log10log
10 10log
opt iopt
SNR dfPd
fd Nf
In (5), optimum frequency,
, is the fre-
quency which maximizes when
,SNR d
Therefore i
optimizes transmission performance in
this range.
Figure 4 shows i
at different values of link length,
Figure 4. i
at different values of link length, .
Copyright © 2010 SciRes. WSN
4.1. AFC in UWA Channels
Considering previous sections, it is obvious that, trans-
mitters and receivers which are using variable link
lengths to communicate with each other, cannot find an
optimum carrier frequency to use in all situations. To
make UWA-WLSNs useable in different geographic
zones, topology of networks must be changeable. It
means that, link length between nodes experience
changes, which is confined by network dimensions. Us-
ing constant carrier frequency in whole network will
results in performance redu ction. In this part a new adap-
tive algorithm to decrease power consumptions of UWA
communication which are made by this problem is pro-
First step of adaptive frequency controlled (AFC) al-
gorithm is defining link length interval, . Second
step is calculating optimum frequency in each interval. In
this part is assigned equal to 500m and working
range of system is assumed, 1-10km, medium range.
With these definitions there are 18 intervals. For each
interval optimum frequency can be calculated from
 
max,max 10log10log
10 10log
iiii T
opt opt
SNR dfPd
fd Nf
 
, 1,2,...,max
didi d
 
Figure 3. Shows that 3db frequency band is a de-
creasing function of link length. Therefore, when
corresponding 3dB bandwidth of
smallest in this interval. And we have
 
max,max 10log10log
iii T
opt opt
SNR dfPd
fd Nf
 
From (6) i
for each interval can be calculated.
In Table 1, Using (7), i
for and
is computed.
dk m
With values of Table 1 AFC algorithm can be per-
formed as flowchart of Figure 5.
In Figure 5 flowchart of AFC algorithm for UWA
systems is shown. As it is seen, before starting algorithm
several initial definitions or calculations most be done
and a table like Table 1 must be formed. Then AFC al-
gorithm can be started. Based on link length between
transmitter and receiver nodes, must be defined
and with aid of Table 1 carrier frequency can be found.
Now one packet of data can be sent. If link length does
not change more than other pockets of data could be
Table 1. i
for 110
and 0.5 km steps.
d (km)
d (km)
7.7901 5.5-6 16.4101 1-1.5
7.4601 6-6.5 14.0901 1.5-2
7.1701 6.5-7 12.5001 2-2.5
6.9001 7-7.5 11.3401 2.5-3
6.6601 7.5-8 10.4301 3-3.5
6.4501 8-8.5 9.7101 3.5-4
6.2501 8.5-9 9.1101 4-4.5
6.0601 9-9.5 8.6001 4.5-5
5.8901 9.5-10 8.1701 5-5.5
Figure 5. Flowchart of AFC algorithm for UWA systems.
sent. By changing link length more than , di
carrier frequency must be defined again. The algorithm
continues and all data packets will be sent to receiver
In Figure 6 result of applying AFC algorithm in an
UWA channel is shown.
Simulations of this part prove that AFC algorithm can
increase performance of UWA systems. As it can be seen
in Figure 6 , in 10 km bit error rate (BER) decreases abou t
9.7% (from 0.3433 to 0.2947). It means that AFC algo-
rithm in UWA channels can increase system perform-
ance up to 9.7%. (Note that maximum bit error rate is 0.5
and vertical axis in Figure 6 is graphed logarithmic).
Copyright © 2010 SciRes. WSN
Figure 6. Results of applying AFC algorithm in an UWA
channel in compare with traditional scheme.
5. Cooperative Communication Schemes in
UWA Networks
Concept of spacial diversity attracts attention of wireless
communications researchers and results in continuous
and massive quests to make use of it in WLSNs. In a
wireless channel several paths can exist between trans-
mitter and receiver. If some of these paths are independ-
ent and have sufficient performance, channel perform-
ance can increase by sending copies of data in these
paths and combining them in receiver. Since paths are
independent total error probability decreases. Therefore
channel and system performance increases. Multiple in-
put multiple output (MIMO) systems make use of special
diversity by using several antennas in transmitter and
receiver. These antennas must be separated enough to
make corresponding paths independent. But In many
usages of WLSNs it is impossible. Because network
nodes maybe smaller than they could support such sepa-
rated antennas. To solve this problem idea of cooperative
communication is proposed. In cooperative communica-
tion systems, transmitter sends one copy of transmitted
data packets to relay node. Then relay depending on co-
operation scheme, amplifies or decodes each data packet
and retransmit it to destination. If relay has proper posi-
tion, relay path will be independent from direct path.
Receiver uses and combines both received signals to
estimate transmitted data [3].
In Figure 7 simplified model of one relay UWA co-
operative channel, which will be used in continuation of
the paper, is shown.
In next section two frequently used cooperative
schemes, DF and AF, in WLSNs is defined and applied
to an UWA cooperative WLSNs.
Figure 7. UWA cooperative channel.
5.1. DF and AF in UWA Cooperative WLSNs
Basic idea of DF is that one copy of data which is sent to
receiver must be sent to relay too. In relay, this message
is decoded, corrected, coded and retransmitted to desti-
nation. In destination data which is received from both
paths are decoded, corrected and combined to estimate
transmitted message. With this approach nether bit rate is
achievable [22].
12 21 23
sup max;,,;
df P
Y (8)
It means that relay decodes message perfectly and re-
transmits it to destination. In Gaussian channels, if
transmitter and relay send their data coherently, above
rate will be achievable.
In Figure 7 transmitter, relay and receiver nodes are
called 1, 2 and 3, respectively. In Gaussian relay chan-
nels, channel gain between nodes i and (j,ij
, 1,2,3ij
) is called . Received signals in relay
and receiver experience additive white Gaussian noise
with unit power. Moreover power constraints in trans-
mitter and receiver are,
EX 1
P and 2
, respectively. By computing (7), for Gaussian chan-
nel we have [23]:
21 1
22 22
max minlog(11),
log 12
hPhP hhPP
 
