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COOPERATIVE SPATIAL MULTIPLEXING IN WIRELESS SENSOR NETWORKS

FADILA BINTI MOHD ATAN

This report submitted in the partial fulfillment of the requirement for the award of Bachelor of Electronic Telecommunication Engineering With

Honours.

Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka

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FAKULTI KEJURUTERAAN ELEKTRONIK DAN KEJURUTERAAN KOMPUTER

BORANG PENGESAHAN STATUS LAPORAN

PROJEK SARJANA MUDA II

Tajuk Projek : COOPERATIVE SPATIAL MULTIPLEXING IN WIRELESS SENSOR NETWORKS

Sesi Pengajian : 2009/2010

Saya ……… FADILA BINTI MOHD ATAN……….

mengaku membenarkan Laporan Projek Sarjana Muda ini disimpan di Perpustakaan dengan syarat-syarat kegunaan seperti berikut:

1. Laporan adalah hakmilik Universiti Teknikal Malaysia Melaka.

2. Perpustakaan dibenarkan membuat salinan untuk tujuan pengajian sahaja.

3. Perpustakaan dibenarkan membuat salinan laporan ini sebagai bahan pertukaran antara institusi

pengajian tinggi.

4. Sila tandakan ( ) :

SULIT*

(Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972)

TERHAD* (Mengandungi maklumat terhad yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)

TIDAK TERHAD

Disahkan oleh:

__________________________ ___________________________________

(TANDATANGAN PENULIS) (COP DAN TANDATANGAN PENYELIA)

Alamat Tetap: ………...

.………

Tarikh: ……… Tarikh: ………

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DECLARATION

“This is hereby declared that all materials in this thesis are my own work and all the

materials that have been taken from some references have been clearly

acknowledged in this thesis.”

Signature :………..

Author : Fadila Binti Mohd Atan

Date :………..

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“I hereby declare that I have this report and in my opinion this report is sufficient in

terms of the scope and quality for the award of Bachelor of Electronic

Telecommunication Engineering With Honours.”

Signature :………

Supervisor‟s Name: Mohd Riduan Bin Ahmad

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ACKNOWLEDGEMENT

In the name of ALLAH the most merciful and with the help of ALLAH. All

good inspirations, devotions, good expressions and prayers are to ALLAH whose

blessing and guidance have helped me throughout entire this project.

The gratitude also conveyed to my supervisor, Mr. Mohd Riduan b. Ahmad

for his unending guidance and patience that help me through the making of this

thesis from the initial stage until the completion of the project. His guidance,

comments and ideas regarding to this project has made this thesis an educational and

fun memories.

.

My gratitude appreciation also goes to my beloved parent and family that

give a tremendous support and great encouragement that resulted in my project

progression. Special thanks to my colleagues and friends that always give support

and cooperation.

Last but not least, I like to take this opportunity to express my appreciation to

all the individuals for giving me the courage, perseverance and advice during the

progression of this project.

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ABSTRACT

Wireless sensor networks are an enabling technology for many future

surveillance-oriented applications. Before a practical wireless sensor network is

realized, however, significant challenges must be overcome. Chief among the

obstacles to netted sensors is providing low power, robust communications between

sensor nodes. Multiple Inputs, Multiple Output (MIMO) communication promises

performance enhancements over conventional Single Input and Single Output (SISO)

technology for the same radiated power

MIMO is the use of multiple antennas at both the transmitter and receiver to

improve communication performance. Wireless networking is now commonly

associated with MIMO because it offers significant increases in data throughput and

link range without additional bandwidth or transmit power.

Spatial multiplexing is a transmission technique in MIMO wireless

communication to transmit independent and separately encoded data signals from

each of the multiple transmits antennas. Therefore, the space dimension is reused, or

multiplexed, more than one time. The wireless networking is modeled by simulation

run by Matlab software.

This project aims to study the performance of this technique on certain

parameter and compared to other transmission technique in wireless network for

example SISO technique. The parameters to be examined are delay, bit error rate

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ABSTRAK

Rangkaian sistem pengesan tanpa wayar digunakan dengan meluasnya pada

hari ini bagi tujuan pengawasan. Bagi menyediakan sistem tanpa wayar yang

berkualiti tinggi satu halangan utama perlu diatasi iaitu sistem yang disediakan

perlulah stabil (mempunyai kebaragkalian bit error yang rendah) dan beroperasi

dengan sumber kuasa yang rendah. Multiple Input and Multiple Output (MIMO)

digunakan untuk menyediakan system ini.

