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

ii

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

iii

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.

iv

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.

v

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

vi

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

1

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

2

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

3

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.

4

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.

5

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

6

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

7

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

8

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,

9

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

10

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.

11

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

12

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

13

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

14

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

[1] Tadashi Ebihara, Member, IEEE, and Koichi Mizutani “Underwater Acoustic

Communication With an Orthogonal Signal Division Multiplexing Scheme in Doubly

Spread Channels”, IEEE Journal of Oceanic Engineering, vol. 39, no. 1, January 2014

[2] D.Arunkumar, M.Margarat, “OSDM Based Underwater Acoustic

Communication”, International Journal of Innovative Research in Computer and

Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3,

Issue 3, March 2015

[3] T. Ebihara, “Single-stream Communication Using Orthogonal Signal Division

Multiplexing with Multiple Antennas”, Progress In Electromagnetics Research

Symposium Proceedings, Taipei, March 25-28, 2013

[4] Baosheng Li, Student Member, IEEE, Jie Huang, Shengli Zhou, Member, IEEE,

Keenan Ball, Milica Stojanovic, Member, IEEE, Lee Freitag, Member, IEEE, and

Peter Willett, Fellow, IEEE, “MIMO-OFDM for High Rate Underwater Acoustic

Communications”, IEEE JOURNAL OF OCEANIC ENGINEERING (TO APPEAR)

[5] Arashpal Chahal, Amandeep Kaur, Ranjeet Singh, “An Adaptive MIMO-OSDM

Prototype for Next Generation Communication Systems under fading Channels”

International Journal of Engineering Research and General Science Volume 3, Issue 1,

January-February, 2015 .

[6] Tadashi Ebihara “High-Speed Underwater Acoustic Communication Using

Orthogonal Signal Division Multiplexing: A MIMO Approach”, Proceedings of

Symposium on Ultrasonic Electronics, Vol. 33 (2012) pp. 509-510 , 13-15 November,

2012

[7] Hamada Esmaiel, Danchi Jiang, “ Multicarrier Communication for Underwater

Acoustic Channel”, Int. J. Communications, Network and System Sciences, 2013, 6,

361-376

43

[8] Ritu Gupta, Megha Kataria, “Spatial Modulation Based MIMO-OSDM for 4G

Wireless Systems under Rayleigh Fading Channel”, International Journal of

Emerging Technology and Advanced Engineering, ISSN 2250-2459, ISO 9001:2008

Certified Journal, Volume 4, Issue 7, July 2014

[9]Tadashi Ebihara, “Experiment of Underwater Acoustic Communication Using

Orthogonal Signal Division Multiplexing and Diversity Technique in a Harbor”,

Proceedings of Symposium on Ultrasonic Electronics, Vol. 35 (2014) pp. 353-354, 3-

5 December, 2014

[10] James Preisig, “Acoustic Propagation Considerations for Underwater Acoustic

Communications Network Development”.

[11] K. Saraswathi, Netravathi K A, “A Study on channel modeling of underwater

acoustic communication”, International Journal of Research in Computer and

Communication Technology, Vol 3, Issue 1, January- 2014

[12] Ram Pattarkine, IEEE MEMBER, “High Rate OFDM Acoustic Link for

Underwater Communication”.

[13] Chengsheng Pan, “Modeling and simulation of channel for Underwater

Communication Network”, International Journal of Innovative Computing,

Information and Control ICIC International 2012 ISSN 1349-4198 Volume 8,

Number 3(B), March 2012.

[14] Milica Stojanovic, Northeastern University, “Underwater Acoustic

Communication Channels: Propagation Models and Statistical Characterization”

[15] K.S. Chitra, “ Underwater Communication Implementation with OFDM” , at

Indian Journal of Geo Marine science.

[16] P. C. Etter, Underwater Acoustic Modeling and Simulation, 3rd ed. Abingdon,

Oxfordshire, U.K.: Spon Press, 2003, p. 94.

[17] M. Stojanovic, “Recent advances in high-speed underwater acoustic

communications,” IEEE J. Ocean. Eng., vol. 21, no. 2, pp. 125–136, Apr. 1996

44

[18] T. Ebihara and K. Mizutani, “Study of Doppler shift correction for underwater

acoustic communication using orthogonal signal division multiplexing,” Jpn. J. Appl.

Phys., vol. 50, 2011, DOI: 10.1143/JJAP.50.07HG06.

[19] A. Goldsmith, Wireless Communications. Cambridge, U.K.: Cambridge Univ.

Press, 2005, p. 367.

[20] N. Suehiro, C. Han, T. Imoto, and N. Kuroyanagi, “An information transmission

method using Kronecker product,” in Proc. IASTED Int. Conf. Commun. Syst. Netw.,

2002, pp. 206–209.

[21] N. Suehiro, R. Jin, C. Han, and T. Hashimoto, “Performance of very efficient

wireless frequency usage system using Kronecker product with rows of DFT matrix,”

in Proc. IEEE Inf. Theory Workshop, 2006, pp. 526–529

[22] Dusan Mitae, “OFDM as a possible modulation technique for multimedia

applications in the range of mm waves”.

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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