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PERFORMANCE ANALYSIS OF
UNDERWATER COMMUNICATION
PROJECT REPORT
Submitted by
SAKTHI PRIYA.P
Register No: 14MCO018
In partial fulfillment for the requirement of award of the degree
of
MASTER OF ENGINEERING
in
COMMUNICATION SYSTEMS
Department of Electronics and Communication Engineering
KUMARAGURU COLLEGE OF TECHNOLOGY
(An autonomous institution affiliated to Anna University, Chennai)
COIMBATORE - 641 049
ANNA UNIVERSITY: CHENNAI 600 025
APRIL - 2016
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BONAFIDE CERTIFICATE
Certified that this project report titled “Performance Analysis of Underwater
Communication” is the bonafide work of SAKTHI PRIYA.P (Reg. No. 14MCO018) who
carried out the research under my supervision. Certified further, that to the best of my
knowledge the work reported here in does not form part of any other project or dissertation on
the basis of which a degree or award was conferred on an earlier occasion on this or any other
candidate.
The candidate with Register No. 14MCO018 was examined by us in the project viva-voce
examination held on ……………………………
INTERNAL EXAMINER EXTERNAL EXAMINER
SIGNATURE
Dr.M.BHARATHI
PROJECT SUPERVISOR
Associate Professor
Department of ECE
Kumaraguru College of Technology
Coimbatore-641 049
SIGNATURE
Dr.A.VASUKI
HEAD OF THE DEPARTMENT
Department of ECE
Kumaraguru College of Technology
Coimbatore-641 049
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ACKNOWLEDGEMENT
First, I would like to express my praise and gratitude to the Lord, who has
showered his grace and blessings enabling me to complete this project in an excellent
manner.
I express my sincere thanks to the management of Kumaraguru College of
Technology and Joint Correspondent Shri Shankar Vanavarayar for the kind support
and for providing necessary facilities to carry out the work.
I would like to express my sincere thanks to our beloved Principal Dr.R.S.Kumar
Ph.D., Kumaraguru College of Technology, who encouraged me with his valuable
thoughts.
I would like to thank Dr.A.Vasuki Ph.D., Head of the Department, Electronics
and Communication Engineering, for her kind support and for providing necessary
facilities to carry out the project work.
In particular, I wish to thank with everlasting gratitude to the project coordinator
Dr.M.Alagumeenaakshi Ph.D., Asst.Professor(III), Department of Electronics and
Communication Engineering, throughout the course of this project work.
I am greatly privileged to express my heartfelt thanks to my project guide
Dr.M.Bharathi Ph.D., Associate Professor, Department of Electronics and
Communication Engineering, for her expert counselling and guidance to make this
project to a great deal of success and I wish to convey my deep sense of gratitude to all
teaching and non-teaching staff of ECE Department for their help and cooperation.
Finally, I thank my parents and my family members for giving me the moral
support and abundant blessings in all of my activities and my dear friends who helped me
to endure my difficult times with their unfailing support and warm wishes.
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ABSTRACT
Underwater communication (UWC) has many applications such as
underwater surveillance systems; speech transmission between the divers; used for the
collection of scientific data recorded at the ocean bottom station etc. The challenges in
underwater acoustic (UWA) communication is that it has tedious time spread by
multipath and Doppler spreads because of the nature of the UWA channel.
The UWA communication has very limited bandwidth and also UWA channel
spreads in both time domain and frequency domain. The Orthogonal Signal Division
Multiplexing (OSDM) technology has proven to be more effective in these channels.
The OSDM is a system that determines multipath profile without an adaptation or
interpolation process to attain stable communication over doubly spread channels. The
BER performance of Orthogonal Frequency Division Multiplexing (OFDM) and
OSDM has been compared and it is found the BER using OSDM is better compared to
OFDM. Multiple Input Multiple Output (MIMO) which mitigates the effect of
multipath fading is also used along with OSDM in order to improve the BER
performance.
OSDM can afford 6.9% better BER performance than that of OFDM for the
same SNR in UWA communication channel. Implementation of 2x2 Multiple-input
multiple-output (MIMO) along with OSDM improves the BER performance by 7.2%
compared to OSDM with single transmit and receive antenna.
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TABLE OF CONTENTS
CHAPTER
NO
TITLE
PAGE
NO
ABSTRACT iv
LIST OF FIGURES vii
LIST OF TABLES viii
LIST OF ABBREVIATIONS ix
1 INTRODUCTION 1
1.1 Overview 1
1.2 Channel Characteristics
1.2.1 Attenuation and Noise
1.2.2 Multipath
1.2.3 The Doppler Effect
1
2
2
3
1.3 System Constraints 4
1.4 Acoustic Waves 4
1.5 Orthogonal Frequency Division Multiplexing 5
1.6 Orthogonal Signal Division Multiplexing 6
1.7 Multiple Input Multiple Output 7
2 LITERATURE SURVEY 11
3 METHODOLOGY 19
3.1 System Model
3.1.1 OFDM system model
3.1.2 OSDM system model
3.1.3 Modulation
3.1.4 IFFT
3.1.5 Cyclic Prefix
19
19
20
25
28
28
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3.2 Channel Description
3.2.1 AWGN channel
3.2.2 Rayleigh channel
3.2.3 Underwater Acoustic Channel
29
29
30
30
3.4 MIMO Systems
3.5 Bit Error Rate
3.6 Signal to Noise ratio
32
34
34
4 SIMULATION RESULTS 36
5 CONCLUSION AND FUTURE WORK 41
REFERENCES 42
vii
LIST OF FIGURES
FIGURE NO. CAPTION PAGE NO.
3.1 Block Diagram of OFDM Transceiver
System
19
3.2 Block Diagram of OSDM
21
3.3 Constellation diagram for BPSK
26
3.4 Constellation diagram for QPSK
27
3.5 Block diagram of Selection Combining
34
4.1 Absorption VS Frequency for Thorps Model
37
4.2 Absorption VS Frequency for Fisher-
Simmons’s model
38
4.3 SNR vs. BER Performance for OFDM and
OSDM
39
4.4 SNR vs. BER Performance for MIMO
OSDM
40
viii
LIST OF TABLES
TABLE NO. CAPTION
PAGE NO.
4.1 Simulation Parameters 36
ix
LIST OF ABBREVIATIONS
UWA Underwater Acoustics
MIMO Multiple Input Multiple Output
SISO Single Input Single Output
FDM Frequency Division Multiplexing
OFDM Orthogonal Frequency Division Multiplexing
OSDM Orthogonal Signal Division Multiplexing
BER Bit Error Rate
QAM Quadrature Amplitude Modulation
PSK Phase Shift Keying
BPSK Binary Phase Shift Keying
QPSK Quadrature Phase Shift Keying
SNR Signal to Noise Ratio
CP Cyclic Prefix
FFT Fast Fourier Transform
IFFT Inverse Fast Fourier Transform
ZF Zero Forcing
AUV Autonomous Underwater Vehicles
DFE Decision Feedback Equalizer
ISI Intersymbol Interference
ICI Inter Channel Interference
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CHAPTER 1
INTRODUCTION
1.1 OVERVIEW
Underwater Acoustic (UWA) communication has been used in number of
applications also it is mainly used as essential knowledge for underwater investigation
behaviors. Underwater acoustic communication is a technique of sending and receiving
message below water [1]. There are several ways of employing such communication
but the most common is using hydrophones. Under water communication is difficult
due to factors like multi-path propagation, time variations of the channel, small
available bandwidth and strong signal attenuation, especially over long ranges.
