Int. J. Communications, Network and System Sciences, 2013, 6, 361-376 Published Online August 2013 (
Review Article: Multicarrier Communication for
Underwater Acoustic Channel
Hamada Esmaiel, Danchi Jiang
School of Engineering, University of Tasmania, Hobart, Australia
Received June 24, 2013; revised July 22, 2013; accepted July 26, 2013
Copyright © 2013 Hamada Esmaiel, Danchi Jiang. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In past decades, there has been a growing interest in the discussion and study of using underwater acoustic channel as
the physical layer for communication systems, ranging from point-to-point communications to underwater multicarrier
modulation networks. A series of review papers were already available to provide a history of the development of the
field until the end of the last decade. In this paper, we attempt to provide an overview of the key developments, both
theoretical and applied, in the particular topics regarding multicarrier communication for underwater acoustic communi-
cation such as the channel and Doppler shift estimation, video and image transmission throw multicarrier techniques,
etc. This paper also includes acoustic propagation properties in seawater and underwater acoustic channel representation.
Keywords: Underwater Communication; Multicarrier Communication; Channel Coding; Orthogonal Frequency
Division Multiplexing (OFDM); Filterbank Multicarrier (FBMC)
1. Introduction
Underwater acoustic channels are considered to be “quite
possibly nature’s most unforgiving wireless medium” [1].
The complexity of underwater acoustic channels is domi-
nated by the ocean environment characteristics which in-
clude significant delay, Double-side-spreading, Doppler-
spreads, frequency-selective fading, and limited bandwidth
[2]. However, efficient underwater communications are
critical to many types of scientific and civil missions in
the ocean, such as ocean monitoring, ocean exploration,
undersea rescue, and undersea disaster response. Human
knowledge and understanding of the oceans, rests on our
ability to collect information from remote undersea loca-
tions. Together with sensor technology and vehicular
technology, wireless underwater communications are de-
sirable to enable new applications ranging from envi-
ronmental monitoring to gathering of oceanographic data,
marine archaeology, and search and rescue missions.
New technologies of high speed communication for
image and video transmission are also desirable to facili-
tate the next generation of efficient undersea expeditions.
However, current acoustic communication technologies
can only provide limited data rates due to the particular
physical features of channel [3]. The corresponding wire-
less technology for undersea communications still needs
significant further development. Research has been ac-
tive for over a decade on designing the methods for wire-
less information transmission underwater. Due to electro-
magnetic waves in underwater channel propagate only
over extremely short distances acoustic wave used. In
contrast, acoustic waves can propagate over much longer
distances. However, an underwater acoustic channel pre-
sents a communication system designer with many diffi-
Figure 1 [4] illustrates the scenario of shallow water
multipath propagation. In such situation, in addition to
the direct path, the signal also propagates via reflections
from the surface and bottom, resulting in a multipath
effect with much larger time dispersion that of wireless
propagation in air.
Figure 1. Shallow water multipath propagation: in addition
to the direct path, the signal propagates via reflections from
the surface and bottom.
opyright © 2013 SciRes. IJCNS
There are many issues that have to be carefully exam-
ined when designing an acoustic based transmission sys-
tem for underwater channels. Some of these are [5]: 1)
Attenuation due to the absorption of the acoustic waves
in water, which limits the distance the sound, can travel;
2) Low propagation speed of the sound, roughly around
1500 m/s; 3) Multipath due to the reflection from the
bottom and surface of sea, causing echoes and interfer-
ence; 4) The transmitted signal suffers from the hetero-
geneous characteristics of the underwater channel as well
as Doppler’s effect caused by the movement of transmi-
ter and receiver; 5) Noise in the ocean. The noise level
can mask the portion of the signal and block the corre-
sponding carried data.
The properties of the underwater medium are also ex-
tremely varied, and change in both space and time. Fluc-
tuations due to environmental characteristics include sea-
sonal changes, geographical variations both in tempera-
ture and salinity, seabed relief, currents, tides, internal
waves, movement of the acoustic systems and their tar-
gets, etc. All this makes the underwater acoustic signal
fluctuating randomly. As such, the selection of modula-
tion and error correction techniques is very challenging.
To mitigate the bandwidth limitation, multicarrier modu-
lation is used in the underwater acoustic channel. It is an
alternative to overcome the long-time delays in under-
water acoustic channels. It increases the symbol interval
and thereby decreases the inter-symbol interference (ISI)
In this paper, we aim to provide a brief overview of
the key developments, both theoretical and applied, in
the field in the subsequent period. We also hope to pro-
vide an insight into some of the open problems and chal-
lenges facing researchers in this field in the near future.
Rather than attempting to provide an exhaustive survey
of all research in the field, we will concentrate on multi-
carrier modulation for underwater acoustic channel simu-
lation, challenges, ideas and developments that are likely
to be the keystone of future digital signal processing for
underwater acoustic communication systems.
This paper summarizes several aspects of underwater
acoustic communication. It is organized into the follow-
ing sections. Section 2 briefly summarizes acoustic propa-
gation properties in seawater. Section 3 analyses channel
coding performance for multicarrier modulation in un-
derwater acoustic communication systems. In Section 4,
multicarrier modulations for underwater acoustic com-
munications are presented. The underwater acoustic chan-
nel estimation for underwater communication is included
in Section 5. In Section 6, Doppler shift estimation for
underwater acoustic communications is studied. Image &
video transmission over the underwater acoustic channel
(UWAC) with multicarrier modulation is discussed in
Section 7. A summary of this paper is included in Section 8.
2. Acoustic Propagation Properties in
A good understanding and reasonably accurate modelling
of the underwater acoustic channel is the required basis
upon which all other works for underwater networks can
be carried out. Several models are already available for
calculating and predicting the attenuation [7,8], which
can also help to model other aspects of the underwater
acoustic channel. Furthermore, parameters of frequency,
distance, depth, acidity to salinity, and temperature of the
underwater environment can be used to characterize how
the channel acts and how network can possibly perform.
This section summarizes underwater channel models for
that purpose.
