Mô hình đặc tính kênh truyền cho thông tin thủy âm vùng nước nông

MINISTRY OF EDUCATION AND TRAINING
HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

DO VIET HA

MÔ HÌNH ĐẶC TÍNH KÊNH TRUYỀN
CHO THÔNG TIN THỦY ÂM VÙNG NƯỚC NÔNG
CHANNEL MODELING FOR SHALLOW
UNDERWATER ACOUSTIC COMMUNICATIONS

DOCTORAL THESIS OF TELECOMMUNICATIONS ENGINEERING

HA NOI - 2017


INTRODUCTION

1. Overview of the Dissertation
Underwater acoustic (UWA) communication systems have been developed for the past three decades [25]. They can be used in potential applications such as environmental monitoring, offshore oil exploration, and
military missions. Nevertheless, UWA communications have a plethora
of difficulties, so they display many challenges for further developments.
The reason can be explained by a large demand on high frequency utilization as well as high data rate access under very complexity shallow
underwater environments. All these requirements, without doubt, call
for intensive research efforts on how to cope with problems faced by
current UWA communications, e.g., limited availability of acoustic frequency spectrum, complex time variations in UWA fading channels, and
urgent needs for good quality of service. Therefore, this dissertation is
devoted to investigate UWA communication systems by considering all
these challenges. In particular, two goals are aimed at, which are known
as: i) UWA channel modeling and ii) performance analysis of UWA
communication systems
The design, development, performance analysis, and test of such communication systems, however, call for a deep insight of the most important characteristics of real-world propagation environments. Similar to

the other communication fashions, channel modeling is an initial investigation because it provides hints to predict performance of communication systems before doing further high cost implementations as hardware
designs [75, 96]. The task of channel modeling is to reproduce the real
channel conditions. In other words, the statistical properties of the real
channel such as path loss, multipath fading and Doppler effect should
be represented by channel modeling. For this reason, this dissertation
presents the analysis and modeling UWA channels in shallow water en1


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vironments, which have strong multipath and Doppler effects on signal
propagations [97].
Without discussing the performance of UWA communication systems
under different propagation environments, this study seems to be unfinished. In this view point, Orthogonal Frequency Division Multiplexing
(OFDM) has been widely applied to acoustic transmission [10, 22, 42, 68,
86] since it can mitigate inter-symbol interference as well as has higher
spectral efficiency than single carrier systems. Thus, for the sake of
completeness, we utilize analyses such as the signal-to-interference ratio
(SIR), the signal-to-interference-plus-noise ratio (SINR), and the channel capacity to determine the performance of the UWA-OFDM systems.
We believe that the performance assessment reported here bridges the
gap between the derived UWA channel models and their impact on the
performance of the deployed UWA communication systems.
This chapter presents the general concepts in UWA channel modeling and a brief introductions to UWA channel characteristics. Moreover,
the motivations and the major contributions of this dissertation are highlighted in the remainder of this chapter.
2. Characteristics of Shallow Underwater Acoustic Channels
The physical characteristics of UWA propagation environments are very
different from those of terrestrial ones with electromagnetic waves. UWA
channels can be characterized by three main aspects [12, 30, 88]: the high
transmission loss depending on signal frequencies, the time-varying multipath propagation, and the low transmit speed of sound in water (about
1500m/s). The fast time variations of UWA channels are mainly caused

by the relative movement [88], internal waves [32], and surface waves
[16, 82]. These features make UWA channels the most difficult communication media in use today [88], and give rise to critical challenges for
further developments.
Acoustic Frequency
The frequency of underwater acoustics is in the range from 10 Hz to
1 MHz [92]. When the bandwidth is between 10÷20 percent of the center
of signal, the communication system is called wide-band. Although the


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bandwidth of UWA communication systems is small, the signal frequency
is also small. Thus, UWA communication systems are wide-band due to
the low relative center frequency in comparison with the bandwidth [88].
Transmission Loss
The transmission loss of UWA propagation significantly depends on
the signal frequency. The three factors that attenuate UWA signals
include spreading loss, absorption loss, and scattering loss. The overall
path loss A (l, f ) is defined as [88]
l k
l−l
A (l, f ) =
α(f ) r ,
lr
where f is the signal frequency and l is the transmission distance, taken
in reference to some lr . The symbol k is the path loss exponent, which
model the spreading loss and its value are usually between 1 and 2.
The absorption coefficient α(f ), which increases rapidly with signal frequency, can be obtained using an empirical formula [20].
Noise
Noise in UWA channels consists of ambient noise and site-specific

noise. Ambient noise is always present in the background of the sea,
while site-specific noise is unique to certain places. The first one is
often modeled as Gaussian and it is not white, while the latter one contains significant non-Gaussian components. The power spectral density
of ambient noise decays at a rate of approximately 18 dB/decade. The
attenuation growing with frequency whereas the noise decays with frequency, result in a signal-to-noise ratio (SNR) that varies over the signal
bandwidth [88].
Propagation Delay
The speed of UWA waves increases with the salinity, temperature, and
pressure of the water. In shallow water environments, the temperature
and pressure are almost unchanging; thus, the speed of sound in shallow water is considered to be a constant value (about 1500 m/s). The
propagation delay τ can then be obtained as
d
τ=
c
where d and c are the propagation distance (in meters) and the speed