Various methods can be used in underwater communication they are radio
waves at extremely low frequencies but it requires large antennas and high
transmission power, optical waves causes less attenuation but affected by scattering,
acoustic waves are best suited for underwater communication. Underwater
communication provides low data rates compared to terrestrial communication also
underwater communication uses acoustic waves instead of electromagnetic waves.
1.2 CHANNEL CHARACTERISTICS
Underwater acoustic channels are generally recognized as one of the most difficult
communication media. Acoustic propagation is best supported at low frequencies, and
the bandwidth available for communication is extremely limited. For example, an
acoustic system may operate in a frequency range between 10Hz and 15 kHz.
Although the total communication bandwidth is very low (500 Hz), [14] the system is
in fact wideband, in the sense that bandwidth is not negligible with respect to the
centre frequency. Sound propagates underwater at a very low speed and propagation
occurs over multiple paths. Delay spreading over tens or even hundreds of
milliseconds results in frequency-selective signal distortion, while motion creates an
extreme Doppler effect. The worst properties of radio channels — poor physical link
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quality of a mobile terrestrial radio channel and high latency of a satellite channel —
are combined in an underwater acoustic channel [14].
1.2.1 ATTENUATION AND NOISE
The path loss depends on the signal frequency. This dependence is a
consequence of absorption (i.e., transfer of acoustic energy into heat). In addition to
the absorption loss, signal experiences a spreading loss, which increases with distance
ambient noise and site-specific noise. Ambient noise is always present in the
background of the quiet deep sea. Site-specific noise, on the contrary, exists only in
certain places. For example, ice cracking in Polar Regions creates acoustic noise as do
snapping shrimp in warmer waters. The ambient noise comes from sources such as
turbulence, breaking waves, rain, and distant shipping. While this noise is often
approximated as Gaussian, it is not white. Unlike ambient noise, site-specific noise
often contains significant non- Gaussian components. The attenuation, which grows
with frequency, and the noise, whose spectrum decays with frequency, results in a
signal-to-noise ratio (SNR) that varies over the signal bandwidth. The acoustic
bandwidth depends on the distance has important implications for the design of
underwater networks. Specifically, it makes a strong case for multihopping, since
dividing the total distance between a source and destination into multiple hops enables
transmission at a higher bit rate over each (shorter) hop. The same fact helps to offset
the delay penalty involved in relaying. Since multihopping also ensures lower total
power consumption, its benefits are doubled from the viewpoint of energy- per-bit
consumption on an acoustic channel.
1.2.2 MULTIPATH
Multipath formation in the ocean is governed by two effects: sound
reflection at the surface, bottom, and any objects, and sound refraction in the water.
The latter is a consequence of the spatial variability of sound speed. Sound speed
depends on the temperature, salinity, and pressure, which vary with depth and
location; and a ray of sound always bends toward the region of lower propagation
speed, obeying Snell’s law. Near the surface, both the temperature and pressure are
usually constant, as is the sound speed. In temperate climates the temperature
decreases as depth begins to increase, while the pressure increase is not enough to
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offset the effect on the sound speed. The sound speed thus decreases in the region
called the main thermocline. After some depth, the temperature reaches a constant
level of 4°C, and from there on, the sound speed increases depth (pressure). When a
source launches a beam of rays, each ray will follow a slightly different path, and a
receiver placed at some distance will observe multiple signal paths may do so at a
higher speed, thus reaching the receiver before a direct stronger ray. This phenomenon
results in a non-minimum phase channel response. The impulse response of an
acoustic channel is influenced by the geometry of the channel and its reflection and
refraction properties, which determine the number of significant propagation paths,
and their relative strengths and delays. There are infinitely many signal echoes, but
those that have undergone multiple reflections and lost much of the energy can be
discarded, leaving only a finite number of significant paths.
1.2.3 THE DOPPLER EFFECT
Motion of the transmitter or receiver contributes additionally to the changes
in channel response. This occurs through the Doppler Effect, which causes frequency
shifting as well as additional frequency spreading. The magnitude of the Doppler
Effect is proportional to the ratio a = v/c of the relative transmitter-receiver velocity to
the speed of sound. Because the speed of sound is very low compared to the speed of
electro-magnetic waves, motion-induced Doppler distortion of an acoustic signal can
be extreme. Autonomous underwater vehicles (AUVs) move at speeds on the order of
a few meters per second, but even without intentional motion, underwater instruments
are subject to drifting with waves, currents, and tides, which may occur at comparable
velocities. In other words, there is always some motion present in the system, and a
communication system has to be designed taking this fact into account. The only
comparable situation in radio communications occurs in low Earth orbiting (LEO)
satellite systems, where the relative velocity of satellites flying overhead is extremely
high (the channel there, however, is not nearly as dispersive). The major implication
of motion-induced distortion is on the design of synchronization and channel
estimation algorithms.
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1.3 SYSTEM CONSTRAINTS
In addition to the fundamental limitations imposed by acoustic propagation,
there are system constraints that affect the operation of acoustic modems. The most
obvious of these constraints is the fact that acoustic transducers have their own
bandwidth limitation, which constrains the available bandwidth beyond that offered
by the channel. The system constraints affect not only the physical link, but all the
layers of network architecture. In an acoustic system, the power required for
transmitting is much greater than that required for receiving. Transmission power
depends on the distance, and its typical values are on the order of tens of watts. In
contrast, the power consumed by the receiver is much lower, with typical values
ranging from about 100 mW for listening or low-complexity detection, to no more
than a few watts required engaging a sophisticated processor for high-rate signal
detection. In sleep mode, from which a node can be woken on command, no more
than 1 mW may be needed.
Underwater instruments are battery-powered; hence, it is not simply the
power, but also the energy consumption that matters. This is less of an issue for
mobile systems, where the power used for communication is a small fraction of the
total power consumed for propulsion, but it is important for networks of fixed bottom-
mounted nodes, where the overall network lifetime is the figure of merit. One way to
save energy is by transmitting at a higher bit rate. Another way to save the energy is
by minimizing the number of retransmissions [14].
1.4 ACOUSTIC WAVES
Acoustic waves are one type of longitudinal waves and these waves have same
direction of vibration as the direction of travel. These waves travel with the speed of
sound. The speed of sound depends upon the medium. The speed of sound in water is
approximately 1500m/s [2]. Acoustic waves can easily be absorbed so the absorption
loss is the main loss to be considered in underwater communication.
Acoustic waves are longitudinal waves that exhibit phenomena
like diffraction, reflection and interference. Sound waves however don't have
any polarization since they oscillate along the same direction as they move.
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Underwater acoustic channel propagation was influenced by three issues
attenuation that increases with signal frequency, time varying multipath propagation
and low speed of sound (1500m/s). Underwater acoustic propagation is best sustained
at low frequencies. Since attenuation increases with frequency, for long range
communication low frequency band only can be used, this in turn reduces the channel
capacity. Band width is extremely limited for long range communication [11].
The underwater communication has many applications from military to
commercial applications. Initially underwater communication was used mainly in
military applications. Recently commercial application has received much attention
i.e. pollution monitoring, remote control in offshore oil industry and used to provide
early warnings of Tsunami’s created by undersea earthquakes, also the pressure
sensors deployed on the seafloor to detect tsunamis. The UWA communication used
to provide the speech transmission between divers, used for the collection of scientific
data recorded at the ocean bottom station and it is mainly used in underwater
surveillance systems [11].
1.5 ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING
The concept of using parallel data transmission by means of frequency
division multiplexing (FDM) was established earlier. Then the idea to use parallel data
streams and FDM with overlapping sub channels to avoid the use of high speed
equalization and to combat impulsive noise, and multipath distortion as well as to
fully use the available bandwidth was given. The initial applications were in the
military communications.
