ii

The copyright of this report belongs to the author under the

terms of the Copyright Act 1987 as qualified by Regulation 4(1)

of the Multimedia University Intellectual Property Regulations.

Due acknowledgement shall always be made of the use of any

material contained in, or derived from, this report.

iii

Declaration

I hereby declare that this work has been done by myself and no portion of the work

contained in this report has been submitted in support of any application for any

other degree or qualification of this or any other university or institute of learning.

I also declare that pursuant to the provisions of the Copyright Act 1987, I have not

engaged in any unauthorised act of copying or reproducing or attempt to copy /

reproduce or cause to copy / reproduce or permit the copying / reproducing or the

sharing and / or downloading of any copyrighted material or an attempt to do so

whether by use of the University’s facilities or outside networks / facilities whether

in hard copy or soft copy format, of any material protected under the provisions of

sections 3 and 7 of the Act whether for payment or otherwise save as specifically

provided for therein. This shall include but not be limited to any lecture notes,

course packs, thesis, text books, exam questions, any works of authorship fixed in

any tangible medium of expression whether provided by the University or otherwise.

I hereby further declare that in the event of any infringement of the provisions of the

Act whether knowingly or unknowingly the University shall not be liable for the

same in any manner whatsoever and undertakes to indemnify and keep indemnified

the University against all such claims and actions.

Signature: ________________________

Name: Chong Wai Kean

Student ID: 1081101543

Date: 7th

January 2013

iv

Acknowledgements

First of all, I would like to express my gratitude towards my project

supervisor, Assoc Prof. Dr. Mohamad Yusoff Bin Alias for accepting me with my

proposed title in this final year project under his supervision. I would like to thank

him for his time contribution to have discussion with me weekly and giving

continuous guidance throughout my progress in my final year project. His valuable

suggestions and advices enabled me to overcome challenges in this project in order

to further improve the quality of this project. Moreover, I would like to thank him

for providing me assistance in term of project tool such as computer and a lab as my

workplace in order for me to accomplish my project smoothly.

Secondly, I would like to thank my co-supervisors, Dr. Hafizal Mohamad

and Dr. Nordin Ramli from Wireless Communication Cluster (WCC) in MIMOS

BERHAD. I would like to thank both of them for lending me a dongle with licensed

simulation software, Qualnet5.2 so that I am able to do simulation for my project at

anywhere. Moreover, they are willing to share their knowledge which is useful for

my project.

Thirdly, I would like to thank my moderator, Dr. Aymen Mohammed

Kareem for his constructive comments and questions about my projects, which help

me to improve my presentation skills and the quality of my final year project.

Fourthly, I would like to thank Miss. Yip Sook Chin, Ahmed and Nazmus

Saadat who helped me a lot on using Qualnet simulation software and also in term

of programming.

Lastly, I would like to take this opportunity to express my most sincere

gratitude to my family and friends who always giving me support and

encouragement during the period of my final year project.

v

Abstract

In the rapid advancement of the wireless technology nowadays, the demand

of the spectrum utilization is increasing dramatically to meet the requirement for

high-speed wireless services. The current static spectrum allocation policy incurs the

spectrum congestion bottlenecks and underutilization of spectrum band. In order to

solve the problems of spectrum usage inefficiency and scarcity, a new technology

namely cognitive radio (CR) is proposed. CR technology enables the secondary user

(SU) to temporarily utilize the unoccupied license channel without interference with

primary user (PU). SU has the ability to vacant the channel and switch to another

unused channel if PU suddenly become active and occupies the respective channel.

One of the main difficulties in a cognitive radio network (CRN) is that the SU

should have the awareness towards the presence of PU to reduce the interference in

licensed communication. This project presents a novel proposed routing scheme

which makes the SU aware of and consider the activity of PU to perform proper

dynamically channel switching to optimize the performance in SU without influence

the performance in PU. Another challenge in a CRN is that PU might be the primary

exposed node (PEN) and/or primary hidden node (PHN) to the secondary users. The

proposed routing scheme generates a channel list namely gamma channel list (GCL)

which can solve the PEN and PHN problem. Moreover, the proposed routing

scheme generates delta channel list (DCL) whereby the channel presented in the list

will be used by the SU for communication to optimize its performance in different

types of scenario without interference with PU and also able to avoid PHN and PEN

problems. The proposed routing scheme is able to lower the probability of packet

lost in SU in order to reduce its average end-to-end delay which can be shown in the

four models for the first scenario. The second model shows the minimum

improvement on the delay in SU which is about 0.35ms, whereas the fourth model

shows the maximum improvement on the performance of SU in term of end-to-end

delay which is as high as 2.78ms. Moreover, the proposed routing scheme also

maintains the throughput of SU. Finally, the simulation results of the proposed

vi

routing scheme show the satisfactory performance as compared to the traditional

AODV routing protocol.

vii

Table of Contents

Declaration .... ............................................................................................................... iii

Acknowledgements ....................................................................................................... iv

Abstract ......... ................................................................................................................ v

Table of Contents ........................................................................................................ vii

List of Figures ................................................................................................................ x

List of Tables ............................................................................................................. xiii

List of Abbreviations .................................................................................................. xiv

List of Mathematical Symbols ................................................................................... xvi

CHAPTER 1: INTRODUCTION ................................................................................. 1

1.1 Research Motivation ............................................................................................ 2

1.2 Research Objective .............................................................................................. 3

1.3 Research Scope .................................................................................................... 4

1.4 Research Timeline ............................................................................................... 4

1.5 Research Contribution ......................................................................................... 7

1.6 Structure of Thesis ............................................................................................... 8

CHAPTER 2: LITERATURE REVIEW ..................................................................... 9

2.1 Wireless Mesh Network ....................................................................................... 9

2.1.1 IEEE 802.11s .............................................................................................................. 10

2.1.2 Network Architecture ................................................................................................. 11

2.1.3 Mesh Basic Service Set .............................................................................................. 11

2.2 Cognitive Radio ................................................................................................. 11

2.2.1 Features of Cognitive Radio ....................................................................................... 13

2.2.2 Types of Cognitive Radio ........................................................................................... 14

2.2.3 Functions of Cognitive Radio ..................................................................................... 14

2.2.3.1 Spectrum Sensing ................................................................................................. 14

2.2.3.2 Spectrum Decision ............................................................................................... 15

2.2.3.3 Spectrum Sharing ................................................................................................. 15

Deleted: vi

Deleted: ix

Deleted: xi

Deleted: xii

Deleted: xiv

viii

2.2.3.4 Spectrum Mobility ............................................................................................... 17

2.3 Cognitive Radio Mesh Network (COMNET) ..................................................... 18

2.4 Routing .............................................................................................................. 18

2.4.1 AODV Routing Protocol ............................................................................................ 19

2.5 Summary of Research Papers ............................................................................. 19

CHAPTER 3: PROPOSED SPECTRUM AWARE ROUTING SCHEME .............. 23

3.1 Overview ........................................................................................................... 23

3.2 Channel List Formation ..................................................................................... 27

3.2.1 Beta Channel List ....................................................................................................... 27

3.2.2 Gamma Channel List .................................................................................................. 28

3.2.3 Delta Channel List ...................................................................................................... 29

3.3 Wireless Mesh Network Based Scenario ............................................................ 30

3.4 System Models with Parameters ........................................................................ 33

3.4.1 System Models............................................................................................................ 33

3.4.2 Parameters................................................................................................................... 37

3.4.2.1 Parameters for Traditional AODV Implementation ............................................. 37

3.4.2.1.1 First Scenario ................................................................................................ 37

3.4.2.1.2 Second Scenario ........................................................................................... 37

3.4.2.1.3 Third Scenario .............................................................................................. 38

3.4.2.1.4 Fourth Scenario ............................................................................................ 38

3.4.2.2 Parameters for Proposed Routing Scheme Implementation ................................. 39

3.4.2.2.1 First Scenario ................................................................................................ 39

3.4.2.2.2 Second Scenario ........................................................................................... 40

3.4.2.2.3 Third Scenario .............................................................................................. 41

3.4.2.2.4 Fourth Scenario ............................................................................................ 41

3.4.2.2.5 Fifth Scenario ............................................................................................... 41

CHAPTER 4: SIMULATIONS AND ANALYSIS ..................................................... 42

4.1 Preliminary Results ............................................................................................ 42

4.1.1 First Scenario .............................................................................................................. 43

4.1.2 Second Scenario.......................................................................................................... 43

4.1.3 Third Scenario ............................................................................................................ 44

4.1.4 Fourth Scenario ........................................................................................................... 44

4.2 Results for Proposed Routing Scheme Evaluation .............................................. 45

ix

4.2.1 First Scenario .............................................................................................................. 45

4.2.2 Second Scenario.......................................................................................................... 50

4.2.3 Third Scenario ............................................................................................................ 53

4.2.4 Fourth Scenario ........................................................................................................... 54

4.2.5 Fifth Scenario.............................................................................................................. 55

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS .............................. 58

5.1 Conclusion......................................................................................................... 58

5.2 Recommendation ............................................................................................... 59

5.2.1 Parameter Setting ........................................................................................................ 59

5.2.2 Comparison between Proposed Routing Scheme with Different Routing

Protocols ................................................................................................................................. 60

5.2.3 Different Types of Scenario and Model ..................................................................... 60

References ......... .......................................................................................................... 61

Appendix A – Patent Filing ......................................................................................... 64

x

List of Figures

Figure 1.1: Spectrum utilization [2] ......................................................................... 1

Figure 2.1: Traditional WLAN architecture ........................................................... 10

Figure 2.2: Wireless mesh network architecture [9] ............................................... 10

Figure 2.3: Dynamic spectrum access [14] ............................................................ 12

Figure 2.4: Cognitive cycle [3] .............................................................................. 13

Figure 2.5: Spectrum sensing classification [4] ...................................................... 15

Figure 2.6: Inter- and Intra-Network spectrum sharing in CRN [4] ........................ 16

Figure 2.7: Spectrum sharing classification [4] ...................................................... 17

Figure 3.1: Point-to-point CR wireless network ..................................................... 24

Figure 3.2: Primary hidden node (PHN) ................................................................ 25

Figure 3.3: Primary exposed node (PEN) ............................................................... 25

Figure 3.4: Flow chart of overall system based on proposed routing scheme .......... 26

Figure 3.5: Flow chart of BCL formation .............................................................. 27

Figure 3.6: Flow chart of GCL formation .............................................................. 28

Figure 3.7: Flow chart of DCL formation .............................................................. 29

Figure 3.8: Scenario of wireless mesh network ...................................................... 31

Figure 3.9: Single point-to-point SU and PU network ............................................ 34

Figure 3.10: Network with 5 SUs and a single point-to-point PU sub-network ....... 34

Figure 3.11: Network with 30 PUs and 30 SUs ...................................................... 35

Figure 3.12: Network with 10 PUs or SUs and 50 SUs or PUs respectively ........... 35

Figure 3.13: Network with 2 PUs or SUs and 50 SUs or PUs respectively ............. 36