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of sound (in m/s), respectively. Because of the low speed of sound, the
propagation delay τ = d/c is about tens milliseconds for transmission
distances of longer than ten meters.
Multipath
In shallow water environments, the propagation of sound appears to
be a complicated multipath, which is mainly caused by reflections at the
surface and bottom. The multipath interference in UWA communication
systems is illustrated in Fig. 1. Each path has its propagation delay
depending on its geometry. The maximal propagation delay is called
as the delay spread of the UWA channel. Because of the multipath
effect, the received signal is composed of various paths with different

amplitudes, propagation delays, and phase shifts.

Figure 1: Multipath interference in UWA communication systems.

Doppler Effect
Another characteristic of UWA channels is time varying, which is
caused by two factors. The first one is the result of the relative movement
between the transmitter (Tx) and the receiver (Rx), while the latter one
is caused by inherent changes in the transmission medium such as the
changes in weather, surface wave, and storm, etc [9].
A relative movement between Tx/Rx or a moving medium results in
the change of frequency of the acoustic waves, which is called as Doppler
shift. An expression for the maximum Doppler frequency shift fD,max is
given by [19]
v
fD,max = fc ,
c


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where fc and c are the transmitted signal frequency (i.e. carrier frequency) and the sound speed, respectively; v stands for the speed of the
observer.
The magnitude of Doppler effect is determined by the ratio a = v/c
named as the relative Doppler shift, which is significant to the carrier
frequency due to the low speed of sound. The non-negligible Doppler
shift is a distinctive characteristic of UWA channels in comparison with
the radio channel.
Moreover, even without intentional movements, the inherent changes
in transmission medium such as waves or drifts of transducers also lead

to the Doppler shift. In shallow water environments, reflections from the
surface are the main reason of time-variant UWA channels. The Doppler
spread presents the spectral width spreading of the received signal, which
depends on the wave height, wind speed, reflections from the surface and
bottom of the sea.

3. UWA Channel Modeling Approaches and the State-of-the-Art

The characteristics of UWA channels are very complex due to Doppler
effects, high attenuations depending on signal frequencies, multipath effects, and additive color noises. Therefore, it is very difficult to model
exactly UWA channels, especially in shallow underwater environments,
which have strong multipath effects on the signal. UWA channel modeling is not new research in underwater communication systems. However,
over the past few decades, although large variety of UWA channel models
have been proposed, there is still no typical model that can be applied for
all UWA channels because of differences in geographical areas, weather
conditions, and seasonal cycles [24, 70, 73, 88, 93, 96]. Recent approaches
of designing UWA simulators in literatures are classified into two main
categories, which are the geometry and the measurement-based.
The UWA geometry-based simulator has been designed by using the
geometrical channel model. The well-known Bellhop code [69] is one of
popular examples of this simulator. The code built the UWA channel
simulator by using the ray theory for a given geometry, but it did not


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consider the random channel variation [75]. To deal with this issue,
some studies run the Bellhop model in combination with environment
conditions, such as temperature and salinity [89], wind speeds [28], and
surface shapes [37]. The simulated UWA channels obtained through

such Bellhop channel simulator showed the statistical properties that
are similar to those of the real UWA channels in some experimental
scenarios. The difficulty of specifying the environment conditions is one
of the limitations of this simulator.
Another kind of the UWA geometry-based simulator is developed by
combining the ray theory with statistical methods to describe the UWA
propagation environment [13, 17, 27, 55, 73, 75, 103, 104]. The statistical properties of the UWA channel were analyzed by using the probability density function (PDF) of the angle-of-arrival (AOA), and the
angle-of-departure (AOD) as key parameters. The AOD is, however,
a derivative parameter of the AOA [55]. In some research studies, the
PDFs of the AOAs are assumed to be normally [103, 104] or uniformly
[75] distributed. Besides, in [13], the author approximated the PDF of
AOA with the half-circular Rice PDF. The geometry-based simulator
can describe the overall UWA channel with fewer estimated channel parameters than the measurement-base one, and it is feasible to extend
from one transmission environment to others without significant efforts.
However, the geometrical modeling is not able to provide the statistical
characteristics of the simulated channel, which is close to those of the
real UWA channel. This is because of the time and spatially varying
characteristics of the shallow UWA propagation environments.
The UWA measurement-based channel modeling approach have been
investigated in [24, 74, 76, 85, 105]. Almost all of these channel simulators are developed from given measurement data, which are obtained
from a specific underwater environment. Based on analyzing the measurement data, the distribution of the propagation paths are specified
such as Rayleigh [24, 85], Rician [76], K-distributed [105], and lognormal [74]. Furthermore, in the replay-based simulators [58, 83, 95],
the time variant channel impulse responses (TVCIRs) of the measured
UWA channel can be reproduced; or a new random TVCIR can be gener-