MIMO adalah sistem dimana terdapat beberapa antena penghantar dan

penerima berbanding sistem Single Input and Single Output (SISO) yang hanya

mempunyai satu antena penghantar dan penerima. MIMO menyediakan rangkaian

pengesan tanpa wayar yang lebih pantas tetapi pada masa yang sama masih

beroperasi dengan penggunaan tenaga yang sama dengan SISO.

Teknik Spatial Multiplexing adalah salah satu teknik modulasi yang

digunakan dalam MIMO. Secaara amnya teknik ini menghantar data dari antena-

antena secara serentak pada satu masa. Data-data yang dihantar ini juga berbeza

antara satu sama lain. Justeru itu ia dapat meningkatkan kelajuan operasi sistem dan

mengurangkan kebarangkalian bit eror. Simulasi system ini dilakukan didalam

perisian Matlab.

Projek ini akan mengaji 3 aspek sistem penghantaran MIMO dan

membandingkannya dengan SISO. Aspek- aspek yang diuji adalah tempoh masa

penghantaran, kebarangkalian berlaku bit eror dan tahap penggunaan tenaga.

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TABLE OF CONTENTS

CHAPTER PAGE

DECLARATION i

ACKNOWLEDGEMENT iii

ABSTRACT iv

ABSTRAK v

TABLE OF CONTENTS vi

LIST OF FIGURES ix

LIST OF TABLE xi

ABBREVIATION xii

CHAPTER 1

INTRODUCTION

1.1 Project Background 1

1.2 Scope of Works 2

1.3 Problems statement 3

1.4 Significant of the Project 4

1.5 Orientation of the Thesis 4

CHAPTER 2

SPATIAL MULTIPLEXINGTHEORY

2.1 Introduction of Wireless Sensor Network 6

2.1.1 Wired Networking 6

2.1.2 Wireless Networking 7

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2.2 Sensor Network 8

2.3 Network Topology 8

2.3.1 Mesh Networks 10

2.4 Transmission Scheme 11

2.4.1. Multiple Input Multiple Output (MIMO) Definition 11

2.4.2 Cooperative MIMO (CO-MIMO) 12

2.4.3 Single Input Single Output (SISO) Definition 13

2.5 Raleigh Fading 14

2.6 Bit Error Rate 15

2.7 Minimum Mean Squared Error (MMSE) 17

2.8 Spatial Multiplexing in MIMO Wireless System 17

2.8.1 VBLAST Architecture 18

2.9 Performance Analysis of Cooperative MIMO System 22

2.9.1 Error Performance Modeling. 22

2.9.2 Energy Performance Modeling. 24

2.9.3 Delay Performance Modeling. 27

2.10 Maximizing data rate using spatial multiplexing 28

2.11 Multiplexing vs. Diversity 28

CHAPTER 3

METHODOLOGY

3.1 Introduction 31

3.2 MATLAB Software 33

3.2.1 Introduction 33

3.2.2 History 33

3.2.3 Syntax 34

3.2.3.1 Variables 34

3.2.3.2 Vectors/matrices 35

3.2.3.3 Semicolon 37

3.2.3.4 Placing comments 37

3.2.3.5 FOR statement 38

3.2.3.6 Data Plot 39

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3.3 Script Explanation 40

3.3.1 BER Comparison Script 40

3.3.2 Delay Comparison Script. 41

3.3.3 Energy Comparison Script. 41

CHAPTER 4

RESULTS AND DISCUSSIONS

4.1 Simulation Results 42

4.1.1 BER Comparison Performance Analysis 44

4.1.2 Energy Consumption Performance Analysis 46

4.1.3 Delay Comparison Performance Analysis 43

4.2 Summary 48

CHAPTER 5

5.1 Conclusion 50

5.2 Future Work 51

REFERENCES

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LIST OF FIGURES

NO TITLE

2.1 Bus Topology 9

2.2 Star Topology 9 2.3 Ring Topology 10 2.4 Mesh Topology 10 2.5 MIMO Transmission System 12

2.6 Comparison between Cooperative and Traditional MIMO 13

2.7 SISO Transmission System 14

2.8 V-BLAST high level system diagram 19 2.9 VBLAST multiplexing with M transmit and N receive antenna 21 2.10 Cooperative SM withM transmits and N receives antenna 21