Figure 3.14: Network with 44 PUs or SUs and 50 SUs or PUs respectively ........... 36

Figure 4.1: Average end-to-end delay of SU with different scenario ...................... 43

Figure 4.2: Average end-to-end delay of SU by varying time interval of PU .......... 44

Figure 4.3: Average end-to-end delay of SU by varying the usage ratio of PU in the

model shown in Figure 3.9 ............................................................................. 46

xi

Figure 4.4: Throughput of SU versus usage ratio of PU in the model shown in Figure

3.9 .................................................................................................................. 47


model shown in Figure 3.10 ........................................................................... 47


3.10 ................................................................................................................ 48




3.11 ................................................................................................................ 49



Figure 4.10: Throughput of SU versus usage ratio of PU in the model shown in

Figure 3.12 ..................................................................................................... 50

Figure 4.11: Average end-to-end delay of SU by varying the time interval of PU in

the model shown in Figure 3.9........................................................................ 51


the model shown in Figure 3.10 ...................................................................... 51





Figure 4.15: Average end-to-end delay of SU by varying the number of PU in the


Figure 4.16: Average end-to-end delay of SU by varying the number of SU in the


Figure 4.17(a): Average end-to-end delay of SU by varying usage ratio of PU with

different number of PU in the CRN with traditional AODV routing protocol .. 56

Figure 4.17(b): Average end-to-end delay of SU by varying usage ratio of PU with

different number of PU in the CRN with proposed routing scheme ................. 57

Figure 4.18: Minimum usage ratio of PU to increase latency in SU by varying

number of PU ................................................................................................. 57

xiii

List of Tables

Table 1.1: Timeline for project part I ....................................................................... 6

Table 1.2: Timeline for project part II ...................................................................... 7

Table 2.1: Summary of research papers ................................................................. 22

Table 3.1: Spectrum availability of each CR node ................................................. 27

Table 3.2: Spectrum identity of each CR node ....................................................... 28

Table 3.3: Three channel lists between the two CR nodes ...................................... 29

Table 3.4: Spectrum availability of each node ....................................................... 31

Table 3.5: Spectrum identity of each node ............................................................. 32

Table 3.6: Three channel lists between two nodes.................................................. 32

Table 3.7: Fixed parameters for PU and SU ........................................................... 37

Table 3.8: Fixed parameters for PU and SU ........................................................... 38

Table 3.9: Varied parameter for PU ....................................................................... 38

Table 3.10: Fixed parameters for PU and SU ......................................................... 40

Table 3.11: Varied parameter for PU ..................................................................... 40

xiv

List of Abbreviations

AODV Ad-hoc on-demand distance vector

AP Access point

BCL Beta channel list

BS Base station

BSS Basis service set

CBR Constant bit rate

CH Channel

COMNET Cognitive radio mesh network

CPL Channel priority list

CR Cognitive radio

CRMN Cognitive radio mesh network

CRN Cognitive radio network

D Delay

DCL Delta channel list

DN Node delay

DORP Delay motivated on-demand routing protocol

DP Path delay

DS Distributed system

DSA Dynamic spectrum access

DSM Dynamic spectrum management

DSR Dynamic source routing

DSSS Direct-sequence spread spectrum

ESS Extended service set

FCC Federal Communication Commission

GCL Gamma channel list

xv

MAP Mesh access point

MBSS Mesh basic service set

MCRN Multi-hop cognitive radio networks

MP Mesh point

MPP Mesh portal

MSCRP Multi-hop single-transceiver CRN routing protocol

NAM Node analytical model

OFDM Orthogonal frequency-division multiplexing

PEN Primary exposed node

PHN Primary hidden node

PU Primary user

QoS Quality of service

RF Radio frequency

RKRL Radio knowledge representation language

RREP Route reply

RREQ Route request

SA Spectrum availability

SAE Simultaneous authentication of equals

SAMER Spectrum aware mesh routing

SI Spectrum identity

SOP Spectrum opportunity

SORP Spectrum aware on-demand routing protocol

STA Station

SU Secondary user

WLAN Wireless Local Area Network

WMN Wireless mesh network

xvi

List of Mathematical Symbols

� Usage ratio of the PU on the respective channel in percentage

�� Probability that the PU active on the respective channel with respect

to the maximum time duration of data transmission

�� Maximum data packets can be transmitted throughout the simulation

�� Total number of data packets to be transmitted throughout the

simulation

�� Time where the data transmission end

�� Time duration between one data transmission and another data

transmission

�� Time where the data transmission start

�� Total time duration that the PU is active and utilizing the respective

channel throughout the whole simulation

�� Maximum time duration of the data transmission

1

CHAPTER 1: INTRODUCTION

In the current generation, the wireless communication technology provides

the communication network with a high data rate. The rapid enhancement of the

wireless technology incurs the demand of the spectrums utilization to be increased

dramatically in order to accommodate the high-speed wireless services. However,

Federal Communication Commission (FCC) has invented a static spectrum

allocation policy that characterizes the wireless networks nowadays. Hence, the

government agencies assign the fixed wireless spectrums to the particular licensed

users. Due to the increasing demand on the limited spectrum, the fixed spectrum

allocation policy has been encountered with spectrum scarcity at the particular

spectrum bands. On the contrary, a study has been done by FCC whereby they

investigate the spectrum utilization in term of temporal variation and discovered that

there are 15% to 85% of the spectrum is unoccupied with the respective geographic

location [1], which leading to unutilized or underutilization of a significant amount

of the licensed spectrum bands. Base on the investigation, it shows that majority of

the allocated spectrums are unoccupied and only small portions of it are fully

utilized which can be shown in Figure 1.1 [2].

Figure 1.1: Spectrum utilization [2]

2

Base on the current policy with the inefficient spectrum utilization and the

limited spectrum availability, it is necessary to invent and develop a new wireless

technology to utilize the existing available spectrum. Hence, a dynamic spectrum

access technique is proposed to effectively deal with the inefficient of the spectrum

utilization problem [4]. The dynamic spectrum access technique is based on the idea

and the key of cognitive radio (CR) technology. CR technology enables the

unlicensed user or secondary user (SU) to perform dynamically sense and

intelligently utilize the unoccupied or underutilized spectrums, which are also

known as white spaces or spectrum holes [5]. CR technology permits the unlicensed

users to occupy the unutilized licensed spectrum band for communication without

causing interference with the licensed users or primary users (PU) which could solve

the problem for the efficiency of spectrums utilization.

This thesis presents the investigation on the routing protocols being used in

CR technology which needed for optimization. This thesis also presents the invented

proposed novel routing algorithm to optimize the performance of the unlicensed

users, which also known as CR users in cognitive radio network (CRN).

1.1 Research Motivation

Routing is one of the main keys that will influence the performance of the

unlicensed user or SU and also the licensed user or PU in the cognitive radio mesh

network. Few intensive researches are carried out in this area and different routing

protocols have been proposed. Those routing algorithms have been shown and

proven to perform and meet certain satisfactory to optimize the performance of the

unlicensed users. However, the performance of those routing protocols could not be

optimized due to the dynamically changes of the environment such as suddenly

appearance of a PU, suddenly activation of the PU by occupying the licensed

spectrum and the primary hidden node (PHN) and primary exposed node (PEN)

problems. Hence, there is a new proposed routing algorithm needed to tackle the

aforementioned problems.

Some researches have been carried out and the respective proposed routing

algorithms have been implemented to produce satisfactory results as compared to

Comment [S1]: Why do this FYP? The research

problem.

3

the traditional routing algorithm. However, those routing algorithms do not consider

the PHN and PEN problems. PHN problem will cause several data loss at the CR

receiver due to the interference by the licensed user who occupying the same

channel as the CR receiver is using. Whereas, the PEN problem will cause the

packet loss and increase the transmission delay at the CR transmitter due to the

activation of the licensed user who occupying the same channel as the CR

transmitter is using.

Moreover, most of the proposed routing protocol do not consider the

dynamically spectrum awareness. Hence, the suddenly appearance of a PU may

occupy the spectrum and render the channel unusable which will cause the route

failure for the SU. This will require the frequency rerouting for the SU to maintain

the communication which will encounter the performance degradation due to the

introduced immoderate end to end delay.

One research has been done whereby the proposed routing scheme aware of

the dynamically spectrum allocation of the PU and also able to prevent PHN and

PEN problem [6]. However, the routing scheme can only be applied in certain types

of scenario. Hence, there is another need to propose a novel routing algorithm to

optimize the performance in routing in CRN as well as to address the

aforementioned problems.

1.2 Research Objective

The main objective of this project is to propose a novel spectrum aware

routing scheme for CR mesh network which can optimize the performance of the SU

without interfering with PU and also would not affecting its performance. To be

more specific, the aims of this project are as shown in the following:

• To avoid the primary exposed node (PEN) and the primary hidden node

(PHN) problems when performing routing in different types of scenario

whereby the novel proposed routing scheme is able to reduce the

probability of packet loss and also the transmission delay.

4

• To perform dynamically channel switching if the PU suddenly become

active and occupy the licensed spectrum whereby the SU aware of the

activities of the PU and it will reduce the probability of the rerouting

which could increase the end to end delay.

• To evaluate the performance of the SU with the proposed routing scheme

as compared to the existing traditional routing algorithms.

1.3 Research Scope

The main focus of this research is on one of the main features of spectrum

management functions in CR mesh network which is spectrum decision to perform

spectrum selection. Subsequently, spectrum mobility also has to be considered in

order to perform spectrum handoff. Study the traditional and proposed routing

protocol and figure out their weaknesses when implemented into the CRN. To

develop a new routing algorithm which allows the CR users effectively occupy the

spectrum band depends on the spectrum availability and also perform dynamically

channel switching for performance optimization in CR mesh network.

1.4 Research Timeline

In order to complete this project, study in the related field was done

continuously and a new idea was proposed after the research objectives and scopes

have been stated based on the problem statements that have been figured out. A flow

of project’s development is shown in Figure 1.2, which consists a total of eight

stages. The whole project is divided into two parts, namely part I and part II. As

shown in Figure 1.2, the first five stages are carried out in part I, whereas the last

three stages will be carried out in part II. Moreover, the brief well-planned research

timeline are shown in Table 1.1 and Table 1.2 for project part I and part II

respectively.

5

Figure 1.2: Flow of project’s development

Define problem statements

Define research objectives

Literature review on cognitive radio

technology and routing protocol

Propose a new spectrum aware

routing algorithm

Implement the new routing algorithm

into cognitive radio environment

Evaluate the performance of the new

routing algorithm

Documentation

Evaluate the performance of

traditional routing protocol in

cognitive radio environment

Part I

Part II

6

Table 1.1: Timeline for project part I

No Milestones

Months of FYP Part I

1-2

week

3-4

week

5-6

week

7-8

week

9-10

week

11-12

week

13-14

week

1 Literature review on routing protocol in

cognitive radio network. X

2 Design and modeling wireless cognitive

radio network in Qualnet. X

3 Performance evaluation and computer

simulation based on traditional AODV

routing protocol in the designed network.