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ated so that its statistical properties are similar to those of the measured
channel. The measurement-based simulator does not require physical input parameters, which may not be easy to set. In addition, the simulated

channels obtained by this simulator are extremely realistic based on actual measurement data. The disadvantage of the measurement-based
simulator is that it can be only applied to the specific transmission environment, where the channel is measured. The best way to expand the
diversity of this simulator is to collect a large amount of measurement
data at different time and locations [84]. Moreover, for designing the
measurement-based channel simulator, a large number of channel parameters, including path gains, Doppler frequencies, propagation delays,
and phase shifts need to be estimated [56]. There are some efficient
computation algorithms to estimate these parameters, such as the rotational invariance techniques (ESPRIT) [34], the space-alternating generalized expectation-maximization (SAGE) [33], the iterative nonlinear
least square approximation (INLSA) [31], the Lp -Norm Method (LPNM)
[59]. The measurements and computation efforts to estimate the large
number of channel parameters make the measurement-based simulator
more complex than the geometry-based one.
In Vietnam, despite of a growing need of UWA communication applications in the military and commerce, there is not many research
papers on UWA communication, especially in the field of channel modeling [2, 3, 6]. Some characteristics of UWA propagations in Vietnam sea
have been investigated in some earlier research [1, 4, 5, 7, 8]; however,
the results of UWA channel modeling have not been given. In [6], the authors have simulated the UWA propagation rays by solving the Eikonal
equation for given environmental conditions. As mentioned above, these
environmental parameters are hard to be specified due to the complexity
of UWA propagation environments. Besides, the simulated UWA propagation rays is time-invariant that may not be able to describe the real
UWA channel in most of cases.
4. Goals of the Dissertation
This dissertation aims at developing accurate and efficient approaches of


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designing shallow UWA channel simulators based on the measurement
data of the real UWA channel in a specific shallow water environment.
The proposed approaches should fulfill the following requirements:
• They should enable the accurate simulation of the shallow UWA
channel characterized by the measured channel impulse response

(CIR), power delay profile (PDP), and/or Doppler power spectrum.
• Determinations of the channel simulation model parameters should
be done in a simple and efficient manner.
• The simulators designed by the proposed approaches should be suitable for the performance analysis of the UWA communication system based on Orthogonal Frequency Division Multiplexing (OFDM)
technique.
To accomplish these goals, two simple and effective approaches are
proposed for the design of UWA channel simulators for the two cases:
(i) Fixed transmitter (Tx) and receiver (Rx) and (ii) Fixed Tx and
Rx moving. The simulation results show that the proposal of design approaches emulate the statistical properties of the measured UWA channels with high accuracy.
Furthermore, the statistical properties of UWA channels in terms of
Doppler power spectral densities (PSDs) is also the objective of this dissertation. In this respect, we present a thorough analysis of Doppler
effects of shallow UWA channels having a time-variant surface motion
and relative Tx/Rx movement. As a result, the closed-form expression
of Doppler power spectrum is proposed and validated through the measurement data.
Using the measurement-based UWA channel simulation model, a detailed analysis on the performance of UWA-OFDM communication systems was presented; then, appropriate transmission parameters such as
the signal bandwidth, the number of sub-carriers, and the transmit power
would be selected.
5. Scope and Delimitations


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The scope of the dissertation is for the approaches of designing shallow UWA channel simulators. The design of measurement-based UWA
channel simulators, which are derived from the measurement data of the
real UWA channel in a specific shallow water environment, is mainly
focused. The aspects look into were the multi-path and Doppler effects
of the measured shallow UWA channels. The measurement results have
been used for the input data of the simulators. All parameters of the
UWA channel simulation model are then derived from the measurement
data without considering the physical aspects of the acoustic wave propagation. Therefore, the obtained UWA channel simulation model is just