In OFDM, each carrier is orthogonal to all other carriers. In a conventional
serial data system, the symbols are transmitted sequentially, with the frequency
spectrum of each data symbol allowed to occupy the entire available bandwidth. In a
parallel data transmission system several symbols are transmitted at the same time,
what offers possibilities for alleviating many of the problems encountered with serial
systems. In OFDM, the data is divided among large number of closely spaced carriers.
This accounts for the ―frequency division multiplex‖ part of the name. This is not a
multiple access technique, since there is no common medium to be shared. The entire
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bandwidth is filled from a single source of data. Instead of transmitting in serial way,
data is transferred in a parallel way. Only a small amount of the data is carried on each
carrier, and by this lowering of the bitrate per carrier (not the total bitrate), the
influence of intersymbol interference is significantly reduced. In principle, many
modulation schemes could be used to modulate the data at a low bit rate onto each
carrier. [22]
Orthogonal Frequency Division Multiplexing (OFDM) is a method of
encoding digital data on multiple carrier frequencies. OFDM is a FDM scheme used
as digital multicarrier modulation method. A large number of closely spaced sub
carrier signals are used to carry data on several parallel data streams or channels. Each
subcarrier is modulated with conventional modulation scheme (QAM or PSK) at low
symbol rate, maintaining total data rates similar to conventional single carrier
modulation scheme in same bandwidth.
An OFDM is a form of multicarrier modulation. It consists of a number of
closely spaced modulated carriers. When modulation of any form- voice, data, etc.; is
applied to carrier then side bands spread out either side. It is necessary for a receiver
to be able to receive the whole signal to be able to successfully demodulated with
data. As a result when signals are transmitted close to one another they must be spaced
so that receiver can separate them using filter and there must be a guard band between
them. This is not in case of OFDM. Although the sidebands from each carrier overlap,
they still be received without the interference that might be expected because they are
orthogonal to each other
Underwater Acoustic communication has doubly spread channel i.e., in both
time domain and frequency domain. The frequency selective fading and Doppler
spreads leads to multipath distortion. The OFDM has proven to be effective in
combating frequency selective multipath distortion without the need of complex time
domain techniques so the OFDM is used in the UWA communication.
1.6 ORTHOGONAL SIGNAL DIVISION MULTIPLEXING
Orthogonal Signal Division Multiplexing (OSDM) is a scheme that measures
multipath profile without any adaptation or interpolation process to achieve stable
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communication over doubly spread channels. OSDM uses kronecker product between
the rows of IDFT matrix and the data sequences. This technique is designed to keep
orthogonality among the data sequences over the frequency-selective fading channel.
By sharing one data sequence as a pilot between the transmitter and the receiver, the
receiver can obtain the channel matrix from the pilot and obtain the message by
solving simultaneous equations.
The OSDM scheme is expected to be more robust against deep-fading
channels because it combines some subcarriers for equalization, whereas the OFDM
scheme equalizes the signal in each subcarrier.
1.7 MULTIPLE INPUT MULTIPLE OUTPUT
Multi Input Multi Output (MIMO) is multiplying the capacity of radio link
using multiple transmitter and receiver antenna to exploit multipath propagation
(phenomenon in which radio signals reaching receiver antenna by two or more paths).
MIMO refers to practical technique for sending and receiving more than one data
signal on same radio channel at same time via multipath propagation. MIMO channel
capacity grows linearly with antenna pairs as long as the environment has sufficiently
rich scatters. This means large channel capacities can be obtained in areas with strong
multi path propagation. The multipath propagation can be expressed in terms of
angular spread at the transmitter array as well as at the receiving MIMO antenna
array.
MIMO is used in UWA in order to improve the Bit Error Rate (BER)
performance of the system. Advantages of using MIMO systems are it achieves better
BER than SISO systems of same SNR, high data rate can be obtained and also
provides better coverage compared to SISO systems.
MIMO can be sub-divided into three main categories, precoding, spatial
multiplexing or SM, and diversity coding.
Precoding is multi-stream beam forming, in the narrowest definition. In more
general terms, it is considered to be all spatial processing that occurs at the transmitter.
In (single-stream) beam forming, the same signal is emitted from each of the transmit
antennas with appropriate phase and gain weighting such that the signal power is
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maximized at the receiver input. The benefits of beam forming are to increase the
received signal gain - by making signals emitted from different antennas add up
constructively - and to reduce the multipath fading effect. In line-of-sight propagation,
beam forming results in a well-defined directional pattern. However, conventional
beams are not a good analogy in cellular networks, which are mainly characterized by
multipath propagation. When the receiver has multiple antennas, the transmit beam
forming cannot simultaneously maximize the signal level at all of the receive
antennas, and precoding with multiple streams is often beneficial. Note that precoding
requires knowledge of channel state information (CSI) at the transmitter and the
receiver.
Spatial multiplexing requires MIMO antenna configuration. In spatial
multiplexing, a high-rate signal is split into multiple lower-rate streams and each
stream is transmitted from a different transmit antenna in the same frequency channel.
If these signals arrive at the receiver antenna array with sufficiently different spatial
signatures and the receiver has accurate CSI, it can separate these streams into
(almost) parallel channels. Spatial multiplexing is a very powerful technique for
increasing channel capacity at higher signal-to-noise ratios (SNR). The maximum
number of spatial streams is limited by the lesser of the number of antennas at the
transmitter or receiver. Spatial multiplexing can be used without CSI at the
transmitter, but can be combined with precoding if CSI is available. Spatial
multiplexing can also be used for simultaneous transmission to multiple receivers,
known as space-division multiple access or multi-user MIMO, in which case CSI is
required at the transmitter.[32]
The scheduling of receivers with different spatial
signatures allows good separability.
Diversity coding techniques are used when there is no channel knowledge at the
transmitter. In diversity methods, a single stream (unlike multiple streams in spatial
multiplexing) is transmitted, but the signal is coded using techniques called space-time
coding. The signal is emitted from each of the transmit antennas with full or near
orthogonal coding. Diversity coding exploits the independent fading in the multiple
antenna links to enhance signal diversity. Because there is no channel knowledge,
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there is no beam forming or array gain from diversity coding. Diversity coding can be
combined with spatial multiplexing when some channel knowledge is available at the
transmitter.
Spatial multiplexing techniques make the receivers very complex, and they are
typically combined with Orthogonal frequency-division multiplexing (OFDM) or with
Orthogonal Frequency Division Multiple Access (OFDMA) modulation, where the
problems created by a multi-path channel are handled efficiently.
Existence of multiple antennas in a system means existence of different
propagation paths. Aiming at improving the reliability of the system, we may choose
to send same data across the different propagation (spatial) paths. This is
called spatial diversity or simply diversity. Aiming at improving the data rate of the
system, we may choose to place different portions of the data on different propagation
paths (spatial-multiplexing). These two systems are listed below.
1. MIMO – implemented using diversity techniques – provides diversity gain –
Aimed at improving the reliability
2. MIMO – implemented using spatial-multiplexing techniques –
provides degrees of freedom or multiplexing gain – Aimed at improving the
data rate of the system.
In diversity techniques, same information is sent across independent fading
channels to combat fading. When multiple copies of the same data are sent across
independently fading channels, the amount of fade suffered by each copy of the data
will be different. This guarantees that at-least one of the copy will suffer less fading
compared to rest of the copies. Thus, the chance of properly receiving the transmitted
data increases. In effect, this improves the reliability of the entire system. This also
reduces the co-channel interference significantly. This technique is referred as
inducing a ―spatial diversity‖ in the communication system. Consider a SISO system
where a data stream [10111] is transmitted through a channel with deep fades. Due to
the variations in the channel quality, the data stream may get lost or severely
corrupted that the receiver cannot recover. The solution to combat the rapid channel
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variations is to add independent fading channel by increasing the number of
transmitter antennas or receiver antennas or the both.