3.1 Flow Chart 33 3.2 Command Window 35

3.3 Examples of variables 36 3.4 Example without array 37

3.5 Example with array 37

3.6 Array from BER comparison and Semicolon 38

3.7 Example of comments 39

3.8 Example for Statement 40

3.9 Example of plotting script 40 3.10 Output Result 41

4.1 BER vs. transmission power for SM Scheme with various

M = 2,4,6,8 and N = 2,4,6,8 44

4.2 Energy Consumption vs. transmission power for SM

Scheme with M=2 and N=2 45

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4.3 Energy Consumption vs. transmission power for

SM Scheme with various M = 2,4,6,8 and N = 2,4,6,8 46

4.4 Delay Comparison vs. transmission power for SM

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LIST OF TABLE

TABLES TITLE PAGE

4.1 System Parameter for BER and PER modeling 30

4.2 System Parameter for Energy Comparison modeling 30

4.3 System Parameter for Delay Comparison modeling 31

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ABBREVIATION

ACK - Acknowledgment

ADC - Analogue-to-Digital Converter

BER - Bit Error Rate

BF - Beamforming

BLAST - Bell Labs Layered Space-Time

BPSK - Binary Phase Shift Keying

DAC - Digital-to-Analogue Converter

DATA - Data Packet

DBLAST - Diagonal Bell Labs Layered Space-Time

HBLAST - Horizontal Bell Labs Layered Space-Time

IEEE - Institute of Electrical and Electronics Engineers

IFA - Intermediate Frequency Amplifier

LNA - Low Noise Amplifier

MIMO - Multiple-Input Multiple-Output

ML - Maximum Likelihood

MMSE - Minimum Mean Square Error

PDF - Probability Density Function

PER - Packet Error Rate

PSD - Power Spectral Density

QoS - Quality of Service

RX - Receiver

SISO - Single-Input Single-Output

SM - Spatial Multiplexing

SNR - Signal-to-Noise Ratio

STBC - Space-Time Block Coding

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VBLAST - Vertical Bell Labs Layered Space-Time

WLAN - Wireless Local Area Network

WSN - Wireless Sensor Network

ZF - Zero Forcing

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CHAPTER 1

INTRODUCTION

1.1 Projects Background

Over the past decade or so an unprecedented growth in the demand for

providing reliable, high-speed wireless communication in order to support a wide

range of application is exponentially increased [1]. Providing such reliable links is

challenging due to the fact that in wireless environment, unlike many other channels,

transmitted signals are received through multiple paths which usually add

destructively resulting in serious performance degradation. Furthermore, the medium

is normally shared by many different users/applications, thus there is the possibility

of interference.

MIMO based communication architectures have recently been proposed as a

means for improving energy efficiency of energy-constraint, distributed wireless

sensor networks. There are many different schemes that can be applied to the MIMO

system. MIMO systems demand powerful signal processing procedures to recovers

the signal transmitted by the antenna arrays, thus the potential advantages of the

MIMO system can be guaranteed and the MIMO system will work in the best

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Among the different signal processing algorithms that exist for the MIMO

system, the V-BLAST algorithm from the spatial multiplexing technique has become

a potential alternative due to its excellent complexity-performance tradeoff.

The main challenge for Wireless Sensor Network (WSN) is to produce

powerful yet low cost and tiny sensor nodes. It is important to acknowledge that all

the processing of a node is normally depends solely on the battery. The sensor node

lifetime can be estimated from the ratio of the battery capacity to the energy usage

per second by the sensor node. Thus in order to elongate the sensor node lifetime, the

usage of energy consumed by the nodes must be minimized.

However there is a trade-off between minimizing the energy with the

performance of the WSN. Should ever the energy consumed is set too low there will

be degrading on the algorithm. Thus causing the information to be sent to have

significant delay or in worst case scenario the information is totally loss [2].