Expected output: Simulation results.

X X

4 Technical review on the compilation of

Qualnet with Microsoft Visual C++ 2008

Express Edition compiler.

Expected output: Familiarize of

compilation with simulation tool.

X

5 Review and understand the programming

c++ codes of the traditional AODV

routing protocol in Qualnet.

X X

6 Do modification on the AODV routing

protocol base on my designed routing

algorithm.

Expected output: Simulated results

showing the designed algorithm have

better performance compared to the

AODV.

X X

7 Documentation, project presentation

slides and project review.

Expected output: Presentation

X

7

Table 1.2: Timeline for project part II

No Milestones

Months of FYP Part II

1-2

week

3-4

week

5-6

week

7-8

week

9-10

week

11-12

week

13-14

week

1 Modification of AODV routing protocol

with additional parameters such as SA,

SI, BCL, GCL and DCL.

X X

2 Compilation, performance evaluation and

computer simulation based on the

proposed and designed routing protocol

in the designed network.

Expected output: Simulation results.

X X

3 Thesis, documentation, project

presentation slides and project review.

Expected output: Final report and

presentation

X X X

4 Preparing draft conference paper/journal

based on the above activities #1 and #2.

Expected output: Paper/Journal.

X X

1.5 Research Contribution

The contribution of this project is a novel spectrum aware routing scheme for

cognitive radio mesh network that has the following benefits:

• The ability to eliminate the PHN and PEN problems.

• Enables the unlicensed user to be aware of the activation of the licensed user

and able to dynamically switch channel.

• Provides better performance for the unlicensed user without interference

with licensed user, as compared to the traditional existing routing protocol.

Comment [S2]: Contributions of the FYP to

existing knowledge

8

1.6 Structure of Thesis

The thesis of this project is divided into several chapters as shown in follow:

Chapter 1 gives an introduction to the current spectrum utilization. Besides,

it also give an overview on the problems faced regarding to the spectrum allocation

and the highly demand on the respective spectrum bands. Moreover, it also presents

the challenges of the research and the objectives follow by proposing a new idea to

solve the aforementioned problems.

Chapter 2 provides the theoretical background and literature review on

wireless mesh network, cognitive radio technology and routing. In this chapter, the

main features of the spectrum management functions will be further discussed

whereby the spectrum decision and spectrum mobility will be emphasized.

Moreover, summary of the research papers will be presented at the end of this

chapter.

Chapter 3 will present the description of the proposed novel spectrum aware

routing scheme in detail. In this chapter, the process of generating the channel lists

such as beta channel list (BCL), gamma channel list (GCL) and delta spectrum list

(DCL) will be described with the aid of figures and tables. Furthermore, the

operation of the proposed routing scheme will be further elaborated to explain on

how it encounters the aforementioned problems in order to fulfil the objectives.

Chapter 4 will perform data presentation after implementing the proposed

routing algorithms into the environment of cognitive radio mesh network. Several

parameters of the system network will be varied to generate different graphs for

evaluation. Moreover, different types of the scenario will be constructed and the

performance of the proposed routing scheme will be evaluated by comparing to the

traditional existing routing protocol.

Chapter 5 will discuss and provide a summary for the research finding.

Furthermore, it will also provide the conclusion of the thesis. In this last chapter,

some suggestions for further improvement in term of routing are also stated for

future work.

9

CHAPTER 2: LITERATURE REVIEW

In this chapter, a brief introduction on the wireless mesh network, cognitive

radio technology and routing are given. Then, the four main functions of the

spectrum management in CR network are explained. Next, a brief description on the

existing routing protocols is given. Finally, the summary of the researched paper are

given in this chapter.

2.1 Wireless Mesh Network

Wireless mesh network (WMN) is defined as a multi-hop wireless network.

There are few advantages in WMN and one of them is that, the whole network is

wireless, hence it can form a larger scale of wireless network without adding any

wired backhaul [7]. Moreover, the mesh network consists of the ability of self-

configuring whereby network grows as devices are added and hence, the coverage is

expended with minimal configuration [7]. Furthermore, it is self-healing in WMN

whereby network continues to operate during maintenance and hence, it resilient to

single point of failure [7]. In addition, WMN do not have hierarchy whereby the

network can be modified easily and each mesh station (STA) manages its own

peering with other mesh STAs [7].

In this modern society nowadays, the wireless communication technology

has become essential to us. It can be seen and proven that the Wireless Local Area

Network (WLAN) technology is highly demanded as it enables ubiquitous wireless

connectivity. Due to the reason of highly demanding of WLAN, the coverage of

WLAN is needed to be expanded and more Access Points (APs) are needed to be

set-up. Base on the current WLAN technology, those APs are interconnected to the

fixed distributed system (DS) with wired in the backhaul in order to connect to the

internet. Hence, in order to expand the WLAN coverage, not only AP has to be

added, the fixed distribution system in the backhaul also has to be expanded and this

set-up is costly due the complexity in term of installation. The traditional WLAN

network architecture is shown in Figure 2.1. Basis Service Set (BSS) is defined as a

set where the station or client provides an integration service to other stations

Comment [S3]: Thorough literature review

10

(STAs) through an access point (AP). On the other hand, Extended Service Set

(ESS) is formed whereby all the stations can roam from one BSS to another through

the fixed DS, which also refers to the entire backhaul and access network.

Figure 2.1: Traditional WLAN architecture

2.1.1 IEEE 802.11s

IEEE 802.11s is a new WLAN standard which is introduced to provide

necessary functions to form a WMN [8]. The main idea of implementing IEEE

802.11s standard in a system is to eliminate the fixed DS as shown in Figure 2.1 and

interconnects all deployed APs in a mesh topology as shown in Figure 2.2 [9]. In

this mesh topology, the distributed system can be eliminated and APs are still

interconnected in the backhaul wirelessly with one or more APs as the gateway is

connected to Internet or external network. Moreover, the backhaul mesh setup, client

mesh setup and combination of both are enabled base on WMN concept as shown in

Figure 2.2. Besides, it provides a better support for peer to peer application [10].

Figure 2.2: Wireless mesh network architecture [9]

MBSS

11

2.1.2 Network Architecture

The topologies of wireless mesh network are shown in Figure 2.2. In the

network, the wireless client is known as mesh station or mesh client. Mesh Point

(MP) is an IEEE 802.11 station which has the mesh capabilities and relay frame

with each other in a router-like hop by hop fashion. However, MP that have

additionally access functionality such as mesh relaying and access point service for

mesh clients is known as mesh access point (MAP). Whereas, the MP that connected

to the Internet gateway or acting as a brige to other networks is known as mesh

portal (MPP). Mesh Basic Service Set (MBSS) is formed based on the

interconnections of the mesh station and MAP in the network. In IEEE 802.11s

standard of WMN, it enables the non-mesh station or the stations from the

traditional WLAN to be connected as a part of the mesh network through the MAP.

2.1.3 Mesh Basic Service Set

In IEEE 802.11s standard, a simple wireless mesh network consists of three

core functions which are mesh discovery, mesh peering and mesh security. Mesh

discovery is occurred when a mesh station boots up, it first identify and locates

neighbouring mesh station. The identification is done by using a traditional

mechanism in IEEE 802.11standard such as passive scan or active scan which to use

beacon frames or to use probe request or response respectively. After the mesh

discovering is being done, mesh peering is then performed to establish connection

between neighbouring stations. Mesh security is to protect the connection between

these mesh stations. The method used for mesh security is known as Simultaneous

Authentication of Equals (SAE) which is implemented before mesh peering but after

mesh discovery [7].

2.2 Cognitive Radio

Base on the recent research and survey, it shows that there is a lot of white

space in the licensed spectrum band, which means there are many unused spectrums

on licensed channels. Meanwhile, the demanding of the spectrum is increasing to

provide higher capacity for more users. Hence, a new technology is being introduced

12

to optimize the spectrum utilization which is known as cognitive radio (CR).

Cognitive radio is firstly proposed by Mitola III in which radio knowledge

representation language (RKRL) is emphasized [11-13]. CR is a radio technology

with the talent of learning and adapting its surrounding environment and its network

parameters are then being adjusted to optimize the utilization of the spectrum with

the flexibility in wireless access [1]. This also signifies the ability of CR to vary its

transmission parameters to optimize its performance based on the information

obtained by learning from its surrounding environment. In general, CR is defined as

a technology that provides dynamic spectrum access (DSA).

In wireless communication, the basic idea of CR is to enables the SU or

unlicensed users or CR users to sense and access to the unoccupied spectrum

intelligently which including licensed or unlicensed spectrum band without causing

interference to the owner of the spectrum. It allows the SU to exploit the spectrum of

PU or licensed users when PU is not using the spectrum or the spectrum are

underutilized, i.e. spectrum hole or white space [5]. CR technology also has the

ability to enable the SU to vacate the channel and switch to another unused channel,

once the respective licensed channel suddenly being occupied by the PU or licensed

user.

One of the main functions of CR technology is dynamic spectrum

management (DSM), which consists of four main components namely spectrum

sensing, spectrum decision, spectrum sharing and spectrum mobility [14] which will

provide seamless communication. The operation of the dynamic spectrum access

which consists of the main components is as shown in Figure 2.3 [14].

Figure 2.3: Dynamic spectrum access [14]

13

2.2.1 Features of Cognitive Radio

One of the main features of the CR is cognitive capability which presents its

ability of autonomously and dynamically controls the operating parameters to

optimize the system operation base on the awareness of the operational environment.

Hence, by improving the traditional radio concept, CR is aware of the ability and

environment whereby it able to vary its physical layer behaviour independently and

also optimize their performance intelligently such as maximize throughput, mitigate

interference and so on in response to the user’s requests. This capability is

momentous to CRN due to the responsibility of CR to continuously sensing to

obtain the information of radio environment and control its parameters adaptively to

optimize the transmission. The adaptive operation is illustrated as shown in Figure

2.4 [3]. In Figure 2.4, it shows that all the operation is performed in a cycle manner

and therefore it is named as cognitive cycle [5]. There are four dynamic spectrum

management functions in the steps of cognitive cycle which are spectrum mobility,

spectrum sharing, spectrum decision and spectrum sensing. Generally as illustrated

in Figure 2.4, spectrum holes will be detected in the process of spectrum sensing.

Then, the information and condition of the spectrum holes will be analyzed in the

process of spectrum analysis. Then, the best spectrum hole will be investigated in

the process of spectrum decision and to be utilized for transmission.

Figure 2.4: Cognitive cycle [3]

14

2.2.2 Types of Cognitive Radio

In CR network, there is a set of parameters to be taken into account in

deciding the changes of signal transmission and reception. Hence, CR can be

classified into several types and the two main types are Spectrum Sensing Cognitive

Radio and Full Cognitive Radio. In Full Cognitive Radio, the wireless network will

take all the possible parameters that have been observed into consideration.

