FUZZY LOGIC-BASED MOBILITY MANAGEMENT SCHEME FOR CELLULAR RADIO SYSTEM

RIZAL MUNADI

THESIS SUBMITTED IN FULFILMENT FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

FACULTY OF ENGINEERING AND BUILT ENVIRONMENT UNIVERSITI KEBANGSAAN MALAYSIA

BANGI

2011

SKEMA PENGURUSAN MOBILITI BERASASKAN LOGIK KABUR BAGI SISTEM RADIO SELULAR

RIZAL MUNADI

TESIS YANG DIKEMUKAKAN UNTUK MEMPEROLEH IJAZAH DOKTOR FALSAFAH

FAKULTI KEJURUTERAAN DAN ALAM BINA

UNIVERSITI KEBANGSAAN MALAYSIA BANGI

2011

iii

DECLARATION

I hereby declare that the work in this thesis is my own except for quotations and

summaries which have been duly acknowledged.

12 July 2011 RIZAL MUNADI P23689

iv

ACKNOWLEDGMENT

All praise belongs to Allah who has bestowed upon me the strength and will to complete this thesis. Peace and blessings of Allah be upon His Messenger, the Prophet Muhammad and his family.

I then wish to express my deep and lasting appreciation to the many individuals who have contributed towards the completion and submission of this thesis. May Allah reward His blessing on them for their sincere cooperation. Among those that I take pleasure to mention includes:

My former first supervisor Assoc. Prof. Zainol Abidin Abdul Rashid, and the

first supervisor Prof. Mahamod Ismail and co-supervisor Assoc. Prof. Ir. Dr. Mardina Abdullah for their guidance, valuable advises and encouragement, time and knowledge through the entire work and writing this thesis. For a short period this work is supported by IRPA grant: 04-02-02-0029, I would like to express many thanks to the IRPA Secretariat, Ministry of Science, Technology and Innovation (MOSTI) of Malaysia, for sponsoring this work. I am also grateful for the conferment of UKM Zamalah scholarships that have helped financially for five semesters.

My colleagues at SMRG laboratory, Dr. Tariqul Islam, Dr. Awad Momani, Dr. Wayan Suparta, and Sumazly Sulaiman.

I also wish to thank all laboratory assistants and technicians in the Department of Electrical, Electronics and Systems Engineering and ANGKASA staffs for their cooperation and support.

Also, I want to thank, Dr. Heikki Kaaranen for discussion and explanation

about UMTS network via email and Torsten Rüdenbusch for permission and giving all the pictures from his book. They are the author of two books used as reference and become a very meaningful for the content of this thesis.

My parents, Boerhanoeddin S. Moehdy and Rostina for their support and constant pray. Also, I want to express my appreciation to my sister and brothers.

Finally, I must state that it would have been impossible for me to do any important work to finalize this thesis without pray and warm support of my wife, Sitti Yudrika and my children: Fathan Mumtaz Yunadi, Rania Zharifah Mumayyaz and Farras Hammam Yunadi. They surrounded me with a very kind atmosphere and plenty of understanding. They allowed me the time to write I should have enjoyed with them both instead. May Allah accept and bless this work and effort for, eventually, everything is done in seeking His pleasure.

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ABSTRACT Mobility management plays an important role in wireless mobile networks in effectively delivering services to the mobile users. In cellular network, mobility management has allowed mobile subscribers to move freely across different networks while maintaining its quality of service for a variety of applications. Location management is one of the main tasks of mobility management which has two basic procedures: location update and paging, which is used to manage call delivery and to maintain connection while roaming in service area. The biggest challenge in location management research is to find the most favorable trade off between location update and paging. Many location management techniques have been explored to reduce signaling cost of the Second Generation and the Third Generation wireless network such as location area design, database distribution, paging, static and dynamic location update schemes. The location management cost depends mainly on subscribers’ mobility behavior and its performance which is typically measured by the number of location update performed and the number of cells paged. The objective of this research is to propose, validate and evaluate the performance of location update scheme using fuzzy logic technique for cellular radio system. Two kinds of mobility model based on random walk mobility and street lane mobility are developed and then tested using balanced and un-balanced location area models. The service area in both models consists of 49 cells which are divided into seven clusters. The clusters of unbalanced model are not uniform in terms of the number of cells. A population of mobile subscribers is generated in accordance to the mobility models. A combination of distance, time, and movement scheme is used to evaluate location update cost. Performance evaluation with fuzzy logic technique uses residence time and speed of mobile subscriber as input parameters. The results of street-lane and four-direction random walk mobility models show that most mobile subscribers are distributed in the center cluster of simulated balanced and unbalanced location area. Meanwhile, eight-direction random-walk mobility model indicates that most of mobile subscribers are concentrated at certain edge-cluster of simulated service area of both balanced and un-balanced location area models. Based on the location update of combination schemes tested, an unbalanced model indicates slightly lower location update activities compared to balanced model. The results of using fuzzy logic technique show that location update cost using fuzzy logic is decreased. The reductions are varied depended on the strategies implementation. These results prove that user’s mobility has influenced location management cost in cellular radio service area.