is a real constant and shows correlation between 1
and 2
Which are transmitted data from transmitter
and relay, respectively. If transmitter 1) and relay 2)
cannot transmit coherently, correlation is unusable and
. Therefore we have
21 1
31 1322
max minlog1,
log 1
Copyright © 2010 SciRes. WSN
In AF scheme, relay node amplifies and retransmits re-
ceived signals without decoding it. R elay r ece ives
in time . Then by considering power constraints mul-
tiplies by
21 1
31 1322
max minlog1,
log 1
A Gaussian relay channel which is modeled as coming
expressions is considered.
 
YihXi Zi
where and are received signals from relay and
transmitter. If all noise powers are unit and power con-
straints in transmitter and receiver are , we have:
21 1
And AF can achieve bit rate
232 21
31 22
21 32
log 11
 
If system works in low SNR regime Which means
, We will have
log 1ln 2
RPh (15)
It means that in such situation, relay channel cannot
help improving system performance and is unusable.
Because in AF scheme both noise and power are ampli-
fied and in low SNR AF cannot help data estimation in
In Figure 8 performances of one relay cooperative
UWA AF and DF channels is simulated and are com-
pared with no cooperation mode. In this figure horizontal
axis is distance between transmitter and receiver and
vertical axis is bit error rate (BER) which is representa-
tive of system performance. Relay position is same as
Figure 7 and , the angle between relay path and di-
rect path in receiver is .
Figure 8 shows that UWA cooperative schemes can
improve performance of UWA-WLSNs. Maximum im-
provement is at 7500m where BER decreases from
0.2596 in noncooperation mode to 0.0927 in DF mode
and 0.1755 in AF mode which means 33.38% and 17%,
respectively. (Note that maximum bit error rate is 0.5 and
vertical axis in Figure 8 is graphed logarithmic). Costs
of such improvements are relay establishment and cor-
responding source usages.
Figure 8. Performance of one relay UWA channel with and
without cooperation.
As it can be seen in Figure 8, if length of direct path
decreases performance improvement of cooperative
schemes decreases too. When direct path decreases less
than 5 km, relay path will be longer than it. Therefore
relay path experiences larger Pathloss and cooperative
channel tends to weak relay channel. As it mentioned in
last part, if relay channel is weak which means experi-
ences low SNR, relay channel will not help improving
system performance and will be unusable.
6. AFC Cooperative UWA Algorithm
In Sections 4 and 5 AFC algorithm and Cooperative
schemes in UWA system are described separately. In this
section, AFC cooperative UWA algorithm which is a
combination of cited methods in Section 4 and 5 is pro-
6.1. AFC Cooperative UWA-WLSNs
In AFC cooperative UWA method transmissions be-
tween all nodes (transmitters or relays) obey AFC algo-
rithm which is shown in Figure 5. It means that for
direct path and each part of relay path, carrier frequency
must be defined by AFC algorithm and these frequen-
cies may differ in different link lengths. Therefore if
AFC algorithm is applied in all parts of all paths, sys-
tem may use different working frequencies simultane-
ously and such networks are called AFC cooperative
In Figure 9 AFC cooperative UWA methods are si-
mulated and compared with other cited methods. De-
pending on cooperation scheme, AF and DF, new meth-
ods are called AF-AFC-UWA and DF-AFC-UWA
methods, respectively.
Copyright © 2010 SciRes. WSN
Figure 9. AFC cooperative UWA schemes compared with
AF, DF and traditional schemes in an UWA-WLSN.
In simulations of this part, based on proposed model in
Figure 7, transmitter power is 90 watts, relay position is