However, only the parameter of frequency spectrum will be taken into consideration

in Spectrum Sensing Cognitive Radio. On the other hand, depending on the

spectrum availability in CRN, it can be classified into two different types namely

Unlicensed Band Cognitive Radio and Licensed Band Cognitive Radio. For

Licensed Band Cognitive Radio, the CR user has the ability of occupying the

spectrum assigned to the licensed user but unable to operate in the unlicensed

frequency band. However, the CR user only able to utilize the unlicensed part of

radio frequency (RF) band for Unlicensed Band Cognitive Radio.

2.2.3 Functions of Cognitive Radio

There are four functions for dynamic spectrum management in CRN which

are spectrum sensing, spectrum decision, spectrum sharing and spectrum mobility.

2.2.3.1 Spectrum Sensing

This sensing process is the first function in CR whereby CR user will aware

of the surrounding radio environment by detecting the current available or unused

spectrum which is known as white space or spectrum hole and also the activation of

PU during transmission to avoid interference. Basically, the spectrum sensing

consists of several necessitate functions in CRN which are interference-based

detection, cooperative detection and transmitter detection [14] as shown in Figure

2.5 [4]. Transmitter detection is taken place by CR user to analyze the radio

environment to detect the activation of PU in transmission and also identify the

spectrum availability. By improving the sensing accuracy for primary transmitter

detection, there are three detection methods to be performed which are energy

detection, cyclostationary feature detection and matched filter detection [15] as

shown in Figure 2.5. Moreover, cooperative detection is proposed in order to

15

encounter the shadowing and multipath fading within multiple CR users [15]. In

contrast with transmitter detection, cooperative detection consists of the

investigation for the presence of PU transmitter within SU communication range.

Lastly, interference-based detection is performed base on the concept of interference

temperature which being introduced by FCC [16]. The idea is that the spectrum will

be considered as white space if the interference temperature of the respective

spectrum band is lesser as compared to the predetermined interference limit.

Figure 2.5: Spectrum sensing classification [4]

2.2.3.2 Spectrum Decision

Spectrum decision is being implemented base on the information of the

parameters that being collected from spectrum sensing, in order to determine the

best available spectrum to be utilized. Basically, spectrum decision will be

performed in two stages. It will first determine the characteristic of the spectrum

holes based on the observation from SU and the statistical data from PU network.

The characterization is depends on the parameters such as link layer delay, wireless

link error, interference and path loss [14]. Next, it will base on the characterization

to choose the best spectrum holes for utilization which also depends on the quality

of service (QoS) requirement. Besides the ability of spectrum characterization and

spectrum selection in the first and second stage in spectrum decision respectively,

CR user also has the ability to reconfigure the communication path according to the

environment.

2.2.3.3 Spectrum Sharing

Spectrum sharing is performed to provide scheduling to coordinate the

sharing of spectrum among PU and SU. Moreover, it also coordinates the sharing of

16

spectrum among SUs when there is more than one unlicensed users are competing

for the same spectrum. In general, spectrum sharing are focus on two types of

network which are intra-network and inter-network spectrum sharing as shown in

Figure 2.6 [4]. In intra-network, spectrum sharing is performed within a CRN.

However, spectrum sharing is performed between multiple coexisting CRNs based

on either centralized or distributed network for the inter-network spectrum sharing.

Basically, spectrum sharing is classified into three different features namely

architecture, spectrum allocation behaviour and spectrum access technique as shown

in Figure 2.7 [4]. Spectrum sensing performs differently in the two different

architecture of centralized and distributed. There is a common control centre or base

station (BS) needed to perform spectrum allocation and scheduling for SUs in a

centralized CRN. However, the spectrum scheduling and allocation among SUs will

be performed locally or globally in a distributed CRN. For the spectrum allocation

behaviour, in order to fulfil the QoS requirements and the spectrum fairness, CR

user will perform a proper spectrum allocation [17-19] and power control [20-22].

There are two type of spectrum allocation behaviour namely cooperative and non-

cooperative. Different from non-cooperative spectrum sharing, the cooperative

spectrum sharing will share the interference information between SUs. Spectrum

access technique is performed to investigate who and when a CR user has the ability

to occupy the channel in order to enable multiple CR users to share the same

spectrum resources [23-25]. There are two types of spectrum access technique

namely overlay and underlay. Overlay and underlay perform spectrum access by

accessing white space and using spread spectrum techniques, respectively.

Figure 2.6: Inter- and Intra-Network spectrum sharing in CRN [4]

17

Figure 2.7: Spectrum sharing classification [4]

2.2.3.4 Spectrum Mobility

Spectrum mobility is to enable SUs to vacate the licensed spectrum which

they are occupying temporary and switch to another available spectrum when PUs

suddenly return and utilize the respective spectrum. There are two main functions

for spectrum mobility in a CR ad hoc network which is connection management and

spectrum handoff. Spectrum handoff is a process where the CR user changes the

operating frequency and reconfigures the communication parameters in order to

perform the connections transformation to an unoccupied spectrum band when the

current spectrum band is being occupied by PU. Spectrum handoff will only be

performed when the CR user detected the spectrum is in used by the PU, when an

on-going communication among CR users is disconnected or the current spectrum

band does not meet the QoS requirement. Moreover, CR user implements

connection management with each layering protocols in order to fulfil the QoS

requirement and to optimize the link quality during the spectrum switching. The

objective of spectrum mobility management is to perform a smooth and high

transition speed to optimize the performance during a spectrum handoff. In addition,

spectrum mobility is interrelated with the routing protocol whereby it will consider

the link failure recovery and also end-to-end routing which is similar to the spectrum

decision.

18

2.3 Cognitive Radio Mesh Network (COMNET)

In cognitive radio mesh network, it allows the licensed but unutilized

spectrum to become available temporary without interference between primary and

secondary users. CR users can dynamically sense spectrum opportunity (SOP),

which is a set of frequency band that are available for utilization due to that it is

currently unoccupied. Moreover, CR users will sense and look for the common SOP

with the next hop node or neighbouring node in order to communicate to each other

in COMNET.

2.4 Routing

Routing is a process to find and to select a proper path for data transmission

from source node to the destination node. There are two types of routing namely

proactive routing and reactive routing. Proactive routing will determine the routes to

some nodes in a network that has been developed so that the route will always be

ready when it is needed [26]. The disadvantage of proactive routing is that it consists

of large overhead to the network due to all existing routes will be discovered by

each node in the network and hence, higher bandwidth is needed to keep the route

up to date [26]. However, its advantage is the short time duration of data

transmission due to that the route is already discovered and existed. Meanwhile,

reactive routing will determine the route only if it is required. Hence, the overhead

of the route discovery is much smaller as compared to proactive routing. However,

every data transmission from a source to a destination must wait for the discovery of

a route [26]. In addition, the routing scheme can be differed base on their delivery

semantics such as unicast, multicast, broadcast, anycast and geocast. Unicast

delivers a message to a single specific node, whereas multicast is to deliver the

message to a group of neighbouring nodes or the nodes that have expressed interest

in receiving the message. Broadcast is the process whereby the message will be

delivered to every node in the network. Lately with the geocast and anycast

semantics, they are used to deliver a message to anyone out of a group which is

typically referring to a geographic area and the one nearest to the source,

respectively.

19

2.4.1 AODV Routing Protocol

Ad-Hoc On-Demand Distance Vector (AODV) routing protocol is generally

designed for ad-hoc network in which it able to perform unicast and multicast

routing [26]. There are few features for AODV routing protocol and one of them is

pure on-demand acquisition whereby the source node will only do route discovery

when necessary. Secondly, the notion of “Active” paths is base on the

neighbourhood detection and the network does not have the knowledge of

centralized topology [27]. Lastly, traditional AODV routing protocol uses “Hello”

message to identify neighbour nodes [27]. As mentioned on the above that AODV

protocol will make the route in the network only if it is required by the source node

to transmit data or send a message, therefore AODV is a reactive routing protocol

that use on-demand-based algorithm. Source node will only broadcasts route request

(RREQ) packet to its neighbour when a route to destination is needed to be found.

The neighbour node is then broadcast the packet to the next hop node until it reaches

to the destination node. When a node sends a RREQ to neighbouring nodes, the

information from the packet which is first arrived will be stored in its routing table

[26]. The information is used to create a route back from the RREQ packet where

the route reply (RREP) packet is transmitted back to the source from the destination

before the data transmission.

2.5 Summary of Research Papers

In this section of the thesis, there are five research papers which I would like

to focus on and summarize the respective proposed routing algorithm in CRN. In the

first paper [28], there are two main problem statements have been highlighted.

Firstly, the network topology will be changed due to that the SOPs of nodes may

alter with time that will affect the ability on finding next hop nodes and also affect

the routing performance. Secondly, a common sprectrum band being shared between

neighbours for connection will incur extra backoff delay. However, the unbalanced

between band switching for less interference and more spectrum utilization will

inccur extra switching delay. Base on the problem statements, a new routing

protocol which is Spectrum Aware On-demand Routing Protocol (SORP) has been

Comment [S4]: Avoid personal reference

20

proposed. The proposed routing protocol will consider the path delay (DP) and node

delay (DN) by using delay (D) as the metric. The DN is depends on the intersecting

flows and the spectrum band whereas the DP consider the backoff and switching

delay that is caused by the intersecting flow as well as its own path [28], [29].

Moreover, during route discovery, information of SOP will be piggyback by RREQ

packet and it will be forwarded only when the node find an intersection between its

own SOP and the RREQ’s SOP. During RREP, intermediate node will assign

spectrum band with the aid of band choices that is extracted from the information of

SOP at the previous RREQ and the current RREP. However, there is some

limitations in this proposed routing protocol where the node unable to perform

routing path reconfiguration once the PU suddenly become active due to the reason

that the node is assumed does not consider the dynamic spectrum awareness as it has

the global knowledge of the network. In the second paper [29], there are two main

problem statements have been highlighted. Firstly, the spectrum information should

be considered in routing due to the CRN topology is changing according to the

spectrum switching progess in CR node. Secondly, routing performance is degraded

due to too many channel switching and hence, backoff overhead, switching delay

and queuing delay needed to be balanced. Base on the problem statements, a new

routing protocol which is Delay motivated On-demand Routing Protocol (DORP)

has been proposed. The proposed routing protocol combine spectrum assignment

and routing together to optimize the spectrum decision making and effective route

selection. Moreover, Node Analytical Model (NAM) is proposed which has the

ability to assign spectrum bands and to determine the sequence of channel switching

for the nodes. However, there is some limitations in this proposed routing protocol

where the node cannot perform routing path reconfiguration once the PU suddenly

become active due to the reason that the node is assumed does not consider the

dynamic spectrum awareness as it has the global knowledge of the network.