2.1. Propagation Loss
The transmitted acoustic signal in underwater acoustic
communication reduces strength with increasing distance
due to many factors such as absorption caused by mag-
nesium sulphate and boric acid, particle motion and geo-
metrical spreading, etc. Propagation loss is composed
mainly of three aspects, namely, geometrical spreading,
attenuation and the anomaly of propagation. The latter is
nearly impossible to model. However it is known that the
signal attenuation, in dB, that occurs over a transmission
distance for a signal frequency
can be approxi-
mated as [9]:
10 log1,10 log10 log,Af kll
  (1)
is the absorption coefficient in dB/km, which
can be obtained from the particular models characterizing
it, and k represents the geometrical spreading factor with
its value between 1 - 2.
2.2. Absorption Coefficient
Attenuation by absorption occurs due to the conversion
of acoustic energy into heat in sea-water. This process is
frequency dependent since at higher frequencies more
energy is absorbed. The attenuation by absorption mod-
els considered for inclusion into the Thorp model [9].
Equation (2) provides the absorption coefficient in dB/km
as a function in carrier frequency c
0.1 40
10log2.75 100.003.
1 4100
ff f
 
 (2)
2.3. Ambient Noise Model
Ambient noise in the ocean can be described as Gaussian
and having a continuous power spectral density (p.s.d.)
[9]. The four most prominent sources for ambient noise
are the turbulence, shipping, wind driven waves and
thermal noise. Their p.s.d. in dB re µPa per Hz are given
Copyright © 2013 SciRes. IJCNS
by the formulae Equations (3)-(6) shown below, respec-
tively [9]:
10 log1730 log,
Nf f (3)
 
10log40 200.526log
60 log0.03,
Nf sf
 
 (4)
Nff f
10log1520log .
Nf f  (6)
The ambient noise in the ocean is affected by different
factors in specific frequency ranges. In the noise models
given in Equations (3) to (6), the effect of colored noise
denoted by represents the turbulence noise at
frequency the shipping noise (with as
the shipping factor which lies between 0 and 1),
the wind driven wave noise (with as the wind speed
in m/s), and the thermal noise. The composite
noise p.s.d. can obtained in µPa from [
 
NfNf Nf NNf
2.4. Signal-to-Noise Ratio
In UWAC, signal-to-noise ratio can be calculated [10]
based on signal attenuation and the noise p.s.d., Specifi-
cally, the SNR observed at the receiver can be calculated
in µPa re dB per Hz using the following equation:
 
SNR ,,
lf N ff
where is the SNR over a distance and a
transmission center frequency f, P is the signal transmis-
sion power and
SNR ,lf
represents the receiver noise band-
width. Equation (8) clearly shows that the underwater
acoustic channel SNR is a function of transmission fre-
quency. As such we can find the optimal frequency for
UWAC to maximize SNR. The attenuation-noise (AN)
factor, given by
lf can be used to reflect the fre-
quency dependent part of the SNR. By close analysis of
this relationship, it can also be used to determine the op-
timal frequency at which the maximal narrow-band SNR
is achieved for each transmission distance l.
Since the SNR is inversely proportional to the attenua-
tion-noise factor, the optimal frequency is that for which
the value of 1/AN (represented in dB re µPa per Hz) is
the highest over the combination of a certain distance,
l. In Figure 2, [10] frequency-dependent part of
the narrowband SNR,
11, ,
fNf is shown. In
this plot the attenuation and noise parameters are selected
as and
to reflect the UWAC
with moderate shipping activity and no wave noise.
It can be seen that there is a frequency for which the
Figure 2. Narrow band SNR, 1/A(1, f)N(f) ; k = 1.5; s = 0; ω
= 0.
narrowband SNR is maximized for a particular distance,
for the given attenuation and noise constants. This optimal
frequency, let it denoted
l can be selected as the
carrier frequency c
for that particular transmission
distance [10].
2.5. Channel Capacity
As per the Shannon theorem the channel capacity i.e. the
theoretical upper bound on data that can be communi-
cated through a undistorted channel subject to additive
white Gaussian noise is given by the following formula
log 1,
where is the channel bandwidth in Hz and
represents the channel SNR. The basic Shannon rela-
tionship shown in Equation (9) can be extended to be
applicable in cases where the noise is dependent on fre-
quency to take the form of [9]:
For time-invariant channel, if a certain time interval
with Gaussian noise, we can obtain the total capacity by
dividing the total bandwidth into multiple narrow sub-
bands and summing their individual capacities collec-
tively. Then each sub-band has a small width
is centered on the transmission frequency, i.e. the band-
width. In the case where the transmission bandwidth,
,Bl over a distance is known along with the trans-
mission power
Pl , we can extend Equation (10) to
obtain the channel capacity over a distance [
logd .
Al fNfBl
The choice of the underlying absorption coefficient
model imposes a dependence of the capacity on depth,
temperature, salinity and acidity as well.
Copyright © 2013 SciRes. IJCNS
2.6. Doppler Shift and UWAC Multipath
In underwater communication, the relative movement be-
tween the transmitter and the receiver due to the constant
motion of nodes results in Doppler shifts, which signifi-
cantly distort received signals. It is required to estimate
the Doppler shift and compensate it for all UWAC ap-
plications. Different from the case of terrestrial commu-
nication where the Doppler effect is modeled by a fre-
quency shift, due to the slow sound speed in water, the
effect of transceiver motion on the duration of the sym-
bol cannot be neglected [11]. Doppler phase ,
pending on the relative velocity and the ratio between
the carrier frequencyc
and the symbol rate 1RT
[12] caused in the received signal as:
Rc v
 
 (12)
From [13,14], UWAC multipath representation for mul-
tipath arrival p is characterized by its mean magnitude
and delay
These quantities are dependent
on the path length
l which in turn is a function of the
given range
The path magnitude gain is given by
pp pc
 where
 is the
amount of loss due to reflection at the bottom and surface,
is the number of reflections for path From
Equation (1), the acoustic propagation loss, represented
by resulting in the following equation:
pc pc
Al flf
The delay for path given by
tlc (c = 1500
m/s is the speed of sound in water) and
l is the path
length for path p [14]. for each path re-
spectively, the path lengths can be calculated using pla-
nar geometry.