In spatial multiplexing, each spatial channel carries independent information,
thereby increasing the data rate of the system. This can be compared to Orthogonal
Frequency Division Multiplexing (OFDM) technique, where, different frequency sub
channels carry different parts of the modulated data. But in spatial multiplexing, if the
scattering by the environment is rich enough, several independent sub channels are
created in the same allocated bandwidth. Thus the multiplexing gain comes at no
additional cost on bandwidth or power. The multiplexing gain is also referred as
degrees of freedom with reference to signal space constellation [2]. The number of
degrees of freedom in a multiple antenna configuration is equal to min (NT,NR),
where NT is the number of transmit antennas and NR is the number of receive
antennas. The degrees of freedom in a MIMO configuration govern the overall
capacity of the system.
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CHAPTER 2
LITERATURE REVIEW
In this chapter the papers related to the underwater acoustic (UWA)
communication various schemes used in UWA communication, channel models of
UWA communication are studied.
In [1], the performance comparison of OSDM and other existing schemes
such as OFDM, Decision Feedback Equalizer (DFE) schemes in doubly spread
channels are discussed. The OSDM scheme has said to achieve far better BER
performance compared to all other schemes with same SNR at doubly spread
channels. With those results they have given that the OSDM can become a viable
alternative offering a highly reliable communication environment for UWA
communication with multipath and Doppler spread (such as shallow water) with
practical complexity.
In [2], the UWA channels, especially shallow-water ducts, are
characterized by numerous encounters with both the sea surface and seafloor.
Therefore these multipath environment causes signal fading and intersymbol
interference (ISI) also the existence of the moving sea surface and the communication
platform‟s movement cause a Doppler shift, and multiple Doppler-scaling paths and
time variation of the UWA channel lead to Doppler spread. The ISI and Doppler
spread can serve as a barrier to UWA communication, because the effect of ISI and
Doppler spread can become several orders of magnitude greater than the one observed
in a communication system using radio, considering the sound speed underwater. The
OSDM scheme in doubly spread channel is effective in combating the ISI and
Doppler spreads.
In [3], the authors overviewed the several communication schemes that use
multiple antennas the focused on the single-user communication in which the
transmitter does not know the channel state information, and the signal power
allocated for each transmitting antenna is equal. Single-stream communication using
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OSDM with multiple antennas was given to increase the communication quality with
less complexity, compared to multistream communication using OSDM with multiple
antennas. In the multistream scheme, the simultaneous equation sometimes become
ill-conditioned, this results in loss of performance. This problem could be avoided
with an increase in the number of antennas in the receiver. However, the increase in
complexity remains an issue. The single-stream communication using OSDM with
multiple antennas was given to avoid the ill-condition problem with less complexity.
By designing the data sequences in the transmitter, the ill-condition problem could be
avoided with less complexity. The single-stream communication achieved better bit-
error rate performance compared to the multistream case with less complexity in
exchange for an efficient data rate, and it may be suitable for wireless communication
systems with a limited number of reception antennas.
In [4], the authors discussed that MIMO-OFDM system with spatial
multiplexing was presented. The receiver works on a block-by-block basis where null
and pilot subcarriers are used for Doppler and channel estimation, respectively, and an
iterative structure is used for MIMO detection and decoding. The performance results
based on data processing from three different experiments, showing very high spectral
efficiency via parallel data multiplexing with high order constellations. The results
suggest that MIMO-OFDM is an appealing choice for high data rate underwater
acoustic communications. Further investigations on MIMO underwater acoustic
communications, both single- and multi-carrier approaches, are warranted, especially
on the capacity limits in underwater channels, advanced receiver designs, and
experimental results in more challenging channel conditions with large Doppler
spread.
In [5], the authors have given that increasing demand for high-
performance 4G broadband wireless is enabled by the use of multiple antennas at both
base station and subscriber ends. Multiple antenna technologies enable high capacities
suited for Internet and multimedia services, and also dramatically increase range and
reliability. However, in fast fading channels, the time variation of a fading channel
over OFDM symbol period results in a loss of sub-channel orthogonality, which leads
to inter-carrier interference (ICI). MIMO systems have been recently under active
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consideration because of their potential for achieving higher data rate and providing
more reliable reception performance compared with traditional single-antenna systems
for wireless communications. A space-time (ST) code is a bandwidth-efficient method
that can improve the reliability of data transmission in MIMO systems. It encodes a
data stream across different transmit antennas and time slots, so that multiple
redundant copies of the data stream can be transmitted through independent fading
channels. The MIMO system needs to be integrated and be backward compatible with
an existing non MIMO network. MIMO signalling imposes the support of special
radio resource control (RRC) messages. The terminals need to know via broadcast
down link signalling if a base station is MIMO capable. The base station also needs to
know the mobile‟s capability, i.e., MIMO or non-MIMO. This capability could be
declared during call set up. Handsets are also required to provide feedback to the base
station on the channel quality so that MIMO transmission can be scheduled if the
channel conditions are favourable.
In [6], The authors have given the Underwater acoustic (UWA)
communication in shallow water is still challenging due to large time and frequency
spread of the channel, which act as barriers to achieve high-speed and reliable
communication. Applying digital communications such as single-carrier system with
decision feedback equalizer and orthogonal frequency division multiplexing (OFDM)
has actively been researched. As an alternative, the application of orthogonal signal
division multiplexing (OSDM) has been proposed by the author. It has been found
that OSDM may lead to high-quality communication in the presence of channel
reverberation and large Doppler shifts2). However, effective data rate remains much
below to other systems, such as radio communication. They have employed two-array
Elements in the transmitter, the effective data rate become twice without increasing
the signal bandwidth, and efficient BER was achieved.
In [7], the authors discussed that the 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. Underwater acoustic channels are considered to be “quite
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possibly nature‟s most unforgiving wireless medium”. The complexity of underwater
acoustic channels is dominated by the ocean environment characteristics which
include significant delay, Double-side-spreading, Doppler- spreads, frequency-
selective fading, and limited bandwidth. 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 locations. Together with sensor
technology and vehicular technology, wireless underwater communications are
desirable to enable new applications ranging from environmental monitoring to
gathering of oceanographic data, marine archaeology, and search and rescue missions.
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, which rely on an adaptive 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 magnitude. 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.
In [8], the authors said that because of the enormous capacity upsurge a
MIMO systems offer; such systems gained a lot of interest in mobile communication
research. One indispensable problem of the wireless channel is fading, which occurs
as the signal follows multiple paths between transmitter and receiver antennas. Fading
can be mitigated by diversity, which means that the information is transmitted not
only once but several times, hoping that at least one of the replicas will not undergo
severe fading. There are various coding methods, a main issue in all these schemes is
the exploitation of redundancy to achieve high reliability, and high spectral efficiency
15
and high performance gain for MIMO-OFDM systems. High spectral efficiency and
high transmission rate are the challenging requirements of future wireless broadband
communications. In a multipath wireless channel environment, the deployment of
Multiple Input Multiple Output (MIMO) systems leads to the achievement of high
data rate transmission without increasing the total transmission power or bandwidth.
Multiple-Input Multiple-Output antenna systems are a form of spatial diversity. An
effective and practical way to approaching the capacity of MIMO wireless channels is
to employ space-time block coding in which data is coded through space and time to
improve the reliability of the transmission, as redundant copies of the original data are
sent over independent fading channel.