valid for the specific transmission environment that the UWA channel is
measured.
The dissertation does not cover the analysis of effects of geometrical or environmental parameters (e.g., the water depth, the salinity, the
temperature, etc...) on the measured UWA channel. The measurement
data itself reflects the influence of these parameters. Furthermore, the
study only concentrates on the shallow environments; thus, the channel
simulation model used in this dissertation does not capture the characteristics of UWA channels in the deep water.
6. Motivations and Contributions of the Dissertation
In the previous section, the complexity of UWA channels has been discussed. Furthermore, there is still no typical model that can be applied
for all UWA channels because of the differences in geometrical and environmental conditions. This implies that we have to consider the UWA
channel characteristics specific to each different environments and locations. Hence, the studies of UWA channels and their characteristics
such as path loss, delay and Doppler spread have been paid much attention for implementing UWA channel simulators as well as real UWA
communication systems.
For the design of UWA channel simulators, we can implement the previously mentioned approaches, geometry-based and measurement-based
ones. Each approach has its own advantages and disadvantages. The
performance of each approach is analyzed by comparing the statistical


10

properties of the simulated channels with those of the measured UWA
channel in a shallow water environment. The simulation results show
that the measurement-based channel simulator provides the simulated
channel which matches well with the measured UWA channel, but it
requires application of complex optimization computation methods to
estimate a larger number of channel parameters. The geometry-based
simulator has a lower complexity than the measurement-based simulator. However, the statistical properties obtained by this simulator do not
fit with those of the measured UWA channel. It motivates us to propose
an effective channel simulator, which is not only simple in computation
but also in good agreement with the measured UWA channel.

Moreover, for the purpose of design and performance analysis of UWA
communication systems, the statistical properties of UWA channels in
terms of correlation functions, Doppler power spectral densities (PSDs),
and power delay profiles (PDPs) need to be analyzed [55, 56]. As the
matter of fact, many research papers have investigated the power delay
profile (PDP) of UWA channels, but the modeling of the Doppler spectrum has been less well developed [94, 101]. Despite the significant role
of the Doppler power spectrum in designing UWA channel simulators
and in evaluating the UWA communication system performance, studies
on modeling it are still lacking in the literature. In UWA communication
systems, especially in shallow water environments, the Doppler effect is
a severe problem because of the fast-time variant process of the seasurface moving that results in unpredictable Doppler shifts [9, 21]. This
is our motivation to propose a Doppler power spectrum model for shallow UWA channels. In this dissertation, the Doppler effects in shallow
UWA communication systems is investigated in consideration of both
the Doppler components caused by the surface motion and by the transmitter/receiver movement. Then, the Doppler power spectrum model
was proposed and validated using the Doppler measurement results of
the shallow UWA channel.
Another important task of underwater communication study is to analyze and verify performance of systems. To mitigate inter-symbol interference (ISI) due to the large delay spread, Orthogonal Frequency


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Division Multiplexing (OFDM) modulation has been widely applied to
acoustic transmission [10, 22, 42, 68, 86]. In OFDM systems, the transmission bandwidth is divided into many narrow sub-channel. This makes
the symbol duration to be increased and then the inter-symbol interference (ISI) to be reduced but the inter-channel interference (ICI) caused
by the Doppler effect becomes more serious [47]. In UWA-OFDM systems, the Doppler effect is much more severe because of following reasons: the sound speed is low and varying, the system is inherently wideband [88], the Doppler frequencies are comparable to carrier frequencies,
and the Doppler effects are unequal over sub-carriers [9]. The frequency
of the transmitted signal is significantly distorted by the Doppler effect [21]. The motion-induced distortion has far-reaching implication for
the synchronization design and the channel estimation algorithms [88].
Unlike other research studies that analyzed the ICI effect based on the
assumption of Doppler spectrum such as classical, uniform or two-path

[9, 22, 47, 86], we consider the ICI effect on the measurement-based UWA
simulator which is a wide-band shallow UWA channel model derived from
the measurement data. Moreover, the ambient noise from sources such
as turbulence, waves, shipping, and rain is frequency dependent and not
white noise [88]. Most of the research studies have not evaluated noise
in combination with ICI effect or they considered noise as white noise
[9, 22, 86]. This calls for the needs of evaluating the UWA-OFDM system under the effects of both ICI and ambient noise. In this dissertation,
we focus on both the ICI effect and ambient noise on UWA-OFDM systems. Based on the measurement-based UWA channel, we present an
exact analysis of the ICI of UWA-OFDM systems. Consequently, important parameters of the system are evaluated: the transmit power, ICI
power, noise power and the resulting signal-to-interference ratio (SIR),
signal to interference plus noise ratio (SINR), and channel capacity.
This dissertation deals with the analysis of UWA channel modeling
approaches and UWA-OFDM system performance. In this regard, the
major contributions of this dissertation are summarized as follows:
1. The two typical approaches of designing shallow UWA channel sim-