In the third paper [30], there are two main problem statements have been

highlighted. Firstly, the dynamic variation of channel set may cause routing failure

in multi-hop cognitive radio networks (MCRN). Secondly, the “deafness problem”

will cause performance degradation which is occured due to the two consecutive

switching nodes that without switching mechanism [30]. Based on the problem

21

statements, a new routing protocol which is Multi-hop Single-transceiver CRN

Routing Protocol (MSCRP) is proposed. The proposed routing protocol form an

interation between network nodes for exchanging the information of the available

channels. Moreover, the switching nodes’ working channels will be informed to

their neighbours by using the LEAVE/JOIN messages [30]. However, there is some

limitations in this proposed routing protocol where it will increase some extra

overhead to the network and it also do not have the ability to avoid PHN and PEN

problems.

In the fourth paper [31], there are two main problem statements have been

highlighted. Firstly, channelization which serves as a basis for recently proposed

routing metrics over WMN is no longer valid in COMNET. Secondly, the short term

opportunistic performance and long-term route stability have to be balanced in order

to handle as well as to optimize the dynamic variation in added dimension of

spectrum in routing. Base on the problem statements, a new proposed routing

protocol which is Spectrum Aware Mesh Routing (SAMER) seeks to utilize avilable

spectrum blocks by routing data traffic over path with higher spectrum availability.

Moreover, the short-term route performance and long-term route stability can be

balanced via forming a runtime forwarding route mesh. However, there is some

limitations in this proposed routing protocol where the node unable to perform

routing path reconfiguration once the PU suddenly become active and it also unable

to encounter the PHN and PEN problems.

In the fifth paper [32], it is also the benchmarked conference paper for my

project. There are two main problem statements have been highlighted in this paper.

Firstly, the dynamic behaviour of the PUs will change the topology of cognitive

radio mesh network (CRMN) which will cause packet lost due to the routing failure.

Secondly, route failure and frequency rerouting that introduce excessive end to end

delay are caused by the suddenly appearance of a PU that may occupy the spectrum

and a channel is rendered to be unusable. Base on the problem statements, a new

routing protocol which is spectrum aware distributed routing scheme for multi-hop

CRMN is proposed. PEN and PHN problems can be avoided in this routing scheme.

Moreover, the routing scheme consists of route maintenance approach which has the

ability to reduce the route failure probability by introducing Channel Priority List

22

(CPL) [32]. Furthermore, it will perform route selection intelligenly whereby it will

utilize an utility function to select the route with the minimum end-to-end delay.

However, there is some limitation in this proposed routing scheme where it can only

be applied in certain scenario and it do not have the ability to utilize the available

channel in current routing or sending data packet when the active PU suddenly

become inactive and vacant the channel to be free.

All the five research papers are further simplified as shown in Table 2.1.

Table 2.1: Summary of research papers

No Title Approach Limitation

1 Spectrum Aware On-

demand Routing in

Cognitive Radio Network

Piggyback SOP by RREQ using

common control channel and it

consider the switching delay and

backoff delay.

Does not consider the

dynamic spectrum awareness

and unable to perform

routing path reconfiguration

when the PU suddenly

become active.

2 Join On-demand Routing

and Spectrum Assignment

in Cognitive Radio

Network

Piggyback SOP by RREQ using

common control channel and it

consider the switching, backoff

as well as queuing delay.

Does not consider the

dynamic spectrum awareness

and unable to perform

routing path reconfiguration

when the PU suddenly

become active.

3 Spectrum Aware Routing

for Multi-Hop Cognitive

Radio Networks with a

Single Transceiver

Information of available

channels is piggyback by RREQ

and being broadcasted on all

available channels without

common control channel based

routing.

Increase extra overhead to

the network.

4 SAMER: Spectrum

Aware Mesh Routing in

Cognitive Radio

Networks

It considers the spectrum

availability and quality in

routing data traffic.

Unable to perform routing

path reconfiguration when

the PU suddenly become

active.

5 A Novel Spectrum Aware

Routing Scheme for

Multi-hop Cognitive

Radio Mesh Networks

It considers the SOP and the

active frequency bands will be

placed in CPL based on the

channel usage ratio of the PU.

The routing protocol can

only be applied in certain

scenario.

Comment [S5]: Summarize literature review in

table form

23

CHAPTER 3: PROPOSED SPECTRUM

AWARE ROUTING SCHEME

In this chapter, an overview of the novel proposed routing scheme will be

further elaborated on how it performs and being applied or implemented in order to

optimize the performance of SUs without interference with PUs in a CR mesh

network. Moreover, how the PHN and PEN problems are being avoided in a CR

mesh network by the proposed routing scheme will be explained in general.

Furthermore, it will also briefly explain on how the proposed routing scheme can

perform dynamically channel switching in the CR environment.

3.1 Overview

To explain the proposed routing scheme in general, a point-to-point CR

wireless network as shown in Figure 3.1 is used as an example for elaboration. In

this scenario, there are four PUs and two CR users which are denoted as CR A and

CR B. In the whole network, the two CR users are communicate to each other and

both of the users are considered to utilize channel 1, 2, 3 and 4 which are denoted as

CH 1, CH 2, CH 3 and CH 4 respectively for communication, provided that the PUs

are not utilizing those channels. In Figure 3.1, it shows that PU 1 and PU 2 are

utilizing their respective CH 1 and CH 2. However, PU 3 and PU 4 are inactive

which introduce white space in spectrum band of CH3 and CH 4.

By looking into each of the CR users, they will have their own information

regarding the spectrum availability (SA) after detection or sensing. The SA is

defined as a set of channels that can be used for communication without interference

with PUs or without influence the performance in PUs. In order for the two CR users

to communicate, they must have the same channel. Hence, in the proposed routing

protocol, a beta channel list (BCL) is introduced which will store the common SA

for both intended communicating users. The formation of the BCL will be further

elaborated in section 3.2.1.

Comment [S6]: The proposed new method

24

Figure 3.1: Point-to-point CR wireless network

Base on the scenario as shown in Figure 3.1, CH 1 is not an available

spectrum for CR A as it is under the coverage of PU 1 who is utilizing CH 1.

However, CH 1 is one of the SA for CR B as it does not under the coverage of any

PU who is utilizing that channel. Therefore, CR B can utilize CH 1 although PU 1 is

using in the network and this phenomenon is known as frequency reuse.

Nevertheless, a situation would be occurred whereby CR B communicates by

sending data to CR A using CH 1 and there is where the PHN problem is occurred

as shown in Figure 3.2. The PHN problem causes several data loss at the receiver,

CR A due to the interference by the licensed user, PU 1 who occupying the channel

that CR receiver intended to use for communication. On the other hand, there is a

situation where the CR user does not aware to the activity of the PU whereby the CR

transmitter, CR A intended to transmit data to the CR receiver, CR B by utilizing

CH 1 which is using by PU 1 as shown in Figure 3.3. This phenomenon will cause

PEN problem which will incur the packet loss and increase the transmission delay at

the CR transmitter, CR A due to the activation of the PU 1 who occupying CH 1 that

the CR A intended to use.

Comment [S7]: Good explanation of Figure

25

Figure 3.2: Primary hidden node (PHN)

Figure 3.3: Primary exposed node (PEN)

In the proposed routing scheme, it has the ability to avoid PEN and PHN

problems by selecting a proper channel to be occupied during routing. Gamma

channel list (GCL) is introduced whereby it will store the common spectrum identity

(SI) for both intended communicating users and also store the channel left in the

network that is not contained in both respective SI of the two neighbouring users. SI

is defined as a set of channels that owned by the PUs provided that the CR user is

within the coverage of those PUs. The formation of GCL will be further elaborated

in section 3.2.2.

In a CR environment, the awareness of the CR user towards the activities of

PU should be taken into consideration. Giving an example in Figure 3.1, both CR A

and CR B may choose to utilize CH 2 for communication in order to avoid PHN and

PEN problems base on the GSL. However, there is a situation where the CH 2 is

used by PU 2 initially and hence, there will be an interference with PU 2 if both CR

26

users utilize the channel for communication. Therefore, another new channel list is

introduced in the proposed routing scheme namely delta channel list (DCL), which

will provide an optimum solution to avoid the uncertainty in the scenario as stated

earlier. The formation of the DCL will be further elaborated in section 3.2.3. In

addition, the proposed routing scheme has the ability to be aware of the activity of

PU and dynamically switch to another predetermined channel, once the CR users

notify the current listening channel is occupied by the PU in order to avoid

interference.

Generally base on the proposed routing scheme, when CR A wants to

transmit data to CR B, it will first perform a route discovery by broadcasting RREQ

packet towards the destination of CR B. The information of the SA and SI will be

stored in RREQ packets so that the three channel lists such as BCL, GCL and DCL

between two users can be generated. Once the RREQ packet reached at CR B, it will

send RREP towards CR A. Next, the data transmission will begin whereby CR A

will communicate with CR B by occupying the channel listed in GCL to optimize

the performance. Moreover, CR A able to switch to another available channel based

on the predetermined channel lists if the channel intended to be listened is suddenly

listened by the PU. An overall system flow base on the proposed routing scheme is

illustrated in general in a flow chart as shown in Figure 3.4.

Start

Send RREQ to neighbouring nodes

Formation of BCL

Formation of GCL

Formation of DCL

Destination

node?

Send RREP to

source node

Data transmission

End

YES NO

(I)

(II)

(III)

Figure 3.4: Flow chart of overall system based on proposed routing scheme

27

3.2 Channel List Formation

In this section of the thesis, the three proposed channel list such as BCL,

GCL and DCL will be further elaborated on how they are being formed.

3.2.1 Beta Channel List

Beta channel list (BCL) is generated base on the SA of both intended

communicating users, whereby it will store the common SA for both intended

communicating users. Base on the scenario as shown in Figure 3.1 as an example,

CH 3 and CH 4 are the SA for CR A, whereas CH 1, CH 3 and CH 4 are the SA for

CR B as shown in Table 3.1. Both CR users having the common channel of CH 3

and CH 4, hence the two channels will be the element of BCL which can be used for

communication as shown in Table 3.3. The formation of BCL is illustrated in

general in a flow chart as shown in Figure 3.5.

Table 3.1: Spectrum availability of each CR node

Start

Indicate the common SA

between two nodes

Store the respective

channels into BCL

End

(I)

Figure 3.5: Flow chart of BCL formation

28

3.2.2 Gamma Channel List

Gamma channel list (GCL) is generated base on the SI of both intended

communicating users, whereby it will store the common SI for both intended

communicating users and also store the channel left in the network that is not

contained in both respective SI of the two users. Base on the scenario as shown in

Figure 3.1 as an example, the SI for CR A are CH 1, CH 2 and CH 3, whereas CH 2,

CH 3 and CH 4 are the SI for CR B as shown in Table 3.2 and there do not have any

channel left in the network which is not contained in both respective SI of the two

users. Hence, CH 2 and CH 3 will be the element of GCL that can be used for

communication between two CR users to avoid PEN and PHN problem as shown in

Table 3.3. The formation of GCL is illustrated in general in a flow chart as shown in

Figure 3.6.