In [9], the authors formulated an important factor in the transmission of
data is the estimation of channel which is essential before the demodulation of OFDM
signals since the channel suffers from frequency selective fading and time varying
factors for a particular mobile communication system. The estimation channel is
mostly done by inserting pilot symbols into all of the subcarriers of an OFDM symbol
or inserting pilot symbols into some of the sub-carriers of each OFDM symbol. The
first method is called as the pilot based block type channel estimation and it has been
discussed for a slow fading channel. This paper discusses the estimation of the
channel for this block type pilot arrangement which is based on Least Square (LS)
Estimator and Minimum Mean-Square Error (MMSE) Estimator. The second method
is the comb-type based channel estimation in which pilot symbols are transmitted on
some of the sub carriers of each OFDM symbol. This method usually uses different
interpolation schemes such as linear, low-pass, spline cubic, and time domain
interpolation. It is shown that second-order interpolation performs better than the
linear interpolation. the performance of the pilot based block type channel estimation
by using Binary Phase Shift Keying (BPSK) modulation scheme in a slow fading
channel are compared. The transmitted signal under goes many effects such reflection,
refraction and diffraction. Also due to the mobility, the channel response can change
rapidly over time. At the receiver these channel effects must be cancelled to recover
the original signal. The BER of AWGN channel is approximately 10^-2 which is
better than Rayleigh fading and flat fading channel at SNR=10dB using BPSK &
16
QPSK on different number of taps. The MMSE is compared with LS and the MMSE
performs better than the LS using 3 taps where the performance metric is mean square
and symbol error rate have been discussed.
In [10], the authors there are a wide range of physical processes that
impact underwater acoustic communications and the relative importance of these
processes are different in different environments. In this paper some relevant
propagation phenomena are described in the context of how they impact the
development and/or performance of underwater acoustic communications networks.
The speed of sound and channel latency, absorption and spreading losses, waveguide
effects and multipath, surface scattering, bubbles, and ambient noise are all briefly
discussed. The ocean is a time and spatially varying propagation environment whose
characteristics pose significant challenges to the development of effective underwater
wireless communications systems. The high rate of absorption of electro-magnetic
signals in sea water has limited the development of electromagnetic communications
systems to a few specialized systems. Similarly, optical signals are also rapidly
absorbed in sea water and have the added disadvantage of scattering by suspended
particles and high levels of ambient light in the upper part of the water column. the
ocean is bounded from above by the surface and, at the frequencies and ranges
typically of interest for acoustic communications, It is effectively bounded from be-
low by the sea floor. Thus, at some range from the source the acoustic signal can no
longer spread vertically and the nature of spreading changes from spherical to
cylindrical spreading. There is no single channel model that captures the relevant
acoustic propagation characteristics in all underwater environments. Thus, the
successful development of under- water acoustic communications networks will
greatly benefit from an understanding of the roles of the different characteristics in
different environments of interest. Signal attenuation and propagation speed, the ocean
waveguide and time-varying multipath, surface scattering, bubbles, and ambient noise
can all impact physical layer, MAC, routing, and coding decisions, performance, and
analysis.
In [11], the authors have proposed the ability to effectively communicate
underwater has numerous applications for researchers, marine commercial operators
17
and defence organizations. As electromagnetic waves cannot propagate over long
distances in seawater, acoustics provides the most obvious choice of channel. In
channel modelling, the attenuation due to the wave scattering at the surface and its
bottom reflections for different grazing angles and bottom types, ambient noises such
as shipping noise, thermal noise, turbulences are considered. Absorption coefficient in
the channel with different established models like Thorp‟s and Fisher-Simmons is also
studied. Wireless signals experience a variety of degradations due to channel
imperfections. Just as electromagnetic signals are subject to a number of channel
effects, including attenuation, reflections, and interference, underwater acoustic
signals are subject to the same effects. One key difference between the RF and
underwater acoustic channels is propagation speed. Acoustic signals in water are
corrupted by interference from reflection and scattering at the water surface and
bottom. For this reason, it is difficult to achieve high data rates in underwater
channels. Sea water acts as acoustic waveguide and transmits sound signal in itself.
Sound channel as a sound waveguide is a channel with random parameters. But this
subject does not have the meaning of its unpredictability. The most important
characteristic of sea water is its inhomogeneous nature.
In [12], the authors have investigated about the a high bit rate acoustic
link over an underwater channel. Design of a Digital communication system utilizing
acoustic signals for underwater applications is a very challenging field due to the
extremely complex nature of the underwater channel. The conventional techniques for
overcoming the channel effects used in communication systems elsewhere fail to give
the desired results when applied in this field of communications. Orthogonal
Frequency Division Multiplexing has been selected as the modulation scheme,
deviating from the more conventional single-frequency methods hitherto used in this
field in the past. A relatively simple but robust communication system has been
designed covering techniques ranging from Communications, Acoustics and Signal
Processing. The inherent features of the OFDM scheme make the system rugged
against channel effects such as extremely strong multi-path and additive noise.
Additional features have been incorporated to make the system immune to Doppler
shifts and hardware instabilities. The major constraint in using this underwater
18
medium is the extremely complex and continuously varying nature of the sea.
Nevertheless, this underwater channel has been used (sparingly) and the most
common mode of underwater communication is acoustic waves. The modest usage of
the underwater medium, hitherto, has been restricted to analog voice communication
systems, with some capability for data communication using Frequency Shift Keying
or Amplitude Shift Keying. OFDM has been used in the design of the system,
deviating from the conventional path of using „Single Carrier-Frequency Systems‟ for
such applications.
In [15], the authors have given that Orthogonal Frequency Division
Multiplexing (OFDM) is an emerging technology in wireless communication for high
data rate. It‟s a special form of multicarrier communication technique which is the
platform for modern communication systems. Underwater channel is time varying
multipath channel causing Intersymbol interference (ISI), Inter carrier interference
(ICI) and fading. Due to the detrimental effect of time and frequency spreading
achieving high data rate in underwater communication is the challenging one. In
OFDM, orthogonality between the sub-carriers is the most essential condition to be
adopted. It can be achieved by selecting the sub carriers having integer number of
cycles between the adjacent subcarriers should be exactly equal to 1. The main
advantage of OFDM to be used in underwater communication is that limited acoustic
bandwidth can be utilized effectively. The applications of diversity techniques further
improve the performance of the communication systems against the fading channel
impairments.
19
CHAPTER 3
METHODOLOGY
In this chapter the proposed methodology for analysing the performance
of the UWA channel in OFDM and OSDM system model have been discussed.
3.1 SYSTEM MODEL
3.1.1 OFDM System Model
Figure 3.1 represents the basic block diagram of OFDM [4][5] system,
consist of transmitter and receiver two sections, named OFDM transceiver system.
The data bits inserted from the source are firstly mapped (BPSK, QPSK, 16-QAM,
64-QAM) using given modulation techniques and after that converted from serial to
parallel through convertor. Now N subcarriers are there and each sub-carrier consists
of data symbol X(k) (k=0,1,….,N-1), where k shows the sub-carrier index. These N
subcarriers are provided to inverse fast Fourier transform (IFFT) block. After
transformation, the time domain OFDM signal at the output of the IFFT [5][6] can be
given as
( ) ∑ ( ) (
)
Figure 3.1 Block Diagram of a OFDM transceiver System
Mod
Channel
Serial to
Parallel
IFFT Add CP Parallel to
Serial
Serial to
Parallel
Remove CP FFT Parallel to
Serial
Demodulation
Data In
Data Out
20
After that, Cyclic Prefix (CP) [7] is added to mitigate the ISI effect. We
get signal xcp(n), which is sent to parallel to serial convertor again and then, this
signal is sent to frequency selective multi-path fading channels [5][8] and a noisy
channel with i.i.d. AWGN noise. Two fading channels have been used i.e. AWGN and
Rayleigh channels along with Absorption channel model [4].