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ulators, the geometry-based and measurement-based ones, are investigated. The performance of each simulator is then analyzed by
comparing the statistical properties of the simulated channels with
those of the measured UWA channel in a shallow water environments. Thereafter, as presented in [J2], an effective approach for
designing shallow UWA channel simulators, which is not only simple in computation but also in good agreement with the measured
UWA channel, has been proposed. Furthermore, the geometry-based
UWA channel model is used in [C1], while the measurement-based
one is addressed in [J1],[J2].
2. Theoretical background analysis of Doppler effects generated by the
transmitter/receiver (Tx/Rx) movement, and by the motion of seasurface is presented. Sequentially, a closed-form expression of Doppler
power spectrum model for shallow UWA channels has been proposed
and validated through curve fitting with the Doppler power spectrum

measurement results of real shallow UWA channels. The detail of
this contribution is presented in [J3].
3. The ICI plus ambient noise analysis for UWA-OFDM systems over
the measurement-based channel simulator for a real shallow UWA
channel is utilized. The topic studied in depth includes the exact calculation of ICI power, ambient noise power and appropriate transmit
power, as well as their effects on performance of the UWA-OFDM
system. Signal to interference ratio (SIR), signal to interference
plus noise ratio (SINR), and capacity performance are evaluated for
different parameters, including signal bandwidths, number of subcarriers, and the transmit power. These parameters should be chosen carefully in order to obtain the desired capacity and SINR with
minimizing the ICI effect. The results provide practical guidelines
for choosing proper transmission parameters for UWA-OFDM systems. The paper [C1] deals with the SINR analysis, while different
performance studies of UWA-OFDM systems are presented in [C2],
[J1].
7. Organization of the Dissertation


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This dissertation deals with the derivation and analysis of designing shallow UWA channel simulators. The topics studied in depth include the
approaches of modeling shallow UWA channels, Doppler power spectrum
based on the measurement data. Using the measurement-based UWA
channel simulator, the UWA-OFDM system performance is analyzed.
Consequently, the system parameters can be optimized for the design of
UA-OFDM systems using the simulation results. The structure of this
dissertation is as follows:
• Chapter 1 presents the two approaches of designing shallow UWA
channel simulators, the geometry-based and measurement-based ones.
This chapter is dedicated to review the main advantages and disadvantages of each one. Moreover, we also discuss simulation channel
models, namely SOS (sum-of-sinusoids) and SOC (sum-of-cisoids),
which can be used in modeling UWA channels. Therein, the peculiar

characteristics as well as advantages of employing the corresponding
UWA channel models are also highlighted. Specially, this chapter
proposes an effective approach of designing shallow UWA channel
simulators that its implementation is simple in computation; however, it outputs the simulated channel with statistical properties
close to the reality.
• Chapter 2 The proposal for a closed-form expression of Doppler
power spectrum model for shallow UWA channels has been presented. Using the geometry model for shallow UWA channels, the
theoretical background of Doppler effects generated by the transmitter/receiver (Tx/Rx) movement, or by the motion of sea-surface
has been analyzed. As a result, the Doppler power spectrum can be
modeled as a summation of the Spike-shape and the Gaussian-shape.
The Spike-shape presents the Doppler component from the Tx/Rx
movement, while the Gaussian-shape presents the Doppler component from the sea-surface motion. The proposed model is validated
through curve fitting with the Doppler power spectrum measurement
results of a real shallow UWA channel.
• Chapter 3 This chapter utilizes the ICI plus noise analysis UA-


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OFDM systems over the measurement-based channel models for
shallow underwater acoustic channels. We carry out the exact calculation of ICI power, ambient noise power and appropriate transmit
power, as well as their effects on performance of the OFDM system. Signal to interference ratio (SIR), signal to interference plus
noise ratio (SINR), and capacity performance are evaluated for different signal bandwidths, number of sub-carriers, and the transmit
power. Based on the simulation results, the system parameters can
be optimized for design of UA-OFDM systems.


Chapter 1
DESIGN OF SHALLOW UWA CHANNEL SIMULATORS

In this chapter, two typical approaches of designing underwater acoustic
(UWA) channel simulators, the geometry-based and the measurementbased ones, are investigated. Then, the performance of each one is analyzed by comparing the statistical properties of the simulated channels with those of the real measured shallow UWA channel. The results show that the geometry-based simulator has a lower complexity
than the measurement-based simulator; however, the statistical properties obtained by this simulator are in general not realistic and have a
limited confirmation by measurements. It is noteworthy that the designed channel models using this approach can be utilized for parameter
studies [55]. This is because they can be extended without significant
effort when changing some parameters such as the water depth, the
transmission distance, the signal frequency, and so on. On the contrary,
the measurement-based channel simulator provides the simulated channel which matches well with the real measured UWA channel, but it
requires application of complex optimization computation methods to
estimate a larger number of channel parameters. This is our motivation
to propose an effective approach of designing UWA channel simulators,
which is not only simple in computation but also in good agreement with
the real UWA channel. The parameters of the proposed simulator can be
directly exploited from the measurement data without applying any optimization computation method. Moreover, the simulation results show
that the channel statistical properties obtained by the proposed simulator match well with those of the real measured UWA channel.