5km away from receiver, angle between direct path and
relay path in receiver, , is and direct path differs
from 2 km to 10 km. In receiver maximum ratio com-
biner (MRC) is used to combine received signals and
make data estimations.
In Figure 9, like Figure 8, when length of direct path
decreases, performance improvements which are made
by cooperative schemes decrease too. In such situations,
in spite of cooperative schemes, AFC algorithm plays its
role and improves system performance.
Simulations of this part show that maximum im-
provement is happened in 8km where BER decreases
form 0.2801 in traditional scheme to 0.0794 in DF-AFC-
UWA scheme which means 40.14%. (Note that maxi-
mum bit error rate is 0.5 and vertical axis in Figure 9 is
graphed logarithmic).
7. Conclusions and Summaries
In This paper a new method to improve performance of
cooperative UWA-WLSNs is proposed and evaluated v ia
simulations. The method is based on controlling and op-
timizing carrier frequencies which are used in data links
between network nods. In UWA channels Pathloss and
noise psd are related to carrier frequency. Therefore,
unlike radio communications, in UWA Communications
SNR is related to frequency besides propagation link
length. In such channels an optimum frequency in whole
frequency band and link lengths cannot be found. To
solve this problem AFC algorithm is proposed.
In Cooperative transmission, transmitter sends one
copy of transmitted data packets to relay node. Then re-
lay depending on cooperation scheme, amplifies or de-
codes each data packet and retransmit it to destination.
Receiver uses and combines both received signals to
estimate transmitted data. In this paper, receiver uses
MRC to combine received signals and make data estima-
tions. To use special diversity of cooperative communi-
cation in UWA-WLSNs these methods are applied and
In first section of the paper, UWA communication is
introduced. In Section 2 a summarized literature review
of UWA communications is offered. In subsequent sec-
tion, UWA channel introduced and some of its parame-
ters formulated. AFC algorithm for UWA channels is
proposed in Section 4. Then in next section Cooperative
communication schemes, AF and DF, is presented and
applied to UWA-WLSNs. Finally, in Section 6 AFC co-
operative UWA algorithm in WLSNs is proposed and
evaluated within computer simulations. This algorithm is
a combination of cooperative transmission and AFC al-
gorithm which is proposed in previous sections.
Simulations show that the new method, called AFC
cooperative UWA communication, can improve per-
formance of underwater acoustic wireless sensor net-
works up to 40.14%.
8. Acknowledgments
Authors would like to express their sincere thanks to Iran
Telecommunication Research Center (ITRC) for its val-
uable supports.
9. References
[1] M. Stojanovic, “Underwater Acoustic Communication
Channels: Propagation Models and Statistical Characteri-
zation,” IEEE Communications Magazine, Vol. 47, No. 1,
January 2009, pp. 84-89.
[2] M. Stojanovic, “Underwater Acoustic Networks: Channel
Models and Network Coding Based Lower Bound to
Transmission Power for Multicast,” IEEE Journal on Se-
lected Areas in Communications, Vol. 26, No. 9, De-
cember 2008, pp. 1708-1719.
[3] V. T. Vakily and M. J. Jannati, “Performance of Coop-
erative Transmission in Underwater Acoustic Sensor
Networks,” ICCIA International Conference on Control
Instrumentation and Automation, Tehran, 26-27 May 2010.
[4] M. Stojanovic, “Underwater Acoustic Communications,”
IEEE Electro International, Boston, 5 May 1995.
[5] M. Chitre, S. Shahabudeen, L. Freitag and M. Stojanovic,
“Recent Advances in Underwater Acoustic Communica-
tions & Networking,” IEEE Journal of Oceanic Engi-