3.1.2 OSDM System Model
The orthogonal signal-division multiplexing (OSDM) has been proposed, a scheme
that measures the multipath profile without an adaptation or interpolation process, to
achieve stable communication in doubly spread channels. Orthogonal signal division
multiplexing (OSDM) is a new information transmission method using the Kronecker
product between the rows of an IDFT matrix and the data sequences. This technique is
designed to keep orthogonality among the data sequences over the frequency-
selective fading channel. By sharing one data sequence as a pilot between the
transmitter and the receiver, the receiver can obtain the channel matrix from the pilot
and obtain the message by solving simultaneous equations. This technique has been
extended to multistream communication with multiple antennas
The OSDM scheme is expected to be more robust against deep-fading channels
because it combines some subcarriers for equalization, whereas the OFDM scheme
equalizes the signal in each subcarrier [1{5]. Moreover, the OSDM technique has
been extended to multi-stream communication with multiple antennas.
The figure 3.2 represents the basic block diagram of OSDM [4][5] system. The
information vectors of length M, [n=0, 1… N-1] as the sender message is taken
into account. Every single comprises a dissimilar message whose elements are
modulated symbols [e.g., Quadrature Phase-Shift Keying (QPSK)] stated as complex
symbols [1].
21
(a)
X Ẍ
Channel
( )
(b)
Ch#01
xr1
Ch#02
xr(N-1)
Ch#K-1
Figure 3.2: Block Diagram of OSDM (a) Transmitter (b) Receiver
Kronecker
Operation
Ʃ
Add Cyclic
Prefix
Remove Cyclic
Prefix
Remove Cyclic
Prefix
Remove Cyclic
Prefix
𝐷
𝐷
𝐷𝑁
𝐷
𝐷𝑁
𝐷
𝐷
𝐷
𝐷𝑁
Corr
Corr
Corr
Inver
se
Filter
22
The information vectors of length M, [n=0, 1… N-1] as the sender message is
taken into account. Every single comprises a dissimilar message whose elements
are modulated symbols [e.g., phase-shift keying (PSK)] stated as complex symbols.
The N information vectors are multiplexed into a single data stream of length M
N, X, according to
∑ ⨂ (1)
Where
[
( )]
(2)
√ [
( )
]
√ [
( )
]
√
] (3)
In (1), ― ⨂ ‖ signifies the Kronecker product, and each represents to a row of
the inverse discrete Fourier transform (IDFT) matrix . Notice that it resembles to
an interleaved signal in the direct-sequence code-division multiple access (DS–
CDMA), as well as a signal in OFDM if equals 1. If the maximum channel delay is
symbols, the transmission data stream, namely frame X’ is obtained by prepending a
cyclic prefix in which the last part of X with a length of L is placed at the beginning of
X, as follows
23
Ẍ = (X[MN-L] X[MN-L+1]……. X[MN-1]) (4)
Notice that L resembles to a correctable channel reverberation time in a discrete
model, and L ≤ M . The sender data stream is conveyed through the channel. The
received data stream from the channel can be stated using X’ and a channel
response of length L, as
(5)
Where ―*‖signifies a convolution. Here is a connection linking the cyclic-prefix
removed sequence , the channel response and multiplexed data stream Ẍ, as
=Ẍ [
] ]
] ]
]
]
] ] ]
] (6)
Where,
] ] ] (7)
The relationship between and is expressed by
(8)
Where,
⨂ (9)
= Ẍ[
] ]
] ]
]
]
]
] ]
] (10)
24
And is an -by- identity matrix, * is a complex conjugate of the transposition
of , is a complex conjugate of
, and h[l] (l=L,L+1,....M-1) is zero. The
Kronecker product is prepared in the recipient prior to the communication, and is
called the matched filter. We express the matched-filter- functioned sequences as .
If n=0 the relationship between and becomes
[
] ]
] ]
]
]
] ] ]
] (11)
If is contributed to the sender and the recipient, and its periodic autocorrelation
function becomes an impulse according to the receiver can obtain the
[
] ] ] ]
] ]
] ] ]
] =[
] (12)
Channel response by estimating the periodic cross-correlation function between and
as,
[
] ]
] ]
]
]
] ] ]
]
= ( ] ] ]) (13)
( ) (14)
In the following,
] √
] (15)
Whose periodic cross correlation function becomes an impulse as [1]
25
3.1.3 MODULATION
In electronics and telecommunications, modulation is the process of varying
one or more properties of a periodic waveform, called the carrier signal, with a
modulating signal that typically contains information to be transmitted.
In telecommunications, modulation is the process of conveying a message
signal, for example a digital bit stream or an analog audio signal, inside another signal
that can be physically transmitted. Modulation of a sine waveform transforms a
baseband message signal into a pass band signal.
A modulator is a device that performs modulation. A demodulator
(sometimes detector or demod) is a device that performs demodulation, the inverse of
modulation. A modem (from modulator–demodulator) can perform both operations.
The aim of analog modulation is to transfer an analog baseband (or low pass)
signal, for example an audio signal or TV signal, over an analog band pass channel at
a different frequency, for example over a limited radio frequency band or a cable TV
network channel.
The aim of digital modulation is to transfer a digital bit stream over an
analog bandpass channel, for example over the public switched telephone network
(where a bandpass filter limits the frequency range to 300–3400 Hz) or over a limited
radio frequency band.
BPSK
BPSK (also sometimes called PRK, phase reversal keying, or 2PSK) is the
simplest form of phase shift keying (PSK). It uses two phases which are separated by
180° and so can also be termed 2-PSK. It does not particularly matter exactly where
the constellation points are positioned, and in this figure they are shown on the real
axis, at 0° and 180°. This modulation is the most robust of all the PSKs since it takes
the highest level of noise or distortion to make the demodulator reach an incorrect
26
decision. It is, however, only able to modulate at 1 bit/symbol (as seen in the figure)
and so is unsuitable for high data-rate applications.
In the presence of an arbitrary phase-shift introduced by the communications
channel, the demodulator is unable to tell which constellation point is which. As a
result, the data is often differentially encoded prior to modulation.
BPSK is functionally equivalent to 2-QAM modulation.
Figure 3.3 Constellation diagram for BPSK
QPSK
Sometimes this is known as quadriphase PSK, 4-PSK, or 4-QAM. (Although
the root concepts of QPSK and 4-QAM are different, the resulting modulated radio
waves are exactly the same.) QPSK uses four points on the constellation diagram,
equispaced around a circle. With four phases, QPSK can encode two bits per symbol,
shown in the diagram with Gray coding to minimize the bit error rate (BER) —
sometimes misperceived as twice the BER of BPSK.
The mathematical analysis shows that QPSK can be used either to double the
data rate compared with a BPSK system while maintaining the same bandwidth of the
signal, or to maintain the data-rate of BPSK but halving the bandwidth needed. In this
latter case, the BER of QPSK is exactly the same as the BER of BPSK - and deciding
differently is a common confusion when considering or describing QPSK. The
transmitted carrier can undergo numbers of phase changes.
27
Given that radio communication channels are allocated by agencies such as
the Federal Communication Commission giving a prescribed (maximum) bandwidth,
the advantage of QPSK over BPSK becomes evident: QPSK transmits twice the data
rate in a given bandwidth compared to BPSK - at the same BER. The engineering
penalty that is paid is that QPSK transmitters and receivers are more complicated than
the ones for BPSK. However, with modern electronics technology, the penalty in cost
is very moderate. As with BPSK, there are phase ambiguity problems at the receiving
end, and differentially encoded QPSK is often used in practice.