This project aims to study the fundamental of MIMO Multiplexing technique

in particular the Spatial Multiplexing (SM) technique and compares the results with

conventional SISO technique in terms of delay and energy consumption.

There are several objectives that need to be considered in this project such that:

 To design and simulate SM algorithm using Matlab simulator.

 To evaluate the performance of SM technique in terms of delay and

energy consumption.

 To compare SM performance with Single Input and Single Output (SISO)

system.

1.2 Scope of Works

In this project, the Spatial Multiplexing Technique is designed by Matlab

software. The simulation is carried out until the result obtained meets the required

specifications. Finally, the comparison between the simulation result and existing

system is investigated.

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1.3.1 Problems Statement

Common radio devices use a single-input, single-output (SISO) configuration

with one transmitter and one receiver and information sent over a single data

channel. However, a move is under way from single-carrier technologies that

transmit one digital symbol at a time to new methods that can potentially transmit

hundreds of symbols simultaneously. One such method is input

multiple-output (MIMO).

The main challenge of WSN is to produce powerful yet low cost and tiny sensor

nodes. This means less energy consumption due to the fact that the sensor operates

solely on the tiny battery which it is installed. Another aspect to be considered is the

fact that the design network should have minimum time delay to achieved better data

throughput.

Multipath effects can degrade a SISO transmission. For example, a Bluetooth

signal with a symbol rate of 1M symbols per second must receive a symbol within a

window of one microsecond. If multipath effect delays the signal by more than this, a

significant symbol error will occur. MIMO systems, on the other hand, require

multiple paths. If two signals are transmitted with known characteristics, for instance

a header, at the receiver end, one can assume what the signal should look like and

create a model of the channel effects. When the unknown signal comes, i.e. the data,

subtracting the channel effects can solve for the transmitted symbols. [3]

The relationship between symbol error probabilities to the data rate is crucial. As

the invention of MIMO is indeed to provide more stable and less error link it is in

need of low error rates. This is unachievable by SISO as there is only one channel

path used. As mention above, the WSN should operate in relatively low power. This

comes to another relationship between error rate and energy consumption. Whenever

error is beyond repair at demodulator, the request for retransmission is entered.

Retransmission requires more energy usage and eventually added the usage of power

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From the discussed theory above, MIMO transmission is proposed to build low

power consumption with fast and reliable link. The SM technique is one of the

techniques used in MIMO transmission. The performance of SM is investigated and

the comparison is done with respect to SISO.

1.4 Significant of the Project

The project proposed the SM technique in MIMO transmission environment.

The proposed way is expected to provide fast and reliable wireless link with

relatively low power consumption. The technique is more powerful than SISO in

terms of BER and Delay time, with the same or slight increased in power

consumption. Thus the intention of providing stable and low consumption network is

achieved.

1.5 Orientation of the Thesis

The thesis is organized into 5 main chapters focusing on the SM technique in

WSN environment. Chapter two gives the introduction to WSN, comparison between

wired and wireless network, followed by fading channels and diversity techniques.

Also, the sensor network is explained with conjunction of network topology. The

fading channel considered in this thesis is quasi-static fading. The diversity

techniques is discussed based on energy saving and less delay attributes. Two

common diversity associated with SM performance analysis are diversity gain and

multiplexing gain. Then the thesis follows on the aspects of MIMO communication

theory. This includes error probability, channel modeling, the comparison between

traditional and cooperative MIMO and the employment of SM in MIMO.

Chapter 3 yields methodology of the project. This includes the flow chart, Matlab

environment, and script of the actual simulation code. All discussion focuses on

running the project smoothly. The Matlab discussion includes the history, usage, and

examples on syntax exploitation to be used on the real coding script. Move on then,

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the script is discussed thoroughly to yields all the connections and relationship to the

parameters examined.

Chapter 4 produces the result of simulations depending on the parameter

reviewed. The output graphs are discussed and relates to their respective theories.

Discussion is made afterward comparing all graph to three major concern; delay,

BER and energy consumption. The last chapter concludes all with a summary of the

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CHAPTER 2

SPATIAL MULTIPLEXING THEORY

2.1 Introduction of Wireless Sensor Network

Wireless Sensor Networks consist of small nodes with sensing, computation

and wireless communication capabilities. Various architectures and node deployment

strategies have been developed for wireless sensor network, depending upon the

requirement of application. Sensor networks are used in different applications e.g.

environmental monitoring, habitat monitoring, home automation, military application

etc [3].