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SKEMA PENGURUSAN MOBILITI BERASASKAN LOGIK KABUR BAGI SISTEM RADIO SELULAR

ABSTRAK

Pengurusan mobiliti memainkan peranan yang penting dalam rangkaian bergerak tanpa wayar bagi menyediakan perkhidmatan kepada pelanggan bergerak secara berkesan. Bagi rangkaian selular, pengurusan mobiliti membenarkan pelanggan berpindah secara bebas melintasi rangkaian yang berbeza di samping mengekalkan kualiti perkhidmatan untuk pelbagai aplikasi. Pengurusan lokasi merupakan satu tugas utama daripada pengurusan mobiliti yang mempunyai dua prosedur asas: kemaskini lokasi dan keloi yang digunakan untuk mengurus penghantaran panggilan dan mengekalkan sambungan semasa perayauan di dalam kawasan perkhidmatan. Cabaran terbesar dalam penyelidikan pengurusan lokasi adalah untuk mendapatkan timbal balik yang paling baik antara kemaskini lokasi dan keloi. Beberapa kaedah pengurusan lokasi telah dikaji untuk mengurangkan kos pengisyaratan dalam rangkaian tanpa wayar Generasi Kedua dan Generasi Ketiga seperti rekabentuk kawasan lokasi, taburan pangkalan data, keloi, skema kemaskini lokasi statik dan dinamik. Kos pengurusan lokasi secara asasnya bergantung kepada kelakuan mobiliti pelanggan dan prestasinya lazimnya diukur berdasarkan jumlah kemaskini yang dihasilkan dan jumlah sel yang dikeloi. Objektif bagi penyelidikan ini adalah untuk mencadangkan, mengesahkan dan menilai prestasi skema kemaskini lokasi yang menggunakan teknik logik kabur bagi sistem radio bersel. Dua jenis model mobiliti berasaskan model mobiliti yang bergerak secara rawak dan model mobiliti lorong jalan telah dibangunkan dan kemudian diuji dengan menggunakan model kawasan perkhidmatan: model lokasi kawasan terimbang dan tak terimbang. Kawasan perkhidmatan mengandungi 49 sel yang dibahagikan kepada tujuh kelompok. Setiap kelompok dari model tak terimbang adalah tidak sama dari segi bilangan sel. Sekumpulan pelanggan bergerak yang dijana mengikut model-model mobiliti ini. Gabungan dari skema jarak, masa dan pergerakan telah digunakan untuk menilai kos kemaskini lokasi. Penilaian prestasi bagi logik kabur menggunakan parameter masukan masa tinggal, ketumpatan dan kelajuan pelanggan bergerak. Keputusan dari model mobiliti mengikut lorong jalan dan jalan rawak empat arah menunjukkan bahawa bahagian tengah dari kawasan perkhidmatan mempunyai taburan pelanggan terbanyak untuk model lokasi kawasan terimbang dan lokasi kawasan tak terimbang. Seterusnya, keputusan penggunaan mobiliti jalan rawak lapan arah menunjukkan taburan pelanggan terbanyak tersebar di dalam kelompok yang bersempadan dengan pusat kawasan perkhidmatan untuk kedua model lokasi bagi kawasan terimbang dan kawasan tak terimbang. Berdasarkan pelbagai gabungan skema kemaskini lokasi yang diuji, model kawasan tak terimbang memperlihatkan isyarat kemaskini lokasi yang lebih rendah dibandingkan model kawasan terimbang. Keputusan menggunakan kaedah logik kabur menunjukkan bahawa kos kemaskini lokasi berjaya dikurangkan. Pengurangan adalah bergantung pada strategi yang digunapakai. Keputusan ini membuktikan bahawa mobiliti pengguna mempengaruhi kos pengurusan lokasi dalam kawasan perkhidmatan radio selular.

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CONTENTS

Page

DECLARATION iii

ACKNOWLEDGMENT iv

ABSTRAK v

ABSTRACT vi

CONTENTS vii

LIST OF FIGURES x

LIST OF TABLES xiii

LIST OF ABBREVIATIONS xiv

CHAPTER I INTRODUCTION

1.1 Introduction 1

1.2 Problem Statement 6

1.3 Research Objective 7

1.4 Research Motivation and Scope 8

1.5 Thesis Contribution 9

1.6 Thesis Organization 10

CHAPTER II LITERATURE REVIEW

2.1 Introduction 11

2.2 Cellular Technology Evolution and Network 11

2.2.1 Cellular technology evolution 13 2.2.2 2G network architecture 16 2.2.3 3G network architecture 18

2.3 Radio Resource Management 25

2.3.1 Cellular network database 26 2.3.2 Calling process 28 2.3.3 Signaling process 29 2.3.4 Connected mode status 31

2.4 Mobility Management 35

2.4.1 Mobility models in cellular system 36 2.4.2 Random walk mobility model 38 2.4.3 Fluid flow mobility model 40

2.4.4 Random Gauss-Markov model 41

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2.4.5 Random direction mobility model 41 2.4.6 City area, zone area and street unit mobility model 42

2.5 Location Management 44

2.6 Location Update 45

2.6.1 Registration area location update schemes 47 2.6.2 Movement-based scheme 48 2.6.3 Timer-based scheme 49 2.6.4 Distance-based scheme 50 2.6.5 Adaptive direction-based scheme 51