15


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1.1. Introduction
The shallow underwater acoustic channel can be characterized as a multipath fading channel. Low propagation speed (1500 m/s) leads to UWA
channels with a long multipath delay spread that causes strong frequency
selectivity. Time-varying multipath fading observed in the radio channels is present also in the acoustic channels and is intensified in shallow
waters due to the strong surface and bottom reflections [13]. The channel typically has significant Doppler spread due to the low propagation
speed, even when the transmitter/receiver are not moving fast.
In order to design, analyze, and simulate underwater communication
systems, efficient modeling of the acoustic channel is essential. The
two main approaches of designing UWA simulators, the geometry and

the measurement-based ones, are depicted in Fig. 1.1 and Fig. 1.2, respectively. It shoud be mentioned that, these figures only illustrate the
methodology behind the design of shallow UWA channel simulators with
the simplified propagations, not a real shallow UWA channel.
The methodology behind the geometry-based channel modeling is illustrated in Fig. 1.1. The UWA geometry-based simulator is designed by
using the geometrical channel model. In this case, the reference model is
developed by performing an ensemble averaging over an infinitely large
number of scenarios [17, 55, 62]. Parameters of the reference model such
as path loss, delay and Doppler spread can be derived from the geometry model and physical laws of UWA propagation environments. It is
noted that the reference is non-realizable because of an infinite number
of scatterers. Therefore, a simulation model can be derived by approximating statistical properties of the reference model with a finite number
of scatterers. The main advantage of this approach is the computational
simplicity and it can be utilized for parameter studied [55]. However,
the simulation results show that its simulated channel is not realistic.
That is because parameters of the model are hard to determine exactly
under the real UWA propagation conditions.
The methodology behind the measurement-based approach (see Fig. 1.2)
is that the channel simulator are derived from the measurement data of


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Figure 1.1: The methodology behind the geometry-based channel modelling [17, 55].

the particular scenario [24, 74, 76, 85, 105]. By approximating the statistical properties, parameters of the simulators such as path gains, delays
and Doppler frequencies are obtained. The simulated channel of this simulator is highly realistic but only valid for the specific scenario on which
the measurement data have been taken. Furthermore, the effort in field
measurements and in computations of a large number of the simulation
channel parameters are major disadvantages of this approach.
Due to lack of standardized models that can meet all requirements
of UWA channel modeling, the choice of modeling approaches depends

on the application purposes. Namely, for the design and comparison
purposes, one should use the geometry-based simulator; whereas, the
measurement-based one can be a better choice for optimization and deployment of UWA communication systems.
In this chapter, first of all, simulation models, namely SOS (sum-ofsinusoids) and SOC (sum-of-cisoids) methods [38, 102], can be used to
model the UWA channel will be discussed. The SOS method generates
the channel simulation model by summing a finite number of sinusoids,
whereas the SOC one models the channel by a finite sum of complex sinusoids (cisoids). The SOS method generates the channel simulation model
by summing a finite number of sinusoids, whereas the SOC one models
the channel by a finite sum of complex sinusoids (cisoids). Then, both


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Figure 1.2: The methodology behind the measurement-based channel modelling [31,
56].

approaches of designing UWA channel simulators, the geometry-based
and measurement-based ones, are evaluated. The performance of each
approach is verified by comparing its statistical properties with those
of the real UWA channel, which is measured in Halong bay, Vietnam.
Furthermore, an effective approach to design UWA channel simulators is
proposed for two different cases. In the first case, the transmitter (Tx)
and receiver (Rx) are fixed; whereas the Rx is moving in the latter one.
All or a part of the channel parameters of the proposed simulators can be
directly exploited from the measurement data of the real UWA channel
without applying any parameter computation method; thus, its complexity is significantly reduced. Moreover, the simulation results show
that our proposed simulator provides the statistical properties close to
those of the measured UWA channel.
The rest of this chapter is organized as follows. Section 1.2 presents
overview of simulation models that can be used to model UWA channels.

The design of the geometry-based UWA channel simulator is described
in Sect. 1.3. In Sect. 1.4, the steps to design the measurement-based
UWA channel simulator are presented using the measurement data of
the real shallow UWA channel in Halong bay, Vietnam. Sections 1.5 and
1.6 describe the proposed approaches to design UWA channel simulators
for two different cases, fixed transmitter/receiver and moving receiver.