0,1,3,5, 7
2.7. Underwater Acoustic Noise Model
An empirical model for the noise of the acoustic under-
water channel in shallow water from the analysis of field
data measurements has been presented in [15]. In that
paper, a probability density function for the noise ampli-
tude distribution is proposed and the associated likeli-
hood functions are derived. As a result, an expression to
the probability of symbol error for binary signaling is
presented for the channel. In Addition, the results of
simulations conducted using the field collected noise
samples are presented, in order to verify the noise effect
on the performance of underwater acoustic communica-
tion binary signaling systems. The analysis of field data
measurements has shown that the noise amplitude distri-
bution presents good fitting with the Student’sdistribu-
tion. From [
15], the symbol error probability of the bi-
nary UWAN channel can be expressed as:
24 15
ebobo d.
is a vector of
discrete amplitude levels of
noise, integer 1, 2,,,kM
and bo
EN is the energy
per bit to noise power spectral density ratio.
2.8. Underwater Acoustic Channel Simulator
The signal transmission in underwater acoustic commu-
nications can be modeled as a time-varying channel. In
particular, the noise-free signal at a receiver is described
as a convolution [16]:
,d,0,ytht sttTs
between the channel impulse response
and the
source signal
t, where Ts is the signal duration.
This description of linear time-variant systems is quite
generic. In underwater acoustic communications, other
variants of the description may also be used. For example,
for a channel with discrete multipath components, the
model [17-19] used, and UWA channel represented as:
ct Att
Within a data block of interest, each path delay can be
associated with one Doppler scale factor as:
t (17)
and the path amplitudes are assumed constant within one
data block
tA Furthermore we assume that the
UWA channel can be well approximated by
N domi-
nant discrete paths. Hence, the channel model can be
simplified to be:
ct Aat
3. Channel Coding Performance for
Multicarrier Modulation in Underwater
Acoustic Communication
It is required that digital communication systems, par-
ticularly for underwater use, to perform accurately and
reliably in the presence of noise and interference. Among
many possible ways to achieve this goal, forward error
correction coding is the most effective and economical.
The fast temporal variations, long multipath delay spreads,
and severe frequency-dependent attenuations of under-
water acoustic communication channels are extremely
complex that impedes underwater acoustic data transmis-
sion. To alleviate this problem, channel coding is indis-
pensable in UWA communication system to increase the
Copyright © 2013 SciRes. IJCNS
reliability [20]. Also for underwater communication com-
monly used symbol demodulation schemes do not de-
pend on the noise power, so bit error performance with-
out error correcting coding will not be improved [21].
Here, Forward Error Correction (FEC) is a type of error
correction which improves simple error detection schemes
by enabling the receiver to correct errors once they are
detected. This reduces the need for retransmissions and
energy consumption [22].
The power of error correcting codes increases with the
channel coding length constraint and approaching the
Shannon limit with a large number of length constraints.
But in return the complexity of the decoder also increases
with length constraint [23]. For these reasons, it is desir-
able to construct long codes and minimize the complex-
ity of the decoder. The concatenation of codes is a cheap
solution for that [24] for multicarrier modulation in un-
derwater acoustic communication. The major drawback
of the concatenation code is that the decoder is unable to
decode correctly in the presence of a burst of erroneous
bits. Hence, an interleaver can be designed to introduce a
dependency between the bits input. In order to minimize
the error rate two evolved coding scheme [22], which are
summarized in the following two subsections.
3.1. Reed Solomon Coder
Reed Solomon codes
,,nkt are cyclic codes, built
from symbols with a maximum of where
is the number of elements in the Galois Field
and t is the power correcting code. So the
number of control symbols is
It has been approached in [25] to develop a sufficiently
robust acoustic link allowing the transmission of differ-
ent information using Reed Solomon coder and conven-
tional coding to protect data transmission over underwa-
ter acoustic channel. Where underwater acoustic link is
designed to transmit different kinds of data as text, im-
ages and speech signal, blind spatial-temporal equalizer
is used to reduce different underwater acoustic perturba-
tions. To improve the underwater acoustic link perform-
ance and obtain a higher code rate, Reed Solomon Block
Turbo Codes (RS BTC) has been introduced and tested in
real conditions, with the aim to decrease the BER. A dif-
ferential coding has been used to solve the phase ambi-
guities. Channels block coders algorithm applies to trans-
mission of constant data. Where Block codes are FEC
codes that enable a limited number of errors to be de-
tected and corrected without retransmission. Block codes
has been used to improve the performance of a commu-
nications system when other means of improvement
(such increasing transmitter power or using a stronger
modulator) are impractical [26].
3.2. Low Density Parity Check Code (LDPC)
LDPC codes are a special type of linear block coder. The
parity-check matrix H of LDPC codes are very sparse, i.e.
they can be specified by a matrix containing mostly 0’s
[27]. It is used to reduce error codes and achieve credible
transmit performance of underwater digital signal. Turbo
code has also been recommended to apply to underwater
digital speech communication system and simulation re-
sults in underwater digital speech system discussed in
[27] have shown that LDPC has a better performance
than the turbo coder.
Modification in the LDPC has been proposed to more
matched multicarrier underwater communication systems
[28], by focusing on the case of matching the coding
symbols with the modulation symbols. Experimental re-
sults show, with real data that whenever the uncoded
BER is below 0.1, normally no decoding errors will oc-
cur for the rate 1/2 of nonbinary LDPC codes used in [28]
and it consistent with the simulation results. The unen-
coded BER can serve as a quick performance indicator to
assess how likely the decoding will succeed. The re-
searcher results show that LDPC code system has a better
error correct performance and can achieve a better BER
under the relative lower SNR [20].
Performances of LDPC codes with different parame-
ters over different underwater acoustic communication
channels are studied [20], by adjusting the encoding and
decoding parameters according to different underwater
acoustic channels.
When LDPC coder used as channel coder for zero-
padding orthogonal frequency division multiplexing (ZP-
OFDM) multicarrier modulation the spectral efficiency
and the data rate
are [29,30]:
log bitssHz,
kb s.RB
where is the ZP-OFDM symbol duration,
T is the
guard interval,
is number of all subcarrier,
S is
data subcarrier in total, is the rate of nonbinary LDPC
code [
is the quadrature amplitude modulation
symbol, and is the channel bandwidth. LDPC pro-
duces high block-error-rate (BLER) performance in ZP-
OFDM multi-carrier system.