2.7 Paging Scheme 52

2.7.1 Blanket polling paging scheme 52 2.7.2 Shortest distance first paging scheme 53 2.7.3 Sequential paging scheme 53 2.7.4 Selective paging scheme 54

2.8 Application of Optimization Technique 54

2.8.1 Graph theory 54 2.8.2 Fuzzy logic 57 2.8.3 Neuro-fuzzy 65

2.9 Summary 66

CHAPTER III RESEARCH METHODOLOGY

3.1 Introduction 67

3.2 Simulation Environment 67

3.3 Cell Clustering Model 69

3.4 Mobility Model 72

3.4.1 Random walk mobility model 73 3.4.2 Street lane model 74

3.5 Mobility Management Evaluation 76

3.6 Computer Simulation Development 78

3.7 Development Design Using Fuzzy Logic Technique 81

3.8 Summary 85

CHAPTER IV LOCATION UPDATE AND PAGING RESULTS

4.1 Introduction 86

4.2 Mobility Models Analysis 87

4.2.1 Random walk mobility model analysis 88 4.2.2 Street lane model analysis 88

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4.3 Location Management Cost 89

4.3.1 Location update cost 90 4.3.2 Paging cost 92 4.3.3 Location update and paging cost analysis 92

4.4 Summary 99

CHAPTER V FUZZY LOGIC TECHNIQUE AND RESULTS

5.1 Introduction 101

5.2 Graph Analysis 101

5.3 Fuzzy Logic Analysis 107

5.4 Performance Evaluation 111

5.5 Summary 112

CHAPTER VI CONCLUSION

6.1 Introduction 114

6.2 Conclusion 115

6.3 Future Work 116

REFERENCES 117

APPENDIX

A List of Publications 127

B Pantern Index 129

C Psedo Code 130

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LIST OF FIGURES

Figure No. Page

1.1 Subscriber market share in Malaysia 2

1.2 Asia Pacific top 10 markets by customers 3

1.3 Worldwide mobile subscriptions 4

1.4 Total cellular subscriptions worldwide 5

1.5 Mobile subscriptions distribution 5

2.1 Cellular technology evolution 15

2.2 Cellular standards evolution 16

2.3 GSM network architecture 17

2.4 Cell coverage area types 18

2.5 UMTS areas 18

2.6 International mobile subscriber identity 20

2.7 Structure of international mobile subscriber identity 21

2.8 Structure of location area identity 21

2.9 UMTS release 99 network architecture 23

2.10 UMTS release 4 24

2.11 UTRAN architecture 25

2.12 Two-level hierarchy of databases: HLR And VLR 27

2.13 Call routing for a mobile terminating call 29

2.14 Initial UE radio access 30

2.15 UTRAN - connected mode states 31

2.16 RRC signaling connection (message flow) 35

2.17 A concept map of mobility models 37

2.18 Random walk mobility model 40

2.19 Location update diagram in GSM 46

2.20 Movement-based 49

2.21 Timer-based 50

2.22 Distance-based 51

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2.23 Movement and timing 51

2.24 Graph and value 55

2.25 Graph matrices 56

2.26 Isomophism graph and matrices 56

2.27 Fuzzy set to charaterize the room temperature 59

2.28 Union operation 60

2.29 Intersection operation 61

2.30 Complement Operation 61

2.31 Fuzzy inference system 64

2.32 An example of fuzzy sets 65

3.1 Hexagonal cell geometry 68

3.2 Simulation service area 69

3.3 Balanced-cell model 71

3.4 Unbalanced-cell model 72

3.5 Random walk with certain direction 74

3.6 Street lane layout 75

3.7 Simulation flow chart 80

3.8 Triangular function 82

3.9 Membership function of input variable “Speed” 83

3.10 Membership function of input variable “Density” 83

3.11 Membership function of input variable “Restime” 84

3.12 Membership function of output variable “Results” 84

4.1 Initial MSs in the simulation area 87

4.2 An example of MS trajectory using random walk mobility model 88

4.3 An example of highway model for the proposed model 89

4.4 A MS travels path 91

4.5 Location update performance 93

4.6 Paging performance 94

4.7 Signaling in VLR system 95

4.8 Signaling in HLR system 96

4.9 Total of signaling transaction 96

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4.10 Network efficiency performance of proposed strategies 97

4.11 A mixed LU and Paging signaling activities 98

4.12 Comparison of mixed LU-Paging strategies performance 99

5.1 Unique pattern of 4-directions 102

5.2 MS distribution for scheme A using balanced-cell strategy 103

5.3 MS distribution for scheme B using balanced-cell strategy 104

5.4 MS distribution for scheme C using balanced-cell strategy 104

5.5 MS distribution for scheme A using unbalanced-cell strategy 105

5.6 MS distribution for scheme B using unbalanced-cell strategy 105

5.7 MS distribution for scheme C using unbalanced-cell strategy 106

5.8 Movement pattern of the tested schemes 106

5.9 Direction scheme pattern distribution 107

5.10 If-then structure in fuzzy environment 108

5.11 Surface of residence time and density 109

5.12 Surface of speed and residence time 110

5.13 Surface of speed and density 110

5.14 An example of result using fuzzy logic 111

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LIST OF TABLES

Table No. Page

2.1 MCC, MNC, and Network Operators 20

2.2 Mobility model matching real-world 38

3.1 Symmetrical cell clustering 70

3.2 Asymmetrical cell clustering 71

3.3 Threshold value of movement direction 73

3.4 Pattern index 78

4.1 Strategy for mixed LU-Paging pair 98

5.1 Pattern distributions 103

5.2 The proposed 10-set of fuzzy rules 108

5.3 Conventional and fuzzy results comparison 112

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LIST OF ABBREVIATIONS 1G First Generation of Cellular Technology