Table 3.2: Spectrum identity of each CR node

Start

Indicate the common SI

between two nodes


channels into GCL

End

(II)

Indicate the channel left in the

network that is not contained in

both respective SI of two users

Figure 3.6: Flow chart of GCL formation

29

3.2.3 Delta Channel List

Delta channel list is generated base on the two channel lists which is BCL

and GCL. DCL examines the common channels that are presented in BCL and DCL

from both intended communicating nodes. Base on the scenario as shown in Figure

3.1 as an example, it shows that CH 3 and CH 4 are presented in BCL and CH 2 and

CH 3 are presented in GCL. Therefore, by extracting the common channels that

existing in BCL and GCL, CH 3 will then be the element of the DCL as shown in

Table 3.3. That channel will be used for communication between the two CR users

and it is the best channel selection in routing to avoid the uncertainty in the scenario

as stated earlier for performance optimization. The formation of DCL is illustrated

in general in a flow chart as shown in Figure 3.7.

Table 3.3: Three channel lists between the two CR nodes

Start

Extract channels listed in BCL

Extract channels listed in GCL

(III)

End

Examine the common channels

presented in BCL and GCL


channels into DCL

Figure 3.7: Flow chart of DCL formation

30

3.3 Wireless Mesh Network Based Scenario

After having an overview on the proposed routing scheme being applied in a

point-to-point network in section 3.1, this section will elaborate on the proposed

routing scheme to be implemented in a wireless mesh network with CR capability.

The scenario for the wireless mesh network is shown in Figure 3.8. In this scenario,

there is a source and destination node where the source node intended to transmit

data towards the destination node. In the whole network, it consists of sixteen

unlicensed users or CR users and two licensed users or PUs. The CR users are

denoted as node a, b, c and d where node a is the node that without coverage of any

PU, node b is within coverage of PU 1, node c is within coverage of PU 1 and PU 2,

and lastly node d is within the coverage of PU 2. There are two licensed channels in

the network and each channel is owned by one PU such as CH 1 for PU 1 and CH 2

for PU 2. Initially, PU 1 is utilizing CH 1 but PU 2 is inactive and remains CH 2

unutilized. By applying the proposed routing scheme, when the source node

intended to transmit data to the destination node, it will first perform route discovery

by broadcasting the RREQ packets to its neighbouring nodes until it reach the

destination node. Throughout the route discovery process, the three channel lists

between two neighbouring nodes will be generated as shown in Table 3.6 based on

their respective SA and SI as shown in Table 3.4 and Table 3.5, respectively. Once

the RREQ packet reached at the destination node, it will then choose a best path and

send the RREP packet back to the source node. When the RREP packet reached at

the source node, data transmission from source towards the destination node will

begin base on the dedicated path by utilizing the respective dedicated channel.

31

Figure 3.8: Scenario of wireless mesh network

Table 3.4: Spectrum availability of each node

32

Table 3.5: Spectrum identity of each node

Table 3.6: Three channel lists between two nodes

33

3.4 System Models with Parameters

In this section, several system models that have been constructed will be

illustrated in figures and each model will be classified into different types of

scenario. The respective fixed and varied parameters for each scenario are stated in

tables. The licensed simulation software that is being used to construct the system

models and perform simulation is Qualnet5.2.

3.4.1 System Models

There are total of four main models that have been constructed for

simulation. Fist model is a network that consists of a point-to-point SU sub-network

and a point-to-point PU sub-network as shown in Figure 3.9. In Figure 3.10, the

second model shows a network with five SUs and a single point-to-point PU sub-

networks. The third model is a network that consists of an equivalent number of PU

and SU which is 30 users for both as shown in Figure 3.11. Figure 3.12 represents

the fourth model whereby a network that consists of 50 SUs and 10 PUs. Base on

the model as shown in Figure 3.12, it will as well be classified into different cases

by varying the number of SUs and fix the number of PUs or varying the number of

PUs and fix the number of SUs. By fixing the number of SU to 50 users, the number

of PUs will be varied from the minimum of 2 users as shown in Figure 3.13 and an

increment of 2 PUs will be made for different cases until a maximum of 44 PUs in

the network as shown in Figure 3.14. Moreover, by fixing the number of PU to 50

users, the number of SU will be varied from the minimum of 2 users as shown in

Figure 3.13 and an increment of 2 SUs will be made for different cases until a

maximum of 44 SUs in the network as shown in Figure 3.14. For all the models, the

PU and SU sub-networks are using a constant bit rate (CBR) traffic.

34

Figure 3.9: Single point-to-point SU and PU network

Figure 3.10: Network with 5 SUs and a single point-to-point PU sub-network

35

Figure 3.11: Network with 30 PUs and 30 SUs

Figure 3.12: Network with 10 PUs or SUs and 50 SUs or PUs respectively

36



37

3.4.2 Parameters

In this section, several sets of parameter will be set as shown in the tables for

different types of scenario. This section will also be divided into two sub-sections

whereby the first sub-section will show the sets of parameter that create different

types of scenario to evaluate the performance of the traditional AODV routing

protocol and also for the preliminary performance evaluation. However, the second

sub-section will show the sets of parameter that used to construct different types of

scenario to evaluate the performance of the proposed routing scheme.

3.4.2.1 Parameters for Traditional AODV Implementation

To evaluate the performance of traditional AODV routing protocol and

generate the preliminary results, a system model as shown in Figure 3.10 is used for

all scenarios.

3.4.2.1.1 First Scenario

First scenario is constructed whereby both SU and PU sub-networks are

listening to a same channel which is frequency of 2.4GHz to indicate the SU has

interference with the PU. The fixed parameters for both PU and SU are shown in

Table 3.7.

Table 3.7: Fixed parameters for PU and SU

Radio type 802.11a/g

Channel frequency for PU 2.4 GHz

Channel frequency for SU 2.4 GHz

Packet size 512 bytes

No. of packets 100

Simulation time 30s

Start time 5s

End time 25s

Time interval 1s

Transmitter power 20dBm

CR receive sensitivity -85dBm

3.4.2.1.2 Second Scenario

The second scenario is constructed whereby both SU and PU are listening to

different channel to indicate SU does not interfere with PU. In this scenario, SU is

38

listening to channel 1 and PU is listening to channel 2 which is referring to

frequency of 2.4GHz and 2.5GHz, respectively and the parameters are shown in

Table 3.8.



Channel frequency for PU 2.5 GHz

Channel frequency for SU 2.4 GHz


No. of packets 100

Simulation time 30s

Start time 5s

End time 25s

Time interval 1s



3.4.2.1.3 Third Scenario

Third scenario is constructed whereby the network only consists of SU and

without any PU. This scenario is to indicate the PU who is always inactive in a

network all the time. Similar to the first scenario, the fixed parameters for the SU are

shown in Table 3.7 without the parameters of PU.

3.4.2.1.4 Fourth Scenario

The fourth scenario is constructed whereby PU and SU are listening to the

same channel of frequency 2.4GHz to indicate there is some interference between

SU and PU sub-networks. Meanwhile, the usage ratio of the PU will be varied from

100% to 0.05% by varying the time interval of the PU sub-network. Hence, the fixed

and varied parameters for this scenario are shown in Table 3.7 and Table 3.9,

respectively.

Table 3.9: Varied parameter for PU

Time interval Vary from 1s to 20s

From the table of fixed parameters, the start time ( ��) and end time ( ��)

of 5 seconds and 25 seconds, respectively represent the maximum time duration for

data transmission (��) where the calculation is given in equation (3.1) as follow:

�� = �� (3.1)

39

In the Qualnet simulation, one data packet will be start transmitted in every one

second. Therefore, the maximum time duration of data transmission, which is 20

seconds in the scenario indicates the total packets of 20 will be transmitted

throughout the simulation. Time interval ( ��) indicate within how long a data

will be transmitted once throughout the simulation. Hence, in order to calculate the

total time duration that the PU is active on the respective channel (��)

throughout the whole simulation base on the variation of time interval, the

calculation is given in equation (3.2) as follow:

��

�� (3.2)

The probability that the PU active on the respective channel with respect to the

maximum time duration of data transmission can be calculated base on equation

(3.3) as follow:

��

��

(3.3)

Therefore, the usage ratio (α) of the PU on the respective channel in percentage can

be calculated base on equation (3.4) as follow:

� � �� ! 100% (3.4)

In the scenario where the time interval will be varied, the time interval for the PU

sub-network will be set to 1 second, 2 seconds and follow with an increment of 2

seconds for different cases until it reach to the maximum of 20 seconds as shown in

Table 3.9.

3.4.2.2 Parameters for Proposed Routing Scheme Implementation

To evaluate the performance of the proposed routing scheme, the four main

system models as shown in Figure 3.9, 3.10, 3.11 and 3.12 are used.

3.4.2.2.1 First Scenario

First scenario will be constructed in all the models whereby the usage ratio

of the PU will be manipulated by varying the total number of data packets being

transmitted by PU throughout the whole simulation time. Hence, the fixed and

40

varied parameter for this scenario is shown in Table 3.10 and Table 3.11,

respectively.



Listenable channel frequency for PU 2.4 GHz

Listening channel frequency for PU 2.4 GHz

Listenable channel frequency for SU 2.4 GHz and 2.5 GHz

Listening channel frequency for SU 2.4GHz


No. of packets 100

Simulation time 30s

Start time 5s

End time 25s

Time interval 1s



Table 3.11: Varied parameter for PU

No. of packets Vary from 0 to 20 packets

Base on the fixed parameters, the maximum data packets that PU can

transmit is depends on the start time and end time. In Qualnet simulation, one data

packet will be start transmitted in every one second and therefore the maximum data

packets (��) that can be transmitted throughout the simulation will be equivalent

to the value of the maximum time duration for data transmission as calculated base

on equation (3.1). For the varied parameter, the total numbers of data packets

(��) to be transmitted will be varied from 0 packet and an increment of 1 packet

will be made for different cases until the maximum of 20 packets. Hence, the usage

ratio of PU (α) can be calculated base on equation (3.5) as follow:

� �%�&��

%��

! 100% (3.5)

3.4.2.2.2 Second Scenario

Second scenario will also be constructed in all the models whereby the usage

ratio of the PU will be manipulated by varying the time interval of the data

transmission in PU. The respective calculation will be done base on equation (3.1),

41

(3.2), (3.3) and (3.4) as stated in section 3.4.2.1.4 earlier. The fixed parameters for

PU and SU are set as shown in Table 3.10. However, the time interval for the PU

will be varied and set to 1 second, 2 seconds and follow with an increment of 2

seconds for different cases until it reach the maximum of 20 seconds as shown in

Table 3.9.

3.4.2.2.3 Third Scenario

The third scenario will be constructed in a system model as shown in Figure

3.12. The fixed parameters for the PU and SU are as shown in Table 3.10. By fixing

the number of SU to 50 users, the number of PU will be varied from 2 users as

shown in Figure 3.13 and followed by an increment of 2 users for different cases

until the maximum of 44 users as shown in Figure 3.14.