Figure 3.4 Constellation diagram for QPSK
QAM
Quadrature amplitude modulation (QAM) is both an analog and a digital
modulation scheme. It conveys two analog message signals, or two digital bit streams,
by changing (modulating) the amplitudes of two carrier waves, using the amplitude-
shift keying (ASK) digital modulation scheme or amplitude modulation (AM) analog
modulation scheme. The two carrier waves, usually sinusoids, are out of phase with
each other by 90° and are thus called quadrature carriers or quadrature components —
hence the name of the scheme. The modulated waves are summed, and the final
waveform is a combination of both phase-shift keying (PSK) and amplitude-shift
keying (ASK), or (in the analog case) of phase modulation (PM) and amplitude
modulation. In the digital QAM case, a finite number of at least two phases and at
least two amplitudes are used. PSK modulators are often designed using the QAM
principle, but are not considered as QAM since the amplitude of the modulated carrier
28
signal is constant. QAM is used extensively as a modulation scheme for digital
telecommunication systems. Arbitrarily high spectral efficiencies can be achieved
with QAM by setting a suitable constellation size, limited only by the noise level and
linearity of the communications channel.
3.1.4 IFFT
IFFT (Inverse Fast Fourier Transform) used to convert frequency domain to
time domain. Frequency domain usually used in conditions such as filtering,
amplifying and mixing and is used for creating desired wave patterns but the time
domain analysis are used to analyze the behavior of the signal over time and is also
used to understand data sent through the channel.
Always one uses the discrete Fourier transform to convert them to the discrete
frequency form DFT, and vice versa, the inverse discrete transform IDFT is used to
back convert the discrete frequency form into the discrete time form. To reduce the
mathematical operations used in the calculation of DFT and IDFT one uses the fast
Fourier transform algorithm FFT and IFFT which corresponds to DFT and IDFT,
respectively.
In transmitters using OFDM as a multicarrier modulation technology, the
OFDM symbol is constructed in the frequency domain by mapping the input bits on
the I- and Q- components of the QAM symbols and then ordering them in a sequence
with specific length according to the number of subcarriers in the OFDM symbol.
That is by the mapping and ordering process; one constructs the frequency
components of the OFDM symbol. To transmit them, the signal must be represented
in time domain. This is accomplished by the inverse fast Fourier transform IFFT.
3.1.5 CYCLIC PREFIX
The term cyclic prefix refers to the prefixing of a symbol with a repetition
of the end. Although the receiver is typically configured to discard the cyclic prefix
samples, the cyclic prefix serves two purposes
As a guard interval, it eliminates the intersymbol interference from the previous
symbol.
29
As a repetition of the end of the symbol, it allows the linear convolution of a
frequency-selective multipath channel to be modelled as circular convolution,
which in turn may be transformed to the frequency domain using a discrete
Fourier transform. This approach allows for simple frequency-domain processing,
such as channel estimation and equalization
In order for the cyclic prefix to be effective (i.e. to serve its aforementioned
objectives), the length of the cyclic prefix must be at least equal to the length of the
multipath channel. Although the concept of cyclic prefix has been traditionally
associated with OFDM systems, the cyclic prefix is now also used in single
carrier systems to improve the robustness to multipath propagation.
3.2 CHANNEL DESCRIPTION
We choose two most widely used channels i.e. AWGN, Rayleigh fading
channels along with the underwater communication channel.
3.2.1 AWGN Channel
Additive white Gaussian noise (AWGN) channel is a basic or commonly
used channel model for analysing modulation schemes. In this model, the AWGN
channel adds a white Gaussian noise to the signal that passes through it. This implies
that the channel’s amplitude frequency response is flat (thus with unlimited or infinite
bandwidth) and phase frequency response is linear for all frequencies so that
modulated signals go through it without any amplitude loss and phase distortion.
Fading does not exist for this channel. The transmitted signal gets distorted only by
AWGN process AWGN channel is a standard channel used for analysis purpose only.
The mathematical expression in receiving signal is
( ) ( ) ( )
That passes through the AWGN channel where s(t) is transmitted signal and n(t) is
background noise or additive white Gaussian noise [10].
30
3.2.2 Rayleigh Channel
The effects of multipath embrace constructive and destructive interference,
and phase shifting of the signal. This causes Rayleigh fading. There is no line of sight
(NLOS) path means no direct path between transmitter and receiver in Rayleigh
fading channel [9]. The received signal can be simplified to
( ) ∑ ( ) ( ) ( )
Where w(n) is AWGN noise with zero mean and unit variance, h(n) is channel
impulse response i.e.
( ) ∑ ( ) ( )
Where (n) and ( ) are attenuation and phase shift for nth path.
If the coherence bandwidth of the channel is larger than signal bandwidth,
the channel is called flat; otherwise it is frequency-selective fading channel. Here
MIMO OFDM is simulated under frequency-selective fading
channel. The Rayleigh distribution [11] is basically the magnitude of the sum of two
equal independent orthogonal Gaussian random variables and the probability density
function (pdf) given by
( )
(
)
where 𝜎2 is the time-average power of the received signal.
3.2.3 Underwater Acoustic Channel
Underwater acoustic channels are generally recognized as one of the most
difficult communication media. Acoustic propagation is best suitable at low
frequencies, and the bandwidth available for communication is extremely limited. An
acoustic system may operate in a frequency range between 10 and 15 kHz. Although
the total communication bandwidth is very low (5 kHz), the system is in fact
wideband, in the logic that bandwidth is not trivial with respect to the centre
frequency.
In channel modelling, the attenuations due to the frequency absorption,
ambient noises and loss due to the wave scatterings at the surface and bottom for
31
efferent grazing angles and bottom types are considered. Also Ray theory is the basis
of the mathematical model of multipath effects [7].
a. Loss modelling:
The acoustic energy of a sound wave propagating in the ocean is partly:
- Absorbed, i.e., the energy is transformed into heat
- Lost due to sound scattering by inhomogeneity.
b. Absorption:
Underwater acoustic communication channels are characterized by a path loss that
depends not only on the distance between the transmitter and receiver, as it is the case
in many other wireless Channels, but also on the signal frequency. The signal
frequency determines the absorption loss which occurs because of the transfer of
acoustic energy into heat.
c. Attenuation:
Attenuation or path loss that occurs in an underwater acoustic channel over a
distance L for a Signal of frequency is given by equation as
( ) ( )
Where A0 is a unit-normalizing constant, k is the spreading factor, and a (f) is the
absorption coefficient. Expressed in dB, the acoustic path loss is given by equation 2
as
( )
( )
The first term in the above summation represents the spreading loss, and the
second term represents the absorption loss. The spreading factor k describes the
geometry of propagation, and its commonly used values are k = 2 for spherical
spreading, k = 1 for cylindrical spreading, and k = 1.5 for the so-called practical
spreading. The absorption coefficient can be expressed empirically, using the
established models like Thorp’s model, Fischer and Simmons model which gives a (f)
in dB/km for f in kHz [11].
32
( )
In Thorp’s model, the attenuation is independent of temperature and the depth
of the water body. This is taken into consideration in the next model of Fisher-
Simmons’s model. The loss according to the Fisher-Simmons’s model at t = 8 degree
Celsius.
3.4 MIMO Systems
Multiple-input and multiple-output (MIMO) is a method for multiplying
the capacity of a radio link using multiple transmit and receive antennas to exploit
multipath propagation. MIMO is fundamentally different from smart antenna
techniques developed to enhance the performance of a single data signal, such as
beam forming and diversity.