2G Second Generation of Cellular Technology

3G Third Generation of Cellular Technology

3GPP Third Generation Partnership Project

AMPS Advanced Mobile Phone Service

AuC Authentication Center

BCH Broadcast Channel

BS Base Station

BSS Base Station Subsystem

CCPCH Common Control Physical Channel

CD Call Delivery

CDMA Code Division Multiple Access

CIC Cell Identification Code

CPICH Common Pilot Channel

CPT Cell Priority Transition

CN Core Network

DCCH Dedicated Control Channel

DCH Dedicated Transport Channel

EDGE Enhanced Data Rates for Global Evolution

EGPRS Enhanced General Packet Radio Service

ETSI European Telecommunications Standard Institute

FDD Frequency Division Duplex

FFMM Fluid Flow Mobility Model

FHLS Fuzzy Hierarchical Location Service

FIS Fuzzy Inference System

FNN Fuzzy Neural Network

GERAN GPRS/EDGE Radio Access Network

GLA Gateway Location Area

GLR Gateway Location Register

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GMSC Gateway Mobile Switching Center

GPRS General Packet Radio Service

GSM Global System for Mobile Communication

GR Group Registration

HSCSD High Speed Circuit Switched Data

HLR Home Location Register

HSDPA High-Speed Downlink Packet Access

HSPA High-Speed Packet Access

HSS Home Subscriber Server

IMSI International Mobile Subscriber Identity

IMTS Improved Mobile Telephone Service

IPG Individual Profile Graph

IPv6 Internet Protocol version 6

ISDN Integrated Services Digital Network

LA Location Area

LAC Location Area Code

LAI Location Area Identity

LMA Local Mobility Anchor

LU Location Update

MAG Mobile Access Gateway

MANET Mobile Ad-hoc Network

MCC Mobile Country Code

MCMC Malaysian Communications and Multimedia Commission

MDP Markovian Decision Process

MGW Media Gateway

MBMS Multimedia Broadcast and Multicast Service

MM Mobility Management

MNC Mobile Network Code

MNP Mobile Number Portability

MRDMM Modified Random Direction Mobility Model

MSRN Mobile Station Roaming Number

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MSC Mobile Switching Center

MSIN Mobile Subscriber Identity Number

MSISDN Mobile Subscriber ISDN

NMT Nordic Mobile Telephony

NAS Non Access Stratum

NLU No Location Update

NFS Neuro Fuzzy System

NSS Network Switching Subsystem

NU Neighborhood Update

P-CCPCH Primary Common Control Physical Channel

PLMN Public Land Mobile Network

PMIPv6 Proxy Mobile IPv6

PSCH Primary Synchronization Channel

PSS Packet-Switched Streaming Services

PSTN Public Switched Telephone Network

RA Routing Area

RAC Routing Area Code

RAI Routing Area Identity

RDMM Random Direction Mobility Model

RGMM Random Gauss-Markov Model

RNC Radio Network Controller

RNS Radio Network Subsystem

RR Radio Resource

RRM Radio Resource Management

RWMM Random Walk Mobility Model

RWyMM Random Waypoint Mobility Model

SCCP Signaling Connection Control Part

SGSN Serving GPRS Support Node

SMS Short Message Service

SS7 Signaling System Number 7

SSCH Secondary Synchronization Channel

xvii

TACS Total Access Communication System

TMSI Temporary Mobile Subscriber Identity

TCH Transport Channel

TDD Time Division Duplex

TDMA Time Division Multiple Access

UE User Equipment

UMTS Universal Mobile Telecommunications System

UPH User Profile History

URA UTRAN Registration Areas

UTRAN UMTS Terrestrial Radio Access Network

UMTS Universal Mobile Telecommunications System

VLR Visitor Location Register

WCDMA Wideband Code Division Multiple Access

CHAPTER I

INTRODUCTION

1.1 INTRODUCTION

Over the last few years, worldwide cellular market shows tremendous growth.

Cellular phone manufacturers, cellular operators, and customers are the three key

players behind the rapid growth of cellular phone market. Cellular phone

manufacturers keep niche market by launching new models of cellular phones on a

regular basis. On the other hand, cellular operators or cellular network providers

improve quality of service and offer additional value added services as an attempt to

retain existing customers and intensified promotional efforts to attract new prospects

subscriber. In addition, the purchasing power of consumers or the economic growth of

a country also has a very significant role. Nowadays, most people are familiar with

cellular communication technology even in developing or poor countries. It must be

recognized that the cellular phone technology has a considerable impact for life today.