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Furthermore, their performance analysis in comparison with state-ofthe-art is discussed. Finally, Sect. 1.7 highlights the specific findings of
the chapter.
1.2. Overview of Simulation Models for UWA Channels
In this section, fading models, which are widely used in the communications community, will be discussed. These fading models represent
the characteristics of electromagnetic transmission over the air, both for
narrow-band and wide-band scenarios, and often successfully. Therefore,
they will be evaluated to model the distortion caused by the water in
underwater acoustic communications. To capture the propagation characteristics of shallow UWA environments, parameters of these models
will be determined by the physical laws of UWA propagation environments or the measurement data of real shallow UWA channels.
1.2.1. Rayleigh and Rice channels
Rayleigh and Rice channels are the most popular channel models used
in wireless communications. This is because of the fact that they are
easy and effective descriptions of fading channels for investigations using
computer simulations. In multipath propagation environments, due to
diffraction, reflection, and scattering, the received signal is composed of
many different components, which are differences in amplitudes, phases
and delays. A narrowband fading channel is often modeled by a complex
Gaussian process µ (t) = µ1 (t) + jµ2 (t), where µ1 (t) and µ1 (t) are realvalued Gaussian processes. The Rice method is widely applied to model
the real-valued Gaussian process µi (t) (i = 1, 2), which is expressed as
Ni

µi (t) = lim

Ni →∞

ci,n cos (2πfi,n t + θi,n ) ,

(1.1)

n=1

where ci,n , fi,n , and θi,n are the gains, the frequencies, and the phases.
For the case of having the LOS component at the receiver, a timevariant deterministic process m(t) will be introduced to represent the
LOS component, i.e. m (t) = ρej(2πfp t+θp ) , where ρ, fp , and θp denote the
amplitude, the Doppler frequency and the phase of the LOS component,


20

respectively. Consequently, the complex Gaussian process for the LOS
case is expressed as µp (t) = µ(t) + m(t). By taking the absolute value of
µp (t), the Rice process is obtained as
ξ (t) = |µp (t)| = |µ (t) + m (t)| .

(1.2)

In a worst case fading scenario, the Rayleigh process is represented by
ζ (t) = |µ (t)| = |µ1 (t) + jµ2 (t)| .

(1.3)

As described in Eq. 1.1, the Rice and Rayleigh processes are the summation of an infinite number of harmonic functions; thus, it is non-realizable
for implementing a fading channel using their processes.
1.2.2. Deterministic SOS Channel Models
For simulation purposes, it is not necessary to take into account an infinite number of components. As proofed in [62, 64], the desired statistical
properties can be described by a limit number of harmonic functions.
This is called as a deterministic channel modeling principle. Using that
principle, the real-valued Gaussian process is represented by a finite sumof-sinusoids (SOS)
Ni

µ
˜i (t) =

ci,n cos (2πfi,n t + θi,n )

(1.4)

n=1

Sequentially, in the deterministic SOS model, the complex Gaussian process is represented by
µ
˜ (t) = µ
˜1 (t) + j µ
˜2 (t) .

(1.5)

Channel simulation models based on Rices SOS principle [14, 54] have
widely been in used in designing multipath radio channel simulators
(e.g., see [15, 23, 43, 51, 81]). In the SOS channel simulation approach,

the in-phase µ1 (t) and quadrature µ2 (t) (IQ) components of the channels
complex envelope are assumed to be uncorrelated. Under this consideration, it can only be used to simulate fading channels having symmetrical
Doppler power spectral densities (DPSDs). In other words, the SOSbased approach is only applicable to model channels under isotropic


21

scattering conditions. Hence, it can not be used to model the more realistic case of channels characterized by asymmetrical DPSDs, such as
the shallow UWA channels.
1.2.3. Deterministic SOC Channel Models
The SOC (sum-of-cisoids) model, where the Gaussian process is approximated by a finite sum of complex sinusoids (cisoids) [78], is a solution to
overcome the limitation of the SOS channel simulation approach. In the
deterministic SOC model, the complex Gaussian process can be modeled
and efficiently simulated as
N

cn ej(2πfn t+θn ) .