neering, 2008, pp. 1-10.
[6] R. E. Williams and H. F. Battestin, “Coherent Recombi-
nation of Acoustic Multipath Signals Propagated in the
Deep Ocean,” The Journal of the Acoustical Society of
America, Vol. 50, No. 6, 1971, pp. 1433-1442.
Copyright © 2010 SciRes. WSN
Copyright © 2010 SciRes. WSN
[7] M. Stojanovic, J. A. Catipovic and J. G. Proakis, “Phase
Coherent Digital Communications for Underwater Acoustic
Channels,” IEEE Journal of Oceanic Engineering, Vol.
19, No. 1, January 1994, pp. 100-111.
[8] M. Stojanovic, J. A. Catipovic and J. G. Proakis, “Adap-
tive Multichannel Combining and Equalization for Un-
derwater Acoustic Communications,” The Journal of the
Acoustical Society of America, Vol. 94, No. 3, September
1993, pp. 1621-1631.
[9] M. Stojanovic, J. A. Catipovic and J. G. Proakis, “Re-
duced-Complexity Multichannel Processing of Underwa-
ter Acoustic Communication Signals,” The Journal of the
Acoustical Society of America, Vol. 98, No. 2, August
1995, pp. 961-972.
[10] J. Catipovic. “Performance Limitations in Underwater
Acoustic Telemetry,” IEEE Journal of Oceanic Engi-
neering, Vol. 15, No. 3, July 1990, pp. 205-216.
[11] T. Curtin, J. Bellingham, J. Catipovic and D. Webb,
“Autonomous Oceanographic Sampling Networks,” Oce-
anography, Vol. 6, No. 3, 1993, pp. 86-94.
[12] D. P. Brady and J. A. Catipovic, “Adaptive Multiuser
Detection for Underwater Acoustical Channels,” IEEE
Journal of Oceanic Engineering, Vol. 19, No. 2, April 1994,
pp. 158-165.
[13] J. L. Talavage, T. E. Thiel and D. Brady, “An Efficient
Store-and-Forward Protocol for a Shallow Water Acous-
tic Local Area Network,” Proceedings of the Oceans En-
gineering for Todays Technology and Tomorrows Pres-
ervation (OCEANS’94), Brest, 13-16 September 1994, pp.
[14] C. Bjerrum-Niese, L. Bjorno, M. A. Pinto and B. A.
Quellec, “A Simulation Tool for High Data-Rate Acous-
tic Communication in a Shallow-Water, Time Varying
Channel,” IEEE Journal of Oceanic Engineering, Vol. 21,
No. 2, 1996, pp. 143-149.
[15] M. Chitre, “A High-Frequency Warm Shallow Water
Acoustic Communications Channel Model and Measure-
ments,” The Journal of the Acoustical Society of America,
Vol. 122, No. 5, 2007, pp. 2580-2586.
[16] M. Badiey, B. G. Katsnelson, J. F. Lynch and S. Pere-
selkov, “Frequency Dependence and Intensity Fluctua-
tions due to Shallow Water Internal Waves,” The Journal
of the Acoustical Society of A merica, Vol. 122, No. 2, 2007,
pp. 747-760.
[17] M. A. Chitre, J. R. Potter and S. H. Ong, “Optimal and
Near-Optimal Signal Detection in Snapping Shrimp
Dominated Ambient Noise,” IEEE Journal of Oceanic
Engineering, Vol. 31, No. 2, 2006, pp. 497-503.
[18] M. A. Chitre, J. R. Potter and S. H. Ong, “Viterbi De-
coding of Convolutional Codes in Symmetric
Noise,” IEEE Transactions on Communications, Vol. 55,
No. 12, 2007, pp. 2230-2233.
[19] J. Jubin and J. D. Tornow, “The DARPA Packet Radio
Network Protocols,” Proceedings of the IEEE, Vol. 75,
No. 1, 1987, pp. 21-32.
[20] G. G. Xie and J. H. Gibson, “A Network Layer Protocol
for UANs to Address Propagation Delay Induced Per-
formance Limitations,” MTS/IEEE Oceans’01 Confer-
ence, Boston, Vol. 4, 5-8 November 2001, pp. 2087-2094.
[21] M. Stojanovic, “On the Relationship between Capacity
and Distance in an Underwater Acoustic Communication
Channel,” A CM SIGMOB ILE Mobile Comput ing and C om-
munications Review, Vol. 11, No. 4, October 2007, pp.
[22] T. Cover and A. E. Gamal, “Capacity Theorems for the
Relay Channel,” IEEE Transactions on Information Theory,
Vol. 25, No. 5, 1979, pp. 572-584.
[23] A. Host-Madsen and J. Zhang, “Capacity Bounds and
Power Allocation for the Wireless Relay Channel,” IEEE
Transactions on Information Theory, Vol. 51, No. 6, 2005,
pp. 2020-2040.