The success story of cellular communication that attract more users did not

happen in the First Generation (1G) of cellular technology when it came to market. In

the first generation, there are some technology disadvantages such as lack of security,

limited features and services. In addition, cellular phone prices and communication

tariff are too expensive for most people are another reason this technology is not

preferred and then shorten its business life cycle. Later, the Second Generation (2G)

of cellular technology comes and has dramatically changed cellular market. Cellular

operators revise their communication tariff structures, a new feature: Short Message

Service (SMS) is introduced and make it competitive to attract more subscribers. In

this stage, voice quality has been improved, data communication capability is added,

2

and digital technology has been applied to the 2G communication system have

attracted many people to be customers. At this moment, the 2G of cellular technology

is still available and while the Third Generation (3G) of cellular technology gradually

operates to serve customer with more features and services.

The cellular services in Malaysia is essentially an oligopoly market comprising

Celcom, Digi and Maxis. In Malaysian Communications and Multimedia Commission

(MCMC) report of the fourth quater 2007, the cellular service providers in Malaysia

have a combined total of 22.1 million subscriptions; the cellular service market is

among the more matured markets in the region with a penetration rate of 80.8 per 100

inhabitants (MCMC 2007). The market share penetration around 2005 to 2006 is

shown in Figure 1.1.

Figure 1.1 Subscriber market share in Malaysia

Source: MCMC 2007

In Malaysia, Mobile Number Portability (MNP) was introduced in 2007 to

cellular subscribers by the MCMC and then adopted by operators. This

implementation is expected to lead to a more competitive and efficient

telecommunications environment. Number portability is a circuit-switch network

feature that provides consumers with the ability to change service providers, locations,

or service types without changing their telephone numbers. MNP will be particularly

beneficial to business users as it enables them to change service providers whilst

saving them the cost, time and effort associated with a mobile number change. In the

ASEAN region, Singapore has adopted in 1997, but on the other hand, this model is

not offered in Indonesia which has bigger market compared to Malaysia and

Subscriber Market Share - 2005

Maxis; 41%

Digi; 24%

Celcom; 35%

Subscriber Market Share - 2006

Maxis; 42%

Digi; 27%

Celcom; 31%

3

Singapore. One of the cellular operators in Indonesia, PT Telkomsel, Tbk. has more

than 90 million cellular subscribers.

In many countries, the registered cellular phone subscribers can be more than

the total number of people. This can be assumed that many people have more than one

cellular subscription, possibly one for private use and one for work. In this way,

cellular technology has enabled market penetration to become more than 100% in

certain country. In Asia Pacific region, there are five of the world’s ten largest mobile

markets - China and India, which are the number one and two respectively and

Indonesia, Japan and Pakistan, the sixth, eighth and tenth largest. These five countries

occupy the top five places in the regional list, which is completed through the addition

of the Philippines, Vietnam, Thailand, South Korea and Bangladesh as shown in

Figure 1.2.

Figure 1.2 Asia Pacific top 10 markets by customers

Source: Anon 2009

One of the basic characteristics services of the cellular network that differs

from fixed communication is the user’s ability to perform or receive calls in mobile

activity. One of the 2G which dominantly appears world wide up to this time is Global

System for Mobile Communication (GSM). In fact, every day, there are more than one

million new additions to the GSM family of technology users receiving service from

4

one of 700 commercial GSM networks across 218 countries and territories around the

world (Anon 2010a). In 2007, 3G Americas reported that the number of GSM

subscribers worldwide has reached 2.5 billion, a stunning 400% increase in GSM

subscribers from only six years ago, according to the estimates of Informa’s World

Cellular Information Service. “It’s unprecedented for almost any global industry to

achieve the growth and success demonstrated by the GSM family of technologies,

with an estimated 2.5 billion global customers today,” stated Chris Pearson, President

of 3G Americas (Anon 2010a). This can be attributed to several factors such as price,

people awareness, technology and features, cellular handset model, radio coverage

and services. 3G technology as a new entrant in the existing market gradually has

started to disrupt GSM market. As shown in Figure 1.3, Informa Telecoms & Media

report in December 2009 that the penetration of GSM subscribers have reached about

3.7 billion.

Figure 1.3 Worldwide mobile subscriptions

Source: Anon 2010b

The immediate motivating factor for 3G communication system is to increase

system capacity. This technology provides the ability to supplement 2G services.

Universal Mobile Telecommunications System (UMTS) is a 3G telecommunications

technology for mobile devices. The most common form of UMTS makes use of

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Wideband Code Division Multiple Access (WCDMA) that is an air interface standard

and most notably find in 3G mobile telecommunications networks. However, the fact

shows that one of the 2G technologies, at this moment GSM is still the most widely

installed wireless technology in the world. In Figure 1.4, GSM subscriptions reach 4

billion or 77% of total global subscriptions. In term of user distribution, 48% of GSM

subscriptions are in Asia Pacific countries which are the biggest market of mobile

users as shown in Figure 1.5.

Figure 1.4 Total cellular subscriptions worldwide

Source: Anon 2011a

Figure 1.5 Mobile subscriptions distribution

Source: Anon 2011b

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1.2 PROBLEM STATEMENTS

In cellular technology, communication service is determined by the availability of

services at the time of communication is done. As the number of subscribers increases

given a fixed radio spectrum allocation, in order to accommodate the higher

subscriber densities, more signaling consumes scarce radio bandwidth. Increased

signaling incurs additional cost to operators by consuming network resources thus

effecting revenue-generating traffic. To accomplish and minimize signaling activities,

a set of procedures of mobility tracking is performed which its main goal is to locate

cellular user. Therefore, the main solution for supporting the growing population is to

reduce cell size and to increase the bandwidth reuse (Jabbari et al. 1995 & Steele et al.