4. Multi-Carrier Modulation for Underwater
Acoustic Communications
Multi-carrier modulation Systems are well known to be
attractive for communications through multi-path com-
munications channels. The traditional approach expects
the symbol duration of the transmitted signal to be larger
than channel delay spread. This results in a low rate
Copyright © 2013 SciRes. IJCNS
“sampling” of the channel impulse response (once per
symbol in each sub-channel) and a sub-channel band-
width that is less than the coherence bandwidth of the
physical propagation channel [31]. Multi-Carrier Modu-
lation (MCM) have become popular in UWA channel for
two reasons. First, a signal can be processed in a receiver
without the increase of noise or interference caused by
linear equalization of a single carrier signal; second, the
long symbol period used in MCM ensures greater immu-
nity to impulse noise and fast fades [32].
4.1. Orthogonal Frequency Division
Multicarrier modulation in the form of orthogonal fre-
quency division multiplexing (OFDM) has been studied
and implemented for broadband wired and wireless com-
munications for the past two decades. OFDM is widely
adopted because of a number of its advantages as [33-35]:
1) Orthogonality of subcarrier signals that allows: a)
Easy generation of transmit signal through an inverse fast
Fourier transform (IFFT) block; b) Easy separation of the
transmitted data symbols at the receiver through a fast
block; c) Easy equalization through a scalar gain per
subcarrier; d) Easy adoption to multiple-input multiple-
output (MIMO) channels; 2) Closely spaced orthogonal
subcarriers partition the available bandwidth into a maxi-
mum collection of narrow sub-bands; 3) Adaptive modu-
lation schemes can be applied to subcarrier bands to
maximize bandwidth efficiency/transmission rate; 4) The
very special structure of OFDM symbols simplifies the
tasks of carrier and symbol synchronizations.
OFDM as an MCM is particularly efficient when noise
is spread over a large portion of the available bandwidth.
It transmits signals over multiple orthogonal sub-carriers
simultaneously and performs robustly in severe multi-
path environments achieving high spectral efficiency.
OFDM used in underwater communications as a superior
alternative to single carrier broadband modulation to
achieve high data rate transmission [4,17,36,37]. It has
been proved to be an effective technique for combating
the multipath delay spread without the need for complex
time-domain equalizers, due to its robustness against fre-
quency selective fading and narrowband interference. In
fact, if the number of subcarriers is large enough, each
subcarrier only deals with flat fading rather than with
frequency selective fading as a wideband carrier does.
The narrowband interference will affect only one or two
subcarriers of the whole bunch of subcarriers. The major
issue in applying OFDM to underwater channels is the
motion induced Doppler distortion which creates non-
uniform frequency offset in a wideband acoustic signal
4.1.1. ZP-OFDM for Underwater Acoustic
Zero-padded (ZP) [29,30,38] orthogonal frequency divi-
sion multiplexing (OFDM) has been extensively investi-
gated for high data rate underwater acoustic communica-
tion. Frequency-domain oversampling method [30] is used
to avoid information loss incurred by the overlap-add
operation. A larger FFT size used to improve system
performance over underwater acoustic channels with sig-
nificant Doppler spread. The system is validated using
real data collected from field experiments. Zero padding
is used instead of the conventional cyclic prefix in order
to save transmission power during the (long) guard in-
terval. ZP-OFDM transmitted and received signals in the
time domain during underwater channel paths signals after
implemented to make zero-pad guard interval accept chan-
nel path delay shown in Figure 3 [30].
For ZP-OFDM With symbol duration the subcar-
rier spacing is
and the subcarriers are located
at frequencies
ff k
 
where c
is the center frequency, and
is the total
number of subcarriers, leading to the bandwidth B = K/T.
k denote the information symbol on the subcar-
rier. The transmitted passband signal is [
2Ree,0 ,
where T
is the ZP-OFDM block duration as shown in
Figure 3.
The baseband signal
tz is obtained with the pass-
band to baseband downshifting and the lowpass filtering,
leading to [30]:
cp p
jmk bt
zt A
skgb t
Figure 3. Illustration of the transmitted and received signals
in the time domain. (a) One transmitted ZP-OFDM block;
(b) One received ZP-OFDM block.
Copyright © 2013 SciRes. IJCNS
b represents the residual Doppler rate satisfy-
ZP-OFDM saves Power transmitted and increases
channel capacity. In [39] authors derived bounds to the
channel capacity of OFDM systems over the underwater
(UW) acoustic fading channel as a function of the dis-
tance between the transmitter and the receiver.
4.1.2. Pilot Sig nal Desi g n for OFDM Mul ti carri er
The challenges in pilot design for multicarrier transmis-
sion over underwater acoustic time-varying channels are
two-fold [40,41]: 1) Sets of adjacent observations are
needed to estimate the inter-carrier interference (ICI)
coefficients; 2) Keeping pilot and data symbols orthogo-
nal at the receiver is challenging due to the ICI. Gener-
ally there are three types of pilot insertion methods for
OFDM system, comb-type; block-type; hexagonal grid-
type, etc. [42]. Random selection of pilot subcarriers is
motivated by the compressive sensing [43] with the sys-
tematic use of pilot blocks in regular intervals as seen in
non-sparse channel estimation of time-varying channels
In [40], authors are interested in how to address the
ICI between data and pilot subcarriers without guard
zeros. As it decreases spectral efficiency, focus has been
put on whether data symbol carrying subcarriers should
be used as observations in channel estimation, which also
contain ICI originating from the pilot symbols. The per-
formance under varying amounts of pilot overhead has
also been studied. Specifically, authors are looking for an
optimum tradeoff between using more pilots or a more
robust modulation scheme, to achieve the highest spectral
4.1.3. OFDM Based on Discrete Cosine Transfo rm for
Underwater Acoustic Communication
The orthogonal feature of conventional OFDM can also
be achieved by inversing DCT (IDCT)-DCT structure for
underwater communication, which reduces implementa-
tion area and increases computational speed, as only real
calculations, is required. This system provides higher peak-
to-average power ratio (PAPR) reduction and achieves
better noise immunity and hence a better bit error rate
(BER) performance than standard OFDM, while main-
taining a low implementation cost [32,44].
