Review Article: Multicarrier Communication for Underwater Acoustic Channel

doi:10.4236/ijcns.2013.68039

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Int. J. Communications, Network and System Sciences, 2013, 6, 361-376

http://dx.doi.org/10.4236/ijcns.2013.68039 Published Online August 2013 (http://www.scirp.org/journal/ijcns)

Review Article: Multicarrier Communication for

Underwater Acoustic Channel

Hamada Esmaiel, Danchi Jiang

School of Engineering, University of Tasmania, Hobart, Australia

Email: hamada.esmaiel@utas.edu.au, Danchi.Jiang@utas.edu.au

Received June 24, 2013; revised July 22, 2013; accepted July 26, 2013

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

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-

culties.

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.

H. ESMAIEL, D. C. JIANG

362

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)

[6].

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

Seawater

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)

where



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

H. ESMAIEL, D. C. JIANG 363

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)







10log507.520log40log0.4,

Nff f





(5)



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 [





9]:

 





tswth

NfNf Nf NNf



(7)

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

 (8)

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

1.5,k0.5,S0,





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

[9]:

log 1,

CB N













(9)

where is the channel bandwidth in Hz and

BSN

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]:



log1d.













(10)

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



which

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 [

l9]:





logd .

Al fNfBl









 (11)

The choice of the underlying absorption coefficient

model imposes a dependence of the capacity on depth,

temperature, salinity and acidity as well.

H. ESMAIEL, D. C. JIANG

364

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 ,



de-

pending on the relative velocity and the ratio between

the carrier frequencyc

and the symbol rate 1RT



[12] caused in the received signal as:

2π2π

Rc v





 

 (12)

From [13,14], UWAC multipath representation for mul-

tipath arrival p is characterized by its mean magnitude

gains



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,

and



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











 (13)

The delay for path given by

,ppp

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:



2.75

24 15

ebobo d.

kENENxx











(14)

where

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









,



(15)

between the channel impulse response



,ht



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









(16)

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:





ppp

ct Aat



















(18)

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

H. ESMAIEL, D. C. JIANG 365

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

n1,nq





and t is the power correcting code. So the

number of control symbols is







2.t

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,

TT KM





 (19)

kb s.RB



 (20)

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 [

28],

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

H. ESMAIEL, D. C. JIANG

366

“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

Multiplexing

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

[4,17,36,37].

4.1.1. ZP-OFDM for Underwater Acoustic

Communication

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



 

,

(21)

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 [

30]:









2Ree,0 ,

jft

tskgtt

















 (22)

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

jfbt

jmk bt

zt A

skgb t



















(23)

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.

H. ESMAIEL, D. C. JIANG 367

where

b represents the residual Doppler rate satisfy-

ing:





 (24)

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

Transmission

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

[29].

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

efficiency.

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-

H. ESMAIEL, D. C. JIANG

368

Figure 4. TDS-OFDM system for underwater acoustic

channel.

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, ,

isi









 













, (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

sub-channel,

ith 2

is the sub-channel power, 2



is the ambient noise power and it’s a constant of

sub-channel, and ii

ith



is the or carrier-to-noise

ratio

SNR





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.



SNR

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

H. ESMAIEL, D. C. JIANG 369

gradient descent approach is also proposed to refine the

channel estimates obtained by standard sparse channel

estimators.

4.2. Filterbank Multicarrier for Underwater

Communications

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



 (27)

where are the set of Hermite functions defined as

[



61]:

 

nnn

ht t





(28)

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

pkp

vv Avv



 









(29)

where k



are sets of positive weighting factors, 0

the region around









,0,v



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

H. ESMAIEL, D. C. JIANG

370

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

drift/rotation.

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-

tion.

Sparse channel estimation for multicarrier underwater

acoustic communication were proposed in [29]. Based on

the path-based channel model, subspace methods are

H. ESMAIEL, D. C. JIANG 371

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

spread.

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

H. ESMAIEL, D. C. JIANG

372

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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