1995).

Two tasks in location management, Location Update (LU) and Paging

consume scarce resources like wireless network bandwidth and mobile equipment

power. Location management schemes are essentially based on users’ mobility and

incoming call rate characteristics (Tabbane 1997). Several location management

strategies have been proposed in the literature that attempt to minimize either the total

location management cost or individual costs of LU and paging. Intuitively, the

location accuracy depends in the location update frequency. The more frequent the

LU, the more accurate the location information. In other word, this frequent LU

activity will increase the cost of signaling and on the other hand the paging cost may

decrease.

The issue of location management cost has been a concern of many areas in

wireless communication. Several recent studies related to location management cost

including location update, paging and the use of fuzzy technique will be described. In

Vergados et al. (2007), they introduce a 2-level distributed database architecture

combined with the Group Registration (GR) location tracking strategy to be used in

3G wireless networks. With this strategy, the total location management cost is

reduced by updating the location of MSs in a registration area with a single route

response message to the HSS (Home Subscriber Server). Bae and Kim (2007)

proposed an adaptive location service on the basis of fuzzy logic called Fuzzy

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Hierarchical Location Service (FHLS) to minimize the sum of the location update cost

and paging cost. In a Mobile Ad-hoc Network (MANET) environment, Neighborhood

Update (NU), and location server update are two approaches to evaluate the cost

problem (Ye & Abouzeid 2008). Under a Markovian mobility model, the location

update decision problem is modeled as a Markovian Decision Process (MDP). Based

on the separable cost structure of the proposed MDP model, the location update

decisions on NU and LU can be independently carried out without loss of optimality.

Le et al. (2008) investigated update cost problem for database application which

introduce group update for traffic control using Group Update Time Parameter R-tree.

Another approach is applied an intelligent paging scheme with movement based LU

strategy to resolve the total cost (Chang et al. 2008). Zhao et al. (2009) study location

update cost using distance based. Osmani et al. (2009) proposed a method on mobility

management than can be used as an independent component to setup over different

hierarchical location services using fuzzy logic. They proved that the implemented

fuzzy logic has better management of location update operation in hierarchical

location services. Yi et al. (2010) investigate Proxy Mobile IPv6 (PMIPv6) as a

network-based mobility management protocol to support mobility for Internet

Protocol version 6 (IPv6). In the Mobile Access Gateway (MAG) incurs a high

signaling cost to update the location of a mobile node to the remote Local Mobility

Anchor (LMA) if the mobile node moves frequently. Their new mobility management

scheme proposal intended to minimize signaling cost using the pointer forwarding and

achieved superior performance than PMIPv6 scheme. Wang et al. (2010) worked on

the Cell Priority Transition (CPT) mechanism to reduce the location update cost for

the femtocell network. Their study shows that the proposed CPT mechanism reduces

the location update cost for femtocell networks. Singh and Karnan (2010) have

investigated location update cost and proposed an intelligent approach by taking a

User Profile History (UPH). Therefore, in this research, location management cost is

the issue to be solved in this thesis.

1.3 RESEARCH OBJECTIVE

In this thesis, the research objective is to find the minimum cost of location

management operation using mobility management concept in cellular communication

8

system. This cost can be done by analyzing the cost of location update and paging

process. To meet the objective of this research, a set of dynamic location management

schemes and mobility models for cellular network are proposed and run using

software simulator. The details research objective have to achieve are as follow:

a. To develop mobility models and simulator using MATLAB® for location

management schemes.

b. To evaluate the users’ mobility behavior of the models tested.

c. To evaluate the performance of the dynamic location management schemes.

d. To optimize the total cost of location management using Fuzzy Logic

technique.

1.4 RESEARCH MOTIVATION AND SCOPE

The increasing number of users, global connectivity, quality of service and the highly

intense competition in the mobile communications industry are a challenge that must

be faced by 2G and 3G providers of mobile communications. In cellular network, user

can roam the network and has its mobility services. This is the advantage for cellular

user; however, user mobility affects quality of service, and makes capacity planning

more difficult. User profile behavior is different in terms of mobility. Mobility models

have been proposed in many literatures for cellular and ad hoc network. Mobility

models are not comprehensive because of new applications, limited conditions and

network topology. For this reason, in this thesis a new street lane mobility model is

proposed while random walk mobility model also used to evaluate location

management problem. In the proposed location management strategy, mobility models

are independent to cellular technology generation. Both in 2G and 3G have the same

network element, Home Location Register (HLR) and Visitor Location Register

(VLR) and these databases are used to evaluate the location management

performance. To overcome all the critical issues regarding user mobility and signaling

activities should be done by optimizing the location management tasks. In this thesis,

pedestrian is not considering to evaluate because its speed can be zero. For highly

speed user, mobility can be divided into slow and high speed. As mentioned in many

studies, of the two tasks: location update and paging, there is a trade-off (Roy et al.