The diversity coding technique is used in the proposed system. Diversity
combining is the technique applied to combine the multiple received signals of
a diversity reception device into a single improved signal.
Various diversity combining techniques can be distinguished:
Equal-gain combining: All the received signals are summed coherently.
Maximal-ratio combining is often used in large phased-array systems: The
received signals are weighted with respect to their SNR and then summed. The
resulting SNR yields ∑ where SNR of the received signal k is.
Switched combining: The receiver switches to another signal when the currently
selected signal drops below a predefined threshold. This is also often called
"Scanning Combining".
Selection combining: Of the N received signals, the strongest signal is selected.
When the N signals are independent and Rayleigh distributed, the expected gain has
been shown to be ∑
, expressed as a power ratio. Therefore, any additional
gain diminishes rapidly with the increasing number of channels. This is a more
efficient technique than switched combining.
33
Sometimes more than one combining technique is used – for example, lucky
imaging uses selection combining to choose (typically) the best 10% images, followed
by equal-gain combining of the selected images. Other signal combination techniques
have been designed for noise reduction and have found applications in single
molecule biophysics, chemometrics among other disciplines. When the required signal
is a combination of several waves (i.e., multipath), the total signal amplitude may
experience deep fades (i.e., Rayleigh fading), over time or space
The major problem is to combat these deep fades, which result in system
outage. Most popular and efficient technique for doing so is to use some form of
diversity combining.
Diversity means multiple copies of the required signal are available, which
experience independent fading (or close to that) and it is the effective way to combat
fading. The basic principle of the diversity technique is to create multiple independent
paths for the signal, and combine them in an optimum or near-optimum way.
Combining techniques
Selection combining (SC).
Maximum ratio combining (MRC).
Equal gain combining (EGC).
Hybrid combining (two different forms)
Selection combining (SC)
Selection combining is the technique of considering the branch with largest SNR
value at any given moment of time.
34
Figure 3.5 Block diagram of Selection Combining
3.5 BIT ERROR RATE (BER)
In digital transmission, the no. of bit errors is the number of receiving bits of
a signal data over a communication channel that has been changed because of noise,
noise, distortion, interference or bit synchronization redundancy.
The bit error rate or bit error ratio (BER) is defined as the rate at which errors
occur in a transmission system during a studied time interval. BER is a unit less
quantity, often expressed as a percentage or 10 to the negative power.
The definition of BER can be translated into a simple formula:
BER = number of errors / total number of bits sent
Noise is the main enemy of BER performance. Quantization errors
also reduce BER performance, through unclear reconstruction of the digital waveform.
The precision of the analog modulation/ demodulation process and the effects of
filtering on signal and noise bandwidth also influence quantization errors.
3.6 SIGNAL TO NOISE RATIO (SNR)
The SNR is the ratio of the received signal power over the noise power in the
frequency range of the process. SNR is inversely related to BER, that is high BER
causes low SNR. High BER causes an increase in packet loss, enhance in delay and
decrease throughput. SNR is an indicator usually measures the clarity of the signal in
h1
h2
hn
35
a circuit or a wired/wireless transmission channel and measure in decibel (dB). The
SNR is the ratio between the wanted signal and the unwanted background noise.
SNR formula in terms of diversity
36
CHAPTER 4
SIMULATION RESULTS
The parameters considered for the simulation of Underwater
Acoustic Communication model are listed in the table given below,
Table 4.1 Simulation Parameters
PARAMETERS VALUE
Modulation BPSK, QPSK
Channel Model THORPS MODEL with RAYLEIGH
FADING MODEL
Noise Model AWGN
FFT & IFFT 64
Nt 2
Nr 2
Band Width 312 Hz
fc 80 Hz
Subcarrier Number 52
CP length 16
OFDM symbol length 4µs
37
The attenuation characteristic of underwater channel is modelled using
Thorp’s model (Figure 4.1) and Fisher-Simmons’s model (Figure4.2). In Thorp’s
model, the attenuation is independent of temperature and the depth of the water body
and Fisher-Simmons’s model the characteristic depends on these.
Figure 4.1: Absorption VS Frequency for Thorps Model
By Thorps model and Fisher- Simmons model it is understood that the
absorption loss mainly depends up on the frequency. In both the models as frequency
increases the absorption coefficient also increases which may result in absorption loss.
0 10 20 30 40 50 60 70 80 90 1000
5
10
15
20
25
30
35
frequency(khz)
absorp
tion c
oeffic
ient(
db/k
m)
Thorps Model
38
Figure 4.2: Absorption VS Frequency for Fisher-Simmons’s model
The Thorps absorption model is quite closer to the Simmons absorption model
but the Thorps model is mostly preferred in Underwater Acoustic Communication
because while communicating in deep sea the Thorps model seems to be more
accurate than that of the Simmons model.
By using the Thorps model the performance analysis of underwater acoustic
communication using the OFDM and OSDM have been performed for the given
simulation parameters. Here the OSDM seems to give better BER performance than
OFDM. The SNR vs. BER performance of UWA communication is simulated and
shown in Figure 4.3.
0 10 20 30 40 50 60 70 80 90 1000
5
10
15
20
25
30
35
frequency(khz)
absorp
tion c
oeffic
ient(
db/k
m)
Simmons Model
39
Figure 4.3: SNR vs. BER Performance for OFDM and OSDM
From this figure it is inferred that OSDM provides 6.9% better BER
performance compared to that of OFDM.
But still the BER performance in UWA communication channel is not as
expected so the MIMO with Spatial diversity has been introduced in order to improve
the performance.
The 2x2 MIMO was implemented along with OSDM to mitigate the effect of
multipath fading. MIMO with the spatial diversity techniques by using selection
combining have been used. By implementing MIMO along with OSDM the BER
performance in the UWA channel has been improved even more. The simulation
results are given in the Figure 4.4.
0 2 4 6 8 10 12 14 16 18 2010
-3
10-2
10-1
100
SNR
BE
R
SNR vs BER
OSDM
OFDM
40
Figure 4.4: SNR vs. BER Performance for MIMO OSDM
0 2 4 6 8 10 12 14 16 18 2010
-5
10-4
10-3
10-2
10-1
100
SNR
BE
R
SNR vs BER
MIMO OSDM
41
CHAPTER 5
CONCLUSION AND FUTURE WORK
Underwater communications were initially used in military applications but
recently it acquired more attention in commercial applications also. It is mainly used
in underwater surveillance system. The achievable data rate in underwater
communication is low due to the nature of the channel. The performance analysis of
the OSDM scheme and OFDM scheme was accomplished. The findings of this
scheme are assessed by means of communication quality and data rate. The OSDM is
desirable in terms of communication quality; it succeeded in far improved BER
performance compared to the OFDM scheme.
The OSDM will be the better complementary in UWA communications since it
provides 6.9% better BER performance when compared to the OFDM techniques for
the same SNR values. Then by implementing 2x2 MIMO along with OSDM the BER
performance is still more improved i.e., 7.2% improved compared to the OSDM
technology.
The performance of the underwater channel can be analysed and further improved
by using Spatial Modulation (SM). The number of antennas can be increased to
improve the performance of the system.
42
REFERENCES
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45
LIST OF PUBLICATIONS
CONFERENCES
Presented a paper titled “ High Data Rate Underwater Communication
using MIMO OSDM Technology” in IEEE sponsored 3rd
International
Conference on Innovations in Information, Embedded and
Communication systems (ICIIECS’16) at Karpagam College of Engineering
held on 18th
March, 2016.
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