DCT based OFDM is a better technology for under-
water acoustic communication, because the bandwidth
required for DCT is half of that required for DFT when
both systems have same number of subcarriers which
will be matched with underwater channel limited band-
width. It has also been shown that the speed of calcul-
tion of orthogonal components is increased three folds
while the implementation size reduces to half as com-
pared to fast FFT based design [44]. Furthermore, it is
known that the DCT basis have excellent spectral com-
paction and energy concentration properties which in
turn lead to improved performance with suitable channel
estimation [44]. As DCT is widely adopted in image/
video coding standards, by using it for modulation/de-
modulation on frequency selective channels it will result
in a better integrated system design and a reduced overall
implementation cost [44].
4.1.4. Orthogonal Signal-Division Multiplexing for
Underwater Acoustic Communication
Orthogonal signal-division multiplexing (OSDM) is pro-
posed as a UWA communication system scheme that
measures the multipath profile without an adaptation or
interpolation process, to achieve stable communication in
doubly spread channels [45]. The performance compare-
son of the OSDM scheme and existing schemes in dou-
bly spread channels has been done. The ill-conditioned
problem exists for conventional OSDM, which employs
a single transducer in the receiver. The introduction of a
multichannel receiver has been found to be effective
against the ill-conditioned problem. Evaluation of OSDM
communication done by comparing it to existing schemes
with single-carrier Recursive Least Square Differential
Feedback Equalizer (RLS-DFE) [46] and OFDM. OSDM
with a multichannel receiver is attractive in terms of
communication quality. It achieves much better BER per-
formance comparing to the other schemes in both static
and dynamic channels, although its complexity is less
than that of RLS-DFE. OSDM can become a viable al-
ternative offering a highly reliable communication envi-
ronment for UWA communication with multipath and
Doppler spread (tested only for shallow water) with prac-
tical complexity [45] and it can be a very important of
point for future research.
4.1.5. Time Domain Synchronous Orthogonal
Frequenc y D ivisio n Multip lexing
A time domain synchronous orthogonal frequency divi-
sion multiplexing (TDS-OFDM) scheme with dual Pseudo-
noise (PN) sequence [47] is proposed in [48] for UWA
communication. TDS-OFDM system is used for under-
water acoustic channel shown in Figure 4 [48].
Instead of using cyclic prefix (CP) or zero padding (ZP)
as the guard interval, the proposed TDS-OFDM scheme
uses two identical PN sequences as guard interval, and
utilizes them for frame synchronization and channel es-
timation. TDS-OFDM increases the spectrum efficiency
over the conventional CP or ZP OFDM systems where
additional pilots have to be inserted for channel estima-
Copyright © 2013 SciRes. IJCNS
Figure 4. TDS-OFDM system for underwater acoustic
tion. With the dual PN at each frame header, compressed
sensing channel estimation is adopted and a rather simple
equalization design to reduce the receiver complexity.
Conventional performance of ZP-OFDM receiver is
severely limited by the ICI due to the fast channel varia-
tions within each OFDM symbol. Furthermore, the UWA
channel is wideband in nature due to the small ratio of
the carrier frequency to the signal bandwidth [30,49]. In
[50,51] cyclic shift keying spread spectrum OFDM method
propose as a UWA communication system.
In [50,51] cyclic shift keying spread spectrum OFDM
method is proposed to use in a UWA communication
system. The aim is to solving the problem of low data
rate of direct sequence spread spectrum underwater acou-
stic communication [52] and that of the complexity of the
receivers of M-ary spread spectrum. The paper also
aimed to improve bandwidth efficiency and bit rates.
This method has high data rate comparing to conven-
tional direct sequence spread spectrum for the underwa-
ter acoustic communication and low bit error rate.
4.1.6. P o wer and Bit Loading for Underw a ter
Acoustic OFDM System
Adaptive bit and power loading is a constraint optimiza-
tion problem with generally two cases of practical inter-
est, where the objectives are the achievable data rate
maximization (RM) and system margin maximization
(MM) [53].
In [41,54,55] researchers propose a different optimiza-
tion model for underwater acoustic (UWA) channels,
which is achieved by two algorithms: one is the band-
width-efficient bit loading algorithm; the other is the
Lloyd algorithm based limited feedback procedure. It
aims at minimizing the power consumption under con-
straints of the constant symbol data rate and desired bit-
error-rate (BER). Algorithms are employed to quantize
the CSI at the receiver and construct the codebook, which
is adopted to achieve the limited feedback process. After
selecting an initial bit loading vector upon the current
CSI, the receiver will broadcast its index to the transmit-
ter, then the transmitter will compute the bandwidth-
efficient bit loading algorithm and allocate the corre-
sponding power and bits to each OFDM subcarrier. Also
algorithms are used for UWA cooperative communica-
tion system, which involves the Decode-and-Forward
(DF) transmission protocol [41,56].
The ambient noise power
for each OFDM
sub-carrier can be proposed as [41,56]:
 
10 5
10 ,
iH iH
iL iL
iii i
Nfdff df
 (25)
where iL
and iH
are the bound of ith sub-carrier
frequency. The maximum capacity of a UWA SISO-
OFDM system with total system transmits power con-
straint describe as [41,56]:
maxlog 1
s.t.,0,1, ,
 
, (26)
N is the number of sub-channels,
is the sub-
channel transmitted bandwidth, i
is the transmitted
power in the sub-channel, ith
the total transmitted
power, i
is the sub-channel power gain and the power
loading will be based on this parameter, and
gH ,
where i
is the fading amplitude of the
ith 2
is the sub-channel power, 2
is the ambient noise power and it’s a constant of
sub-channel, and ii
is the or carrier-to-noise
CNR per sub-channel. is the “SNR gap” for
characterizing the difference between the needed
to achieve a certain data rate for a practical system and
the theoretical limit.
4.1.7. OFDM Receiver Designs for Underw ater
Acoustic Communication
OFDM receiver designs for underwater acoustic channels
with user and path specific Doppler scaling distortions
were proposed in [57]. The method was motivated by the
cooperative communications framework [58], where dis-
tributed transmitter/receiver pairs may experience sig-
nificantly different Doppler distortions, as well as by the
single user scenarios, where distinct Doppler scaling fac-
tors may exist among different propagation paths. The
conventional approach of front end resampling that cor-
rects common Doppler scaling may not be appropriate in
such scenarios, rendering a post FFT signal that is con-
taminated by user and path-specific inter-carrier inter-
ference. To counteract this problem, authors propose a
family of front end receiver structures that utilize multi-
ple resampling (MR) [59] branches, with each matched
to the Doppler scaling factor of a particular user and path.