2007) and offer an opportunity to find an optimal solution. In this research, to evaluate

9

the cost of location management operation, the calculation cost is carried out with the

proposed mobility models, random walk mobility model and street lane mobility

model. These two mobility models used to evaluate some location management

schemes including location update and paging strategies. To optimize the result, fuzzy

logic technique is implemented and compares to the proposed conventional location

management schemes. The choice of mobility models in the simulation of this thesis is

not reflected the real user mobility. The simulation results have only obtained under

the defined parameters and the tested model. Since this simulation approach its

technology independent and can be used for any wireless technology, the result might

be varied to a certain wireless technology. This is the limitation of this work. For

further investigation, it needs to get the real value and used to evaluate location

management cost problem.

1.5 THESIS CONTRIBUTION

This research is about mobility management and location management cost. The

contributions of this thesis include:

a. The proposed street lane mobility model.

The street lane mobility model consists of three-lanes as the number of lanes is

commonly found on a highway. The model constructed is used to evaluate the

flow and distribution of users. For example, a car (user) is prohibited to jump

its position such as from lane number 1 to lane number 3.

b. The performance evaluation of users’ mobility behavior using pattern approach

and graph theory.

Highway users have limitation in terms of exchange of direction. The change

is only possible when the user speed up or slow down by moving vehicles into

lanes that are available. To identify the characteristics of mobility in highway

user behavior, a pattern that occurs can be studied using graph theory.

c. The performance evaluation of dynamic location management schemes.

LU and paging operations are the main activities to evaluate based on the

proposed schemes. The cost of conventional mechanism results are analyzed

and compared to fuzzy logic.

d. The cost optimization of using fuzzy logic technique in location management.

10

To optimize the cost result, fuzzy logic technique is selected and used to

evaluate the location management cost.

1.6 THESIS ORGANIZATION

In this chapter, a brief overview of cellular technology and market is introduced. At a

glance, the concept mobility management is described. The research objective and

scope are addressed in this thesis. Chapter 2 discusses the procedures involved in

location management, both in general terms, and as implemented in the 2G

technology, GSM, and 3G. Also mobility modeling, the previous work on location

management is reviewed. The advantages and disadvantages of various approaches

are discussed. Two tasks of location management, location update and paging, and

their related schemes, fuzzy technique are discussed. Methodology, the proposed and

tested mobility model: street lane mobility and random walk mobility are explained in

Chapter 3. In this thesis, the direction of user mobility is chosen randomly with the

probability 0.25. These direction choices represent to the real street model which a

vehicle can be moved forward, backward, turn right or left. For street lane mobility

model, a vehicle in the highway or street with 3 lanes can change or shift to a level up

or down only. The proposed mobility models are the basis of the simulation study and

then implemented to compute the location management cost using conventional

mechanism and then compare to the proposed fuzzy logic technique. In Chapter 4, the

result from the simulation study are presented and analyzed. Graph theory analysis

and fuzzy logic results are presented in Chapter 5. Finally, the performance of the

simulation result in location management and user behaviour including fuzzy logic

result will be summarizing in the Chapter 6.

CHAPTER II

LITERATURE REVIEW

2.1 INTRODUCTION

In this chapter, background of cellular technology evolution, 2G and 3G radio network

systems, mobility management including network and location management, calling and

signaling process will be presented. 1G is outdated technology and no longer in operation.

Now, 2G and 3G technology is available. 2G technology is in the mature cycle and still

exists at the moment. This network was built mainly for voice services and slow data

transmission while 3G network technology provides fast data transmission which vastly

increases quality of service run on the 3G networks. Many mobility models and location

management schemes for wireless technology will be reviewed. Graph theory and

optimization techniques such as fuzzy logic, genetic algorithm, neural network technique

which use in many application also will be described.

2.2 CELLULAR TECHNOLOGY EVOLUTION AND NETWORK

The development and history of the cellular technology has seen a tremendous number of

changes since the first cellular telephones were introduced. The main development that

distinguished the first generation cellular phones from the previous generation was the

use of multiple cell sites, and the ability to transfer calls from one site to the next as the

user travelled between cells during a conversation. The early cellular telephones were

very large and could certainly not be placed in a pocket like the handphones of today.

12

The idea of the first cellular network was brainstormed in 1947. It was proposed

to be used for military purposes as a way of supplying troops with more advanced forms

of communications (Kaul et al. 2008). In the 1960s, a new system launched by Bell

Systems, called Improved Mobile Telephone Service (IMTS) was introduced. In 1979,

Japan was the first country in the world operated cellular system and then followed by

some European countries. All the first European cellular systems are generally

incompatible with one another because of the different frequencies and communication

protocols used. Later, these systems are replaced by the Pan European digital standard as

the 2G technology which was first deployed in 1990 which all of Europe dedicated for

cellular telephone service (Maloberti 1989). The 2G technology offered a more attractive

package to buy, besides the traditional voice service, provided some data services and

more supplementary services. The 3G technology comes to market and is expected to

complete the globalization of mobile communication. The first pre-commercial 3G

network was launched by NTT DoCoMo in Japan branded FOMA, in May 2001 on a pre-

release of Wide Code Division Multiple Access (WCDMA) technology (Anon 2005). The

second network to go commercially live was by SK Telecom in South Korea on the

1xEV-DO technology in January 2002. By May 2002, the second South Korean 3G

network was by KT on EV-DO and thus the Koreans were the first to see competition

among 3G operators. In Europe, 3G networks were launches in Italy and the UK by the