3.4.2.2.4 Fourth Scenario

In the fourth scenario, the system model as shown in Figure 3.12 is used and

the fixed parameters for the PU and SU are as shown in Table 3.10. However, unlike

the third scenario, fourth scenario will fix the number of PU to 50 users and vary the

number of SU from 2 users as shown in Figure 3.13 and follow with an increment of

2 users for different cases until to the maximum of 44 users as shown in Figure 3.14.

3.4.2.2.5 Fifth Scenario

Lastly, system model as shown in Figure 3.12 will be constructed for the

fifth scenario. In this scenario, the number of PU in the network will be varied from

2 users as shown in Figure 3.13 and followed by an increment of 2 users for

different cases until the maximum of 44 users as shown in Figure 3.14. Meanwhile,

in each cases of different number of PU in the network, the usage ratio of the PU

will be varied by altering the total number of data packets being transmitted by the

PU. Total numbers of data packets to be transmitted will be varied from 0 packet

and followed by an increment of 1 packet for different cases until the maximum of

20 packets as shown in Table 3.11 and the fixed parameters will be set as shown in

Table 3.10.

42

CHAPTER 4: SIMULATIONS AND

ANALYSIS

This chapter will present the Qualnet simulation results to analyze the

performance of the traditional AODV routing protocol and the proposed routing

scheme by amending on the traditional AODV routing protocol when it is

implemented in a CRN system. In this chapter, all the simulated results will be

collected and plotted in the graph for further illustration and performance evaluation.

Basically, this chapter will be divided into two sections where the first section will

present the preliminary results whereby the traditional AODV routing protocol is

implemented in a CRN system. The second section will present the results whereby

the proposed routing scheme is implemented in a CRN system. Moreover, in this

section, the performance of the traditional AODV routing protocol will also be

implemented and the results will be evaluated and used to compare with the

performance of the proposed routing scheme.

4.1 Preliminary Results

In this section, the simulation results are generated base on the traditional

AODV routing protocol that being implemented in CRN system. A bar graph of

average end-to-end delay of SU with three different types of scenario is illustrated in

Figure 4.1. From the bar graph, the legend that noted as “same channel with PU” is

referred to the first scenario as stated in section 3.4.2.1.1; “different channel of PU”

is referred to the second scenario as stated in section 3.4.2.1.2 and legend of

“without PU network” is referred to the third scenario as stated in section 3.4.2.1.3.

43

Figure 4.1: Average end-to-end delay of SU with different scenario

4.1.1 First Scenario

Base on the results, it shows that when the SU is utilizing the same channel

frequency with PU, the SU will cause an interference with the PU which will

increase the average end-to-end delay of the data packet transmission in SU sub-

network. From the graph as shown in Figure 4.1, it shows the average end-to-end

delay of SU as high as 2.079ms.

4.1.2 Second Scenario

In contrast to the first scenario, this scenario indicates that the SU without

having interference with PU by utilizing different channel frequency with the PU.

Hence, the average end-to-end delay of the data packet transmission in SU sub-

network can be minimized. It can be proven based on the graph as shown in Figure

4.1, when the SU without interference with the PU, the average end-to-end delay of

SU is 1.735ms which is 0.344ms lower as compared to the first scenario where the

SU interfere to the PU.

1.735

2.079

1.735

1.500

1.600

1.700

1.800

1.900

2.000

2.100

2.200

Av

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nd

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(m

s)Average end-to-end delay of SU with

different scenario

without PU network

same channel with PU

different channel with PU

44

4.1.3 Third Scenario

For the legend of “without PU network” as shown in Figure 4.1, it is another

scenario which to show the PU is inactive and leave the channel unoccupied. Based

on the result, the average end-to-end delay of SU in this scenario is 1.735ms. Hence,

the result has indicated that the scenario which the SU utilizing different channel

that without interfere with PU will perform similarly to the scenario that the PU is

inactive.

4.1.4 Fourth Scenario

The simulation results on the average end-to-end delay of SU by varying the

time interval of PU have been plotted in a line graph as shown in Figure 4.2. These

results are generated base on the fourth scenario stated in section 3.4.2.1.4. The

scenario shows both PU and SU are utilizing same channel frequency with

interference to each other. However, it can be proven that the performance of the SU

will be improved in term of average end-to-end delay when the channel utilization

of the PU is reducing by increasing the time interval. By analyzing on the line graph,

it shows the average end-to-end delay of SU which is around 2.08ms will decrease

until around 1.73ms when the time interval of PU increases from 1s to 20s which

represent 100% to 0.05% of channel utilization in PU, respectively.

Figure 4.2: Average end-to-end delay of SU by varying time interval of PU

2.079383

1.73482

1.4

1.6

1.8

2

2.2

1 2 4 6 8 10 12 14 16 18 20

Av

era

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d-t

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nd

de

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(m

s)

Time interval (s)

Average end-to-end delay of SU with

different time interval of PU

delay

45

4.2 Results for Proposed Routing Scheme Evaluation

In this section, the simulation results are generated base on the proposed

routing scheme that being implemented in CRN system. However, the simulation

results base on the traditional AODV routing protocol will also be presented in order

to compare the performance with the proposed routing scheme.

4.2.1 First Scenario

To evaluate the first scenario as stated in section 3.4.2.2.1, a line graph

representing the average end-to-end delay of SU by varying the usage ratio of PU

for all four main models as shown in Figure 3.9, 3.10, 3.11 and 3.12 are illustrated in

Figure 4.3, 4.5, 4.7 and 4.9 respectively. For all the models, the network has been set

whereby both PU and SU are listening to the same channel frequency of 2.4GHz to

indicate the interference between both sub-networks. However, the listenable

channels for SU are 2.4 and 2.5GHz whereby the channel frequency of 2.5GHz is

unutilized by the PU which can be used by SU. Generally, if the usage ratio of PU is

increased by increasing the total number of data packets being transmitted, the

average end-to-end delay of SU will be increased when both SU and PU are using

the same channel. It has been proven in the traditional AODV routing protocol as

shown in the graph. This is because the traditional AODV routing protocol does not

have the ability to aware of the PU activity. However, by implementing the

proposed routing scheme, it shows that the average end-to-end delay of the proposed

routing scheme will maintain the lower value as compared to the traditional AODV

even though the channel utilization of PU is increasing. This is due to that the

spectrum aware proposed routing scheme will consider the effect of PU activity and

perform dynamically channel switching to avoid interference with the PU. For the

example based on this scenario, the SU will switch from the listening channel

frequency of 2.4GHz to another channel frequency of 2.5GHz in order to avoid

interference with PU who is also listening to channel frequency of 2.4GHz. In

addition, a simulation result on the throughput of SU versus usage ratio of PU for

the system models shown in Figure 3.9, 3.10, 3.11 and 3.12 are illustrated in Figure

4.4, 4.6, 4.8 and 4.10 respectively. Base on the results shown for the first two system

46

models, the throughput of the SU for traditional AODV protocol remained constant

throughout the variation of channel utilization of PU which is 4314 bits/s. Moreover,

the results also show the proposed routing scheme will maintain the performance of

SU in term of throughput which is similar to the conventional AODV routing

protocol. On the other hand, due to the complexity of the last two system models

which consists of quite a number of PU, the throughput of SU are slightly affected

when implementing the conventional AODV. It is shown in the bar graph that the

throughput of SU have dropped one bits/s when the channel frequency is fully

utilized by the PU. However, by implementing the proposed routing scheme, the

results have shown that it will not only maintain the throughput of the SU, but it also

improve the performance of SU in term of throughput as compared to the traditional

AODV routing protocol, especially when the channel utilization of the PU is

hundred percent.

Figure 4.3: Average end-to-end delay of SU by varying the usage ratio of PU in the model

shown in Figure 3.9

1.605684

2.026242

1.605684

1.5

1.6

1.7

1.8

1.9

2

2.1

0 10 20 30 40 50 60 70 80 90 100

Av

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(m

s)

PU usage ratio (%)

Average end-to-end delay of SU versus

usage ratio of PU

AODV

Proposed

Comment [S8]: Compare with existing results

47

Figure 4.4: Throughput of SU versus usage ratio of PU in the model shown in Figure 3.9


shown in Figure 3.10

4314 4314

4314 4314

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100

Th

rou

gh

pu

t (b

its/

s)

PU usage ratio (%)

Throughput of SU versus usage ratio of PU

Proposed

AODV

1.73482

2.079383

1.73482

1.7

1.75

1.8

1.85

1.9

1.95

2

2.05

2.1

0 5

10

15

20

25

30

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45

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(m

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PU usage ratio (%)


usage ratio of PU

AODV

Proposed

48




4314 4314

4314 4314

0

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2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100

Th

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pu

t (b

its/

s)

PU usage ratio (%)


Proposed

AODV

8.022576

10.195236

8.022576

7.5

8

8.5

9

9.5

10

10.5

0 5

10

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usage ratio of PU

AODV

Proposed

49




4333 4332

4333 4333

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7000

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9000

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0 100

Th

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pu

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s)

PU usage ratio (%)


Proposed

AODV

22.959503

25.74253

22.959503

22

22.5

23

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25

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26

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(m

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PU usage ratio (%)


usage ratio of PU

AODV

Proposed

50


4.2.2 Second Scenario

To evaluate the second scenario as stated in section 3.4.2.2.2, a line graph

representing the average end-to-end delay of SU by varying the time interval of PU

for all the four main models as shown in Figure 3.9, 3.10, 3.11 and 3.12 are

illustrated in Figure 4.11, 4.12, 4.13 and 4.14 respectively. Similar to the first

scenario, PU and SU in this scenario are listening to the same channel to indicate

interference between them. Generally, when the channel utilization of PU is

decreased by increasing the time interval of PU, the average end-to-end delay of SU

will be decreased when both SU and PU are using the same channel, which can be

shown in traditional AODV protocol from the graph. However, the average end-to-

end delay for the proposed routing scheme will always maintain a constant with

lower value as compared to the traditional AODV protocol. The reason is that the

proposed routing scheme enables the SU to switch to another available channel once

it aware of the PU is listening to the channel that SU is currently listening to avoid

congestion. However, the traditional AODV protocol does not have the ability of

dynamic spectrum access.

4393 4392

4393 4393

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 100

Th

rou

gh

pu

t (b

its/

s)

PU usage ratio (%)


Proposed

AODV

51

Figure 4.11: Average end-to-end delay of SU by varying the time interval of PU in the model

shown in Figure 3.9



2.026242

1.605684

1.605684

0

0.5

1

1.5

2

2.5

1 2 4 6 8 10 12 14 16 18 20

Av

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(m

s)

Time interval (s)


time interval of PU transmission

AODV

Proposed

2.079383

1.73482

1.73482

1.6

1.7

1.8

1.9

2

2.1

2.2

1 2 4 6 8 10 12 14 16 18 20

Av

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(m

s)

Time interval (s)



AODV

Proposed

52





10.195236

8.022576

8.022576

7

7.5

8

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10

10.5

1 2 4 6 8 10 12 14 16 18 20

Av

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AODV

Proposed

25.74253

22.959503

22.959503

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AODV

Proposed

53

4.2.3 Third Scenario

To evaluate the third scenario as stated in section 3.4.2.2.3, a line graph

representing the average end-to-end delay of SU by varying the number of PU in the

model as shown in Figure 3.12 is illustrated in Figure 4.15. Base on result shown in

the graph for the conventional AODV routing protocol, it shows that by fixing the

number of SU and increasing the number of PU in a network, it will degrade the

performance in SU by increasing the average end-to-end delay of SU. However, the

average end-to-end delay for the proposed routing scheme shows a constant with

low value as compared to the traditional AODV protocol. This is due to the

proposed routing scheme can reduce the link failure probability by avoiding the

PHN and PEN problems and also perform a proper channel switching to avoid

interference with the PU.