Following resampling, FFT modules transform the Dop-
pler shift compensated signals into the frequency domain
for further processing through linear or nonlinear detec-
tion schemes. As part of the overall receiver structure, a
Copyright © 2013 SciRes. IJCNS
gradient descent approach is also proposed to refine the
channel estimates obtained by standard sparse channel
4.2. Filterbank Multicarrier for Underwater
In order to combat time dispersion of UWA channels, it
has been proposed to deploy OFDM technique with a
sufficiently long CP. Moreover, to keep the bandwidth
efficiency of the transmission high, long OFDM symbols
that are at least four times of the length of the CP should
be used. Due to small sideband power leakage, filter bank
multicarrier techniques are considered as interesting al-
ternatives to traditional OFDMs for spectrum pooling
Cognitive Radio [60]. This leads to an OFDM system in
which channel variation over each OFDM symbol may
be unacceptably large, thus, results in a significant level
of ICI [61]. For that filter bank multicarrier (FBMC)
systems can be optimized for robust performance in the
doubly dispersive UWA channels [61]. OFDM multicar-
rier losses bandwidth efficiency of the transmission due
to the allocation of 20% of each OFDM symbol to its CP.
This is equivalent of saying the CP length is one quarter
of the length of each fast FFT block in the OFDM system.
Moreover, since the length of CP should be at least equal
to the duration of the channel impulse response, and the
latter is usually very long in UWA channels, very long
symbols is used in the OFDM systems for UWA com-
munications [61]. On the other hand, to avoid ISI, the
prototype filter p(t) designed as Nyquist filter [62,63].
The design method proposed in [64] constructs an iso-
tropic filter according to the equation.
 
ptah t
where are the set of Hermite functions defined as
 
ht t
In [61], it is noted that the presence of channel will
result in a disturbed ambiguity function,
which the null points of
are smeared out. Thus,
it is argued that to design a robust prototype filter, the
constraints on the nulls of the ambiguity function
may be relaxed. Each null point is replaced by a region in
the -plane, wich is termed a null region. It is thus
proposed to design
,pt one should choose to mini-
mize the cost function:
 
,1dd ,dd
vv Avv
 
where k
are sets of positive weighting factors, 0
the region around
0 over which the peak of
remains approximately equal to one, and k
for 1, 2,,,kN
are sets of null regions.
UWA communication method using a class of FBMC
systems was proposed [61]. This class of FBMC systems
was designed to be robust against dispersions in time and
frequency domain. When the Filterbank multicarrier tech-
nique is compared with OFDM, it clearly shows that there
is a wide gap between the performance of FBMC and
OFDM for underwater communication in saving band-
width [61]. For the Single-user communications case
OFDM offers a lower complexity. FBMC offers higher
bandwidth efficiency. For Multi-input multi-output (MIMO)
communications case, OFDM provides full flexibility.
FBMC can be used in certain MIMO setups. Only FMT
can offer the same flexibility as OFDM. But, FMT suf-
fers from the same bandwidth loss as OFDM [65]. FBMC
for underwater communication still have high poor re-
search paper and need more research interest.
4.3. MIMO for Underwater Acoustic Channel
Multi-input multi-output (MIMO) techniques have been
extensively discussed in underwater acoustic communi-
cations to overcome the bandwidth limitation of undersea
channel [66]. Combined with OFDM modulation, MIMO
techniques provide substantial spectral efficiency and rea-
sonable robustness against frequency fading while keep-
ing simple equalizer structure [67-70]. Long acoustic mul-
tipath, however, limits the applicability of MIMO chan-
nel estimation methods that require inversion of a matrix
whose size is proportional to both the number of transmit
elements and the multipath spread. To overcome this
problem, an adaptive algorithm is used [71] that does not
require matrix inversion and operates in a decision-di-
rected manner, thus reducing both the computational
complexity and the overhead. Reduction in complexity
has been sought through selection of significant impulse
response coefficients which results in a reduced-size ma-
trix inversion [72-74]. MIMO-OFDM design consists of
the following key components: 1) Null subcarriers are
inserted at the transmitter to facilitate the compensation
of Doppler shifts at the receiver; 2) Pilot tones are used
for MIMO channel estimation; and 3) An iterative re-
ceiver structure is adopted that couples MIMO detection
with channel decoding [66].
OFDM has a number of desirable features, including
low complexity of implementation and mature technolo-
gies that keep it as the dominant technology for sin-
gle-user (point-to-point) underwater communications.
Moreover, while OFDM can be easily adopted for MIMO
channels, development of MIMO-FBMC systems/net-
works is still nontrivial and may be very limited. Only
FMT, the less bandwidth-efficient member of the class of
FBMC systems, can offer a similar level of flexibility as
Copyright © 2013 SciRes. IJCNS
OFDM in MIMO channels. Therefore, the poor fre-
quency spectra of subcarrier signals in OFDM are the
main issue that limits the applicability of OFDM in some
present and future development of broadband underwater
communication systems. FBMC [75], on the other hand,
is an elegant method scope with that by taking a filtering
approach to underwater multicarrier communications.
5. Underwater Acoustic Channel Estimation
To reduce computation complexity of signal processing
and improve the accuracy of symbol detection, receiver
structures that are matched to the physical-feedback
equalizer is designed first in [76], which rely on an adap-
tive channel estimator for its parameters computation.
The channel estimation complexity is reduced in size by
selecting only the significant components, whose delay
span is often much shorter than the multipath spread of
the channel. This estimation is used to cancel the post-
cursor ISI prior to the linear equalizer involved. Optimal
coefficient selection is performed by truncation in mag-
nitude. The advantages of this approach are the number
reduction in receiver parameters, optimal implementation
of sparse feedback, and efficient parallel implementation
of adaptive algorithms for the multichannel pre-combiner,
fractionally spaced channel estimators and the short feed
forward equalizer filters [77].