Three/Hutchison group, based on WCDMA. In the mid 2000s, an evolution of 3G

technology was implemented, namely High-Speed Downlink Packet Access (HSDPA). It

is an enhanced 3G cellular technology communications protocol in the High-Speed

Packet Access (HSPA) family, also coined 3.5G, 3G+ or turbo 3G, which allows

networks based on Universal Mobile Telecommunications System (UMTS) to have

higher data transfer speeds and capacity. The 3G technology provided better quality,

faster connectivity and higher capacity at lower cost to consumers. The trend is that 3G

will mostly be based on GSM technical solutions for two reasons: GSM technology

dominates the market and the great investment made in GSM should be utilized as much

as possible (Kaaranen et al. 2005).

13

2.2.1 CELLULAR TECHNOLOGY EVOLUTION

There was an enormous variety of the First Generation (1G) systems that were

introduced. In this era, there were three dominating automatic systems for mobile

communications in the world: Advanced Mobile Phone Service (AMPS) in the US, Total

Access Communication System (TACS) in the UK and Ireland, and Nordic Mobile

Telephony (NMT) in Finland and Sweden. In the first generation, traffic was highly

unbalanced. Less than one third of calls were incoming calls, the remaining were

outgoing (Tabbane 1997).

In 1981, the first multinational cellular service has introduced in Europe when

the Nordic Mobile Telephone System or NMT450 began operating in Denmark, Sweden,

Finland, and Norway in the 450 MHz range (Nack 2003). Around this era, some

European countries have their own system and incompatible to others. Europeans quickly

realized the disadvantages of each European country operating on their own mobile

network. Later, a new plan to create a single European wide digital mobile service with

advanced features and easy roaming was started. The technology named as Global

Systems Mobile Telecommunications that later known as one of the 2G. The acronym

GSM had been changed from Groupe Spéciale Mobile. GSM was an all digital system

that started new from the beginning. By April of 1991 commercial service of the GSM

network had begun. Just a year and half later in 1993 there were already 36 GSM

networks in over 22 countries (Déchaux & Scheller 1993). It was a remarkable

technology step in many sense. First, it was the first technology that was properly

specified before implementation. Second, compared with analogue radio technologies,

GSM radio was designed in such a way to provide more capacity and features. In GSM

technology, roaming capability is introduced and offered which bounded by an agreement

between parties. Roaming is a general term referring to the extension of connectivity

service in a location that is different from the home location database where the service

was registered. Roaming ensures that the cellular device is kept connected to the network,

without losing the connection. This feature is not available in the first cellular generation.

14

The 2G cellular technologies can be divided into Time Division Multiple Access

(TDMA) and Code Division Multiple Access (CDMA) standards depending on the type

of multiplexing used. It allows slow data communications, but its primary focus is voice.

Among the 2G cellular technology, GSM which is TDMA-based has shown a very wide

acceptance in terms of world market penetration and subscriptions.

In 2G, the operators have evolved their systems to support the transmission of

data. Three different upgrade path have been developed for GSM carriers, and two of this

solutions also support IS-136. In US, TDMA standard with digital control channel is

termed as IS-136 (Ojanperä & Prasad 2001). Another previous standards are IS-54, IS-41,

and IS-95. The three TDMA upgrade options include: High Speed Circuit Switched Data

(HSCSD), General Packet Radio Service (GPRS), and Enhanced Data Rates for Global

Evolution (EDGE). These options provide significant improvements in Internet access

speed over today’s GSM and IS-136 technology. GPRS system is known as 2.5G and has

enabled operators to offer services in a more efficient form. This new technology makes it

possible for users to make telephone calls and transmit data at the same time.

Theoretically, GPRS terminals can provide up to 150-170 kbps data speeds downstream,

but realistically they only can serve with a maximum downstream speed of 50 kbps and

upstream 10-28 kbps. For EDGE technology increase the transmission rate up to 384

kbps. In some instances EDGE (2.75G) evolution systems may also be known as

Enhanced General Packet Radio Service (EGPRS) systems. The cellular technology

evolution is shown in Figure 2.1.

15

Figure 2.1 Cellular technology evolution

Source: Prasad & Ruggeiri 2003

A common, global mobile communication system naturally creates a lot of

political desires. In the case of 3G, this can be seen even in the naming policy of the

system. The most natural term is “third generation” (3G). In different parts of the world,

different issues are emphasized and, thus, the global term 3G has a regional synonym. In

Europe, 3G has become Universal Mobile Telecommunications System (UMTS). In

Japan and the US, the 3G system often carries the name IMT-2000. This name is a family

of standards for mobile telecommunications defined by the International

Telecommunication Union (Smith & Collins 2000). In the US, the CDMA2000 is also an

aspect of 3G cellular systems and represents the evolution from the IS-95 system. The

cellular technology and cellular standards evolution as shown in Figure 2.2.

16

Figure 2.2 Cellular standards evolution

Source: Ames & Gabor 2000

2.2.2 2G NETWORKS ARCHITECTURE

Global System for Mobile Communications technology has become by far the most

successful 2G standard. The architecture of 2G technology is similar to the 1G system.

The GSM network architecture is organized as a multi