µ
˜ (t) = µ
˜1 (t) + j µ
˜2 (t) =

(1.6)

n=1

The IQ components of SOC models are characterized by the same set of
parameters [78], that is a basically different from the SOS models. This
feature makes SOC models be able to simulate fading channels having

both symmetrical and asymmetrical DPSDs [40, 64, 71, 79]. Another
important feature of the SOC models is that they have their similarity
to the physical wave propagation. Therefore, it is suitable to use them
to develop the measurement-based channel simulators.
1.3. The Geometry-based UWA Channel Simulator
The steps of implementing the geometry-based simulator are illustrated
in Fig. 1.3. In the first step, a geometrical model for UWA channels is
created. The reference model is developed from the geometrical model
in the second steps. Then, a simulation model for the UWA channel
will be selected in the third step. The simulation model should be able
to describe closely the reference model, which is developed from the
geometrical channel model. To estimate the parameters of the simulation
model, a parameter computation method is applied in the fourth step.
Finally, the channel parameters of the simulation model such as path
gains, propagation delays, and Doppler frequencies are determined.


22

Figure 1.3: The scheme of designing the geometry-based channel simulator [17, 55].

1.3.1. Developing the Reference Model from the Geometrical Channel
Model
A shallow UWA geometrical channel model [55] is presented in Fig. 1.4,
where D is the distance between the transmitter (Tx) and receiver (Rx).
The scatterers Si,n (n = 1, 2, ..., Ni and i = 1, 2) are assumed to be randomly distributed on the surface (i = 1) and the bottom (i = 2) of
a shallow-water environment. The angle-of-departure (AOD) and the
angle-of-arrival (AOA) of the nth path are denoted by βi,n and αi,n , respectively. The receiver is moving with speed VR in the direction determined by the angle-of-motion αvR .
Referring the geometrical model in Fig. 1.4, the time variant channel
impulse response (TVCIR) h (τ, t) is composed of three components, and

can be expressed as
2

h (τ, t) =

hi (τ, t),

(1.7)

i=0

where h0 (τ, t), h1 (τ, t), h2 (τ, t) are determined by the LOS component,
the scattered components from the surface, and the scattered components from the bottom, respectively.


23

Figure 1.4: The geometrical model for shallow UWA channels with randomly distributed scatterers Si,n (•) on the surface (i = 1) and the bottom (i = 2)
[55].

The LOS part h0 (τ, t) is given by [55]
cR
As (D0 ) Aα (D0 ) ej(2πf0 t+θ0 ) δ (τ − τ0 ) ,
(1.8)
1 + cR
where cR denotes the Rice factor; the symbols τ0 , f0 , and θ0 stand for
the propagation delay, the Doppler frequency, and the phase shift of the
LOS component, respectively. The propagation delay τ0 = D0 /cs , in
which the speed of sound cs = 1500 m/s for shallow water environments,
the total distance D0 between Tx and Rx is obtained by

h0 (τ, t) =

D0 =

2

D2 + (y1T − y1R ) .

(1.9)

The Doppler frequency f0 is given by
f0 = fD,max cos α0 − αVR ,

(1.10)

where fD,max = VR fc /cs is the maximum Doppler frequency, and fc indicates the carrier frequency. Referring to Fig. 1.4, the AOA α0 will be


24

computed as
y1T − y2R
(1.11)
D
The function As (D0 ) denotes the propagation loss coefficient due to
spherical spreading, which is expressed as follows [19]
α0 = π + arctan

1
,

(1.12)
D
where D is the total propagation distance in meter. The absorption loss
coefficient Aa (D) is given by [19]
As (D) =

Dβ

Aa (D) = 10− 20000 ,

(1.13)

where the parameter β is expressed as
β = 8.68×103

Sa AfT fc2 Bf 2
+
×(1−6.54×10−4 P ) [dB/km] , (1.14)
2
2
fT + fc
fT

where A = 2.34 × 10−6 and B = 3.38 × 10−6 . The symbol Sa denotes
the salinity (in parts per thousand), fc is the carrier frequency (in kHz),
fT = 21.9 × 106−(1520/(T +273)) is the relaxation frequency (in kHz), T is the
temperature (in ◦ C), and P = 1.01(1 + 0.1h) is the hydrostatic pressure
(in kg/cm2 ), where h is the water depth (in meters).
The second h1 (τ, t) and the third part h2 (τ, t) of the TVCIR h (τ, t)
in Eq. 1.7 are given by

Ni
1
As
2Ni (1+cR ) n=1
Ni →∞
j (2πfi,n +θi,n )

hi (τ, t) = lim √
×e

(Di,n )As (Di,n )

.

(1.15)

δ (τ − τi,n )

Using the geometrical channel model, the AOA αi,n can be computed
as

αi,n


π



 2 + arctan

D − xi,n
for i = 1 , if 0 ≤ xi,n ≤ D
y1R
.
=

y2R


for i = 2 , if 0 ≤ xi,n ≤ D
 π + arctan
D − xi,n

(1.16)

Subsequently, the probability density function (PDF) of the AOA pα (α)
can be obtained by applying the concept of transformation of random