Coherent modulation schemes such as phase shift key-
ing (PSK) along with adaptive decision feedback equal-
izers (DFE) for spatial diversity used as an effective way
of communication in such channels [78,79]. However,
the long delay spread and rapid time variation of the
channel often makes this approach computationally too
complex for real-time implementations [80]. Although
the underwater channel has a long impulse response, the
multipath arrivals are often separated. This introduces the
possibility of using a sparse equalizer with tap placement
based on the actual channel response. This can poten-
tially dramatically reduce the number of required taps
and hence leads to a lower complexity, faster channel
tracking and an enhanced performance [81].
In [81], the authors have proposed an algorithm to track
the channel explicitly and determine the tap placement
for the DFE based on this channel estimate. The equa-
lizer and the channel estimator are updated individually
throughout the packet. The channel estimator can also
update either the whole estimation or a set of selected
channel coefficients at one time in batch, depending on
computational and channel considerations [82].
The channel estimation algorithms can cope with spa-
tial diversity by multi-channel combining before equali-
zation. Adaptive estimation is performed using minimum
mean square error as the overall optimization criterion.
The receiver is implemented in a multichannel configu-
ration, which provides the array processing gain neces-
sary for many of the underwater acoustic channels. The
complexity of the detection algorithm is linear in the
number of receive elements and in dependent on the
modulation level of the transmitted signals [83]. DFE
structures may suffer from error propagation due to the
feedback of possible erroneous decisions in the loop.
Hence powerful forward error correction (FEC) codes
are needed to ensure low bit error rate (BER) communi-
cation. Turbo codes are a class of powerful codes that
utilize iterative information exchange between two de-
coders to correct errors. Inspired by this idea, researchers
have developed turbo equalization techniques where it-
erative interactions between the equalizer and a decoder
result in joint estimation, equalization and decoding [84].
A new channel estimation [85] equalization and phase
correction scheme has been developed. The new scheme
estimates the acoustic fading channel without separating
the phase drift and phase rotation for each symbol and
then the SIMO receive signals are equalized and com-
bined. Finally the phase drift/rotation of symbols is cor-
rected per group of symbols using estimated average phase
Authors in [29] considered sparse channel estimation
using subspace methods and compressed sensing on chan-
nels subject to moderate Doppler effects, and extended
the compressed sensing receivers to handle channels with
different Doppler scales on different paths. Channel es-
timation and efficient symbol detection studied in [86]
where the design of state-of-the-art training sequences
and sparse learning via iterative minimization (SLIM)
algorithm was proposed to achieve sparse channel esti-
mation. The authors have developed a conjugate gradient
(CG) based detector, which exploits the diagonalization
properties of the circulant channel matrix to significantly
improve the performance of multi-input multi-output
(MIMO) underwater acoustic communications (UAC).
The UWA communication system under consideration
employs orthogonal frequency division multiplexing
(OFDM) and receiver preprocessing to compensate for
the Doppler effects before channel estimation. First ex-
tend the original homotopic algorithm from real-valued
signals to the complex valued ones. Then propose two
enhancements to the sparse recovery-based UWA chan-
nel estimator by exploiting the UWA channel temporal
correlations, including the use of a first-order Gauss-
Markov model and the recursive least-squares algorithm
for channel tracking. Moreover the authors propose a
scheme to optimize the pilot placement over the OFDM
subcarriers based on the discrete stochastic approxima-
Sparse channel estimation for multicarrier underwater
acoustic communication were proposed in [29]. Based on
the path-based channel model, subspace methods are
Copyright © 2013 SciRes. IJCNS
well-known techniques from the array-processing litera-
ture for the channel estimation problem. Also recent com-
pressed sensing technique is employed to develop some
new methods, namely Orthogonal Matching Pursuit (OMP)
and Basis Pursuit (BP) [87,88]. Based on the continuous
time characterization of the path delays, finer delay has
been suggested to be used of resolution over complete
dictionaries. Also the compressed sensing receivers can
be extended to handle channels with different Doppler
scales on different paths, supplying ICI pattern estimates
that can be used to equalize the ICI [29].
Using extensive numerical simulation and experimen-
tal results, researchers find that, in comparison to the LS
receiver, the subspace methods show significant perfor-
mance improvements on channels that are sparse, but
perform worse if most received energy comes from dif-
fuse multipath. To improve LS estimation, simple win-
dowing and dewindowing technique has been used to
improve the accuracy of an existing basis expansion model
(BEM) and develop a windowed least-squares (WLS)
estimator for doubly-selective fading channels [89]. The
compressed sensing algorithms do not suffer this draw-
back, and benefit significantly from the increased time
resolution using sophisticated dictionaries. When account-
ing for different Doppler scales on different paths, BP
can effectively handle channels with very large Doppler
6. Doppler Shift Estimation for Underwater
Acoustic Communications
In underwater acoustic transmissions, Doppler effects
can be caused by propagation medium movements or by
the relative velocity between the transmitter and the re-
ceiver [12]. It often causes additional difficulties for the
processing of received signals. For instance, underwater
acoustic communications with submarines navigating at
speeds up to 25 knots (12.8 m/s) is challenging, due to
large Doppler range that the receiver must be able to
cope with. Doppler effects result in a compression/ex-
pansion of the transmitted signal [90]. At the receiver
side, it is desirable to remove the effect of these phe-
nomena before any further processing, such as timing
and carrier recovery. A receiver which performs optimal
phase synchronization and channel equalization jointly
has been suggested for underwater communication sys-
tem [91].
Based on cross-correlation among training sequences
located at the beginning and at the end of transmitted
data frame, an algorithm is developed for Doppler shift
estimation [12]. From these training sequences two phase
estimates are calculated: a coarse one and a precise one.
The precise estimation has much smaller ambiguity range
than the coarse one, but it yields higher precision for the
estimated Doppler frequency. Authors have shown that a
convenient combination of the coarse and precise phases
leads to a good Doppler shift estimation within the speed
range under consideration in the particular application
discussed. Frequency-dependent Doppler shifts caused
by the low carrier frequency of the underwater acoustic
channel communication.
Depending on the null subcarrier of the ZP-OFDM,
multi-carrier facilitates Doppler compensation, and the
pilot subcarriers used in channel estimation [17] where
an application of OFDM is investigated in wideband
UWA channels with nonuniform Doppler shifts. To com-
pensate for the nonuniform Doppler distortion, a two-
step approach was used by resampling followed by high-
resolution uniform compensation of the residual Doppler.
Also based on that, a block-by-block receiver is suitable
for fast-varying channels. Based on the availability of
pilot tones in the OFDM transmission, a method devel-
oped to estimate the offset parameters, and a hybrid chan-
nel estimator was then proposed to combine the offset
compensated [92].
Compressed Sensing (CS) can be developed as a method
to solve the channel estimation problems for an UWA
system. Based on the existence of a sparse representation
of the treated signal and an over-complete dictionary
with a set of non-orthogonal bases, in [93] a new type of
channel estimator using the compressed sensing theory is
proposed, leading to a sparse channel estimation from the
highly dependence on Doppler compensation. Instead of
using various compensation approaches, the proposed
index is designed by modeling Doppler shifts as the at-
oms shifts in over-complete dictionary. From the results,
this method improves the mean square error (MSE) per-
formance with lower complexity and hardware cost. In
addition, the method has the additional benefit being less
sensitive to Doppler rate variety.
7. Multicarrier Communication for Image &
Video Transmission over Underwater
Acoustic Channel
Recently, there has been a growing interest in develop-
ment and deployment of image and video transmission
techniques for underwater communication networks for
scientific, environmental, commercial, and military pur-
poses [94,95]. High speed underwater image transmis-
sion capabilities can enable the next generation of un-
dersea expeditions. Efficient image transmission over the
band limited underwater channels relies on two aspects
[96]: 1) Efficient data compression, and 2) Bandwidth
efficient modulation.
The compressed sensing technique [97] generates mini-
mum amount of information necessary for transmission
which makes it useful in underwater communication. The
Copyright © 2013 SciRes. IJCNS
combination of compressed sensing and nonlinear analog
processing can also been employed as joint source and
channel coding [3]. Underwater multimedia sensor net-
works (UMSNs) [98] have been proposed and drawn the
immediate attention in the research community. However,
the practical implementation of these currently designed
and envisioned applications directly depends on reliabil-
ity and quality-aware communication capabilities of the
deployed UMSNs. Comprehensive performance evalua-
tion of error concealment and error correction algorithms
for quality-aware image transmission over UMSNs is
reported in [99].
For high-speed image transmission using Multicarrier
modulation [100,101] MMSE based equalization with the
placement of a pilot symbol for very of three sub-carrier
payload resulted in good performance close to the ideal
performance of the equalization. Set Partitioning in Hie-
rarchical Trees (SPIHT) is an efficient wavelet-based
progressive image-compression technique, designed to
minimize the mean-squared error (MSE) between the
original and decoded imagery used for highly compres-
sion technique [102-105]. Since underwater acoustic chan-
nel suffers from significant bit error rates, some mecha-
nism to protect the encoded image is required.
16-HQAM used as mapper for ZP-OFDM, for unequal
error protection using HQAM modulation technique it
produce highly capability of highly speed image trans-
mission over UWAC. Multicarrier communication sys-
tem can transmit high speed image without equalizer and
also with small overhead forward error correction bits.
System depend on the guard interval, large zero-pad
guard interval of ZP-OFDM system used to avoid infor-
mation loss incurred by the overlap-add operation to im-
prove system performance over underwater acoustic
channels with large Doppler spread [106,107].
The real-time wireless video transmission from an un-
derwater vehicle to a surface platform represents one of
the last milestones in the development of autonomous
systems for ocean exploration and monitoring. The goal
of combining video transmission over UWAC approaches
is to mitigate the gap between the bit rate needed for
video transmission and that supported by the acoustic
channel. Konstantinos Pelekanakis [108] developed a vi-
deo transmission technique over underwater acoustic chan-
nel using coherent single carrier modulation technique
for phase detection. For that a high rate acoustic link for
underwater video transmission was implemented, where
image encoding was accomplished using the JPEG DCT,
scalar quantization and run-length Huffman encoding.
Transmitter processing includes signal constellations.
A system employing variable rate M-QAM techniques
was designed and applied to the experimental data trans-
mitted over a short vertical channel. Excellent results
were obtained at bit rates up to 150 kbps, using modula-
tion methods with bandwidth efficiency as high as 6
bits/sec/Hz. Such rate is sufficient to support real-time
transmission of compressed video. The feasibility of real-
time video transmission over short horizontal acoustic
links was addressed in [109] where the standard MPEG-
4 video compression technique and a wavelet-based me-
thod were combined with acoustic transmission based on
coded OFDM modulation to study the feasibility of video
transmission using an acoustic system for deep-sea oil-
field supervisory control and inspection. The wavelet-
based encoder algorithm includes techniques that deal
with spatial and temporal redundancies in video sequences.
In contrast to MPEG-4 compression, spatial redundancies
are exploited by applying the Discrete Wavelet Trans-
form (DWT) to each of the frames composing the video
sequence. A motion compensation algorithm [110] to
reduce temporal redundancies has been incorporated,
which is a core functionality of the MPEG-4 encoder.
The codec is based on the popular Differential Pulse
Code Modulation (DPCM) model, which is widely used
in video compression standards. Its main components are
the DWT multilevel decomposition of the input frame
using the bi-orthogonal wavelet, quantization of the
DWT coefficients using Vector Quantization (VQ). The
images are smoother due to the high quantization ratio
for the high subbands of the wavelet decomposition and,
as expected, no blocking effect is noticed at all [109].
8. Summary & Conclusion
In this paper we surveyed multicarrier communication
techniques used for data transmission over the underwa-
ter acoustic channel up to as the major aspects can be
summarized as: 1) Underwater channel model and its
challenging in multicarrier communication modulation
like OFDM and FBMC summarized and detail reviewed;
2) The advances of underwater acoustics technology ap-
plications and areas emerged in; 3) Channel characteris-
tics and channel models in reducing noise and channel
noise estimates; 4) Multicarrier modulation and multi-
input multi-output techniques detailed description for un-
derwater communication; 5) Doppler estimation and un-
derwater channel estimation challenging discuses and tech-
niques used evaluated and summarized; 6) Performance
evaluation for different channel coder used for underwa-
ter acoustic multicarrier modulation; 7) Changing and
used techniques for high speed image and video trans-
mission over UWAC also summarized and discussed.
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