Figure 4.15: Average end-to-end delay of SU by varying the number of PU in the model shown

in Figure 3.12

22.959503

30.330211

22.959503

20

22

24

26

28

30

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2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44

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(m

s)

Number of PUs


number of PU

AODV

Proposed

54

4.2.4 Fourth Scenario

To evaluate the fourth scenario as stated in section 3.4.2.2.4, a line graph

representing the average end-to-end delay of SU by varying the number of SU in the

model as shown in Figure 3.12 is illustrated in Figure 4.16. Base on the results

shown in the graph, it has proven that when the number of SU is increased and the

number of PU is fixed, the average end-to-end delay of the SU will also increase if

the position of the sender and receiver is located at one end to the other in a network

and the distance between them are getting further from each other. The line graph

consists of a slight fluctuation when the number of SU is increased to a certain value

and this phenomenon is due to that the position of the additional SU is placed

randomly in the network. However, the main focus of the analysis on this graph is to

present the spectrum aware proposed routing scheme will always has the same or

lower in term of average end-to-end delay of SU as compared to the traditional

AODV routing protocol when the number of SU is increasing.

Figure 4.16: Average end-to-end delay of SU by varying the number of SU in the model shown

in Figure 3.12

1.576752

24.278912

21.553103

0

5

10

15

20

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30

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44

Av

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(m

s)

Number of SUs


number of SU

AODV

Proposed

55

4.2.5 Fifth Scenario

To evaluate the fifth scenario as stated in section 3.4.2.2.5, a line graph

representing the average end-to-end delay of SU with the variation of the usage ratio

of PU with different number of PU by implementing the conventional AODV

routing protocol and proposed spectrum aware routing scheme in a CRN is

illustrated in Figure 4.17(a) and 4.17(b), respectively. Base on the results as shown

in Figure 4.17(a), it has proven that by implementing the traditional AODV protocol

in a CRN, the average end-to-end delay of SU will keep on increasing when the

number of PU is increasing as well as the channel utilization of PU is also

increasing. However, in the case where the CRN consists of least amount of PU and

a large amount of SU, the performance of SU in term of average end-to-end delay is

remained about constant even if the usage ratio of PU is keep on increasing. It can

be shown in the case of 2, 4 and 6 PUs in the network whereby the average end-to-

end delay of the SU is about constant at 23ms throughout the whole variation of PU

usage ratio. This is due to the insignificant influence of the activity of the least

amount of PU towards a large number of SU in a CRN. On the other hand, by

implementing the proposed routing scheme in the CRN, it shows that the average

end-to-end delay of the SU will remain constant and as low as about 23ms from the

CRN with 2 PUs to 44 PUs throughout the whole variation of PU usage ratio. It has

proven in Figure 4.17(b) that the performance of SU in a CRN with 2 PUs and 44

PUs will be similar when the proposed routing scheme is implemented. This is due

to that the proposed routing scheme can avoid PHN and PEN problem and also has

the ability to aware of the activity of PU and perform proper channel switching to an

available channel. Base on the results recorded and plotted in graph as shown in

Figure 4.17(a) and 4.17(b), a line graph that presents the minimum usage ratio of PU

to increase the latency of SU by varying the number of PU is plotted as shown in

Figure 4.18. From the graph, it shows that at the least number of 2 PUs with the

large number of 50 SUs in a CRN, although the PU with a 100% channel utilization,

it will not influence the performance of SU significantly. However, when the

number of PU is increased to 6 users, at the minimum usage ratio of 30% in PU will

start to increase the latency in SU. By increasing the number of PU from 8 to 44

PUs, a minimum usage ratio of 10% in PU is able to influence the performance of

56

SU in term of latency or end-to-end delay. It can be shown in the traditional AODV

routing protocol in Figure 4.18. However, by implementing the proposed routing

scheme, it is proven that there is no minimum usage ratio of PU that will increase

the latency in SU as shown in Figure 4.18 whereby the delay in SU will remain

constant throughout the variation in number of PU and usage ratio of PU. This is due

to the ability of spectrum aware in the proposed routing scheme that enable the SU

to perform dynamically channel switching in order to avoid interference to the PU

which will degrade the performance by increasing the latency or end-to-end delay.

Hence, in the network that consists of 44 PUs and 50 SUs as shown in Figure 3.14,

the SU with the proposed routing scheme will perform similar as the network that

contains only 2 PUs and 50 SUs as shown in Figure 3.13 with the traditional AODV

routing protocol.

Figure 4.17(a): Average end-to-end delay of SU by varying usage ratio of PU with different

number of PU in the CRN with traditional AODV routing protocol

22

23

24

25

26

27

28

29

30

31

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Av

era

ge

en

d-t

o-e

nd

de

lay

(m

s)

PU usage ratio (%)

Average end-to-end delay of SU versus PU

usage ratio with different number of PU2 PUs

4 PUs

6 PUs

8 PUs

10 PUs

12 PUs

14 PUs

16 PUs

18 PUs

20 PUs

22 PUs

24 PUs

26 PUs

28 PUs

30 PUs

32 PUs

34 PUs

36 PUs

38 PUs

40 PUs

42 PUs

44 PUs

Figure 4.17(b): Average end

number of PU in the CRN

Figure 4.18: Minimum usage ratio of PU to increase latency in SU by varying number of PU

22.959503

22

23

24

25

26

27

28

29

30

31

0 5

10

15

Av

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en

d-t

o-e

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(m

s)Average end

usage ratio with different number of PU

0

20

40

60

80

100

2 4 6 8

Min

. P

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Min. PU usage ratio to increase latency in

Average end-to-end delay of SU by varying usage ratio of PU with differ

number of PU in the CRN with proposed routing scheme

: Minimum usage ratio of PU to increase latency in SU by varying number of PU

22.959503

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

PU usage ratio (%)

Average end-to-end delay of SU versus PU

usage ratio with different number of PU

10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44

Number of PUs


SU versus number of PU

57

end delay of SU by varying usage ratio of PU with different

with proposed routing scheme

: Minimum usage ratio of PU to increase latency in SU by varying number of PU

90

95

10

0

end delay of SU versus PU

usage ratio with different number of PU2 PUs

4 PUs

6 PUs

8 PUs

10 PUs

12 PUs

14 PUs

16 PUs

18 PUs

20 PUs

22 PUs

24 PUs

26 PUs

28 PUs

30 PUs

32 PUs

34 PUs

36 PUs

38 PUs

40 PUs

42 PUs

44 PUs

44


AODV

Proposed

58

CHAPTER 5: CONCLUSIONS AND

RECOMMENDATIONS

5.1 Conclusion

Based on the current wireless communication technology, the inefficient use

of the static spectrum allocation has led to the underutilization of spectrum band

which causes white spaces. Hence, there is a need of inventing a new technology to

optimize the utilization of the limited radio bandwidth. CR technology is then

proposed to enable SU to occupy the unused spectrum band or white space without

influence the performance of PU by fulfilling the QoS requirement. For the SU to

perform dynamic spectrum access in utilizing the licensed band, the awareness

towards the activity of PU has to be taken into consideration, in order to avoid

interference with the PU and perform a proper channel switching. Hence, routing is

one of the main key to optimize the performance in SU without interfere to the PU

by a proper channel selection which is the core in this project.

There are many research papers have been studied to realize the

requirements for a CRN system. Several routing protocols have been proposed to be

implemented into the CRN to overcome the current spectrum crisis and the

unbalanced spectrum utilization. However, the respective proposed routing protocols

have their own limitation while applying into the CRN.

In this project, a novel spectrum aware routing scheme has been proposed.

The details of the performance for the proposed routing scheme in CRMN are

further elaborated in chapter 3. In general, the proposed routing scheme will

generate three channel lists between two neighbouring SUs when they are intended

to communicate to each other. The first proposed channel list is known as beta

channel list (BCL) and it will store the common SA between the two respective SUs.

The channel in BCL will meet the minimum requirement and could be used for

communication between the two SUs. The second proposed channel list is known as

gamma channel list (GCL) which will store the common SI between the two

59

neighbouring SUs and also store the channel that left in the network which is not

contained in both respective SI of the two users. The channel in GCL can be used

for communication which can prevent the PEN and PHN problems. The third and

also the last proposed channel list is known as delta channel list (DCL) which will

store the common channels that are presented in BCL and DCL from the two

neighbouring SUs. The channel in DCL will be used by the two neighbouring SUs

to communicate and it is the best channel to be used to avoid the uncertainty in the

scenario as stated earlier for performance optimization.

The proposed spectrum aware routing scheme is implemented into a CRMN

and the performance is compared to the conventional AODV routing protocol. Base

on the simulation results and analysis proved that the proposed routing scheme is

outperforms the traditional AODV routing protocol. Moreover, the novel proposed

spectrum aware routing scheme has the ability to eliminate the PHN and PEN

problems and enables the SU aware of the activity of PU and performs dynamically

channel switching.

5.2 Recommendation

Several future research works based on this project are suggested in this

section.

5.2.1 Parameter Setting

It is interesting to investigate the different type of radio being used to

implement the proposed routing scheme. For example, instead of using radio type of

802.11a/g which is using the modulation technique of orthogonal frequency-division

multiplexing (OFDM), 802.11b radio type which is using the modulation technique

of direct-sequence spread spectrum (DSSS) might be used.

60

5.2.2 Comparison between Proposed Routing Scheme with Different

Routing Protocols

The performance comparison between the proposed routing scheme with

other conventional routing protocols could be a potential future research work.

Besides the conventional AODV routing protocol, other routing protocol such as

Bellman Ford or Dynamic Source Routing (DSR) could also be implemented into

the CRMN and the performance based on the simulation results are compared with

the proposed routing scheme.

5.2.3 Different Types of Scenario and Model

More types of model and scenario could be constructed using simulation

software such as MATLAB, QUALNET, OPNET or NS2 to evaluate the

performance of the proposed routing scheme over the conventional routing

protocols. It is to investigate and further prove the flexibility of the proposed routing

scheme without any constrain.

61

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64

Appendix A – Patent Filing

[1] Wai Kean et.al, “A SYSTEM AND METHOD FOR ROUTING IN A NETWORK”,

(PI2012701087) filed by MIMOS BERHAD.

spectrum aware routing in cognitive mesh network...

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