2.1.1 Wired Networking

In a wired networking, coaxial cable or special grades of twisted pair wires

and network adapters connect the devices. Back in the dawn of computing, two

computers were directly wired to each other using a crossover cable. In order to

accommodate the demands of a network and connect more computers, central

devices like hubs, switches, or routers have evolved [3].

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2.1.2 Wireless Networking

Wireless networking send and receive data through the air and use radio and

infrared waves to transmit information without a physical connection. The access

point receives, buffers and transmits data between the wireless and the wired network

infrastructure. A single access point can support a small group of users and can

function within a range of less than one hundred to several hundred feet [3].

2.1.3 WLAN and Wired LAN Comparison

In a wired network, the cables that connect each computer can be

inconvenient because they need to be hidden in walls or discreetly laid out in the

open so they don‟t become a hazard or an annoyance. On the other hand, cables,

hubs and switches are very inexpensive while wireless equipment may cost three or

four times as much.

For Local Area Network, wired LANs offer bandwidth up to 100 Mbps.

Wireless LANs using 802.11b support a maximum bandwidth of 11 Mbps, while

802.11a and 802.11g WLANs support only 54 Mbps. On the other hand, the greater

mobility of wireless networks can compensate for the performance disadvantage. In

addition, the same Wi-Fi adapter that connects laptop to WLAN will connect to the

hotspots that becoming more ubiquitous every day [9].

Technically, wired networking is more secure than wireless network. Since

wireless signals are transmitted through the air, devices outside of the network can

capture them. However, the majority of wireless network today protect their data

with the Wired Equivalent Privacy (WEP) encryption standard, which makes

wireless communications almost as safe as wired ones [9].

To sums up the comparison, Wired Network has been around for a long time

and wireless network is a way to modernize the network. It allows a simple matter to

relocate a communicating device, and no additional cost of rewiring and excessive

downtime is associated with such a move. It is also a simple matter to add in a

communication device to the system or remove one from the system without any

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the cell sites, the cost of running and maintaining a radio based communications

solution is minimal.

2.2 Sensor Network

The sensor network serves in the challenges ofdetecting the relevant

quantities, monitoring and collecting the data, assessing and evaluating the

information, formulating meaningful user displays, and performing decision-making

and alarm functions are enormous. The usage of wireless technology is commonly

associated to sensor network due to the fact that today‟s environment is increasingly

open to more studies and wireless capability makes it easier for researchers to

gathered useful data without actually are there. Wireless enable for example fireman

to know there is a fire in the jungle without has to spend hours to locate the estimated

area burn [2].

2.3 Network Topology

The basic issue in communication networks is the transmission of messages

to achieve a prescribed message throughput (Quantity of Service) and Quality of

Service (QoS) [10]. QoS can be specified in terms of message delay, message due

dates, bit error rates, packet loss, and economic cost of transmission or transmission

power. Networking topologies can be:

i. Star

ii. Ring

iii. Bus

iv. Tree

v. Mesh

vi. Fully Connected

Mesh networks are regularly distributed networks that generally allow

transmission only to a node‟s nearest neighbors. The nodes in these networks are

generally identical, so that mesh nets are also referred to as peer-to-peer (see below)

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nets. Mesh nets can be good models for large-scale networks of wireless sensors that

are distributed over a geographic region, e.g. personnel or vehicle security

surveillance systems. Note that the regular structure reflects the communications

topology; the actual geographic distribution of the nodes need not be a regular mesh.

Since there are generally multiple routing paths between nodes, these nets are robust

to failure of individual nodes or links. An advantage of mesh nets is that, although all

nodes may be identical and have the same computing and transmission capabilities,

certain nodes can be designated as „group leaders‟ that take on additional functions.

[image:24.595.188.451.277.394.2]

If a group leader is disabled, another node can then take over these duties [11].

Figure 2.1: Bus Topology [11]

[image:24.595.222.395.460.617.2]

Figure

Figure 2.1: Bus Topology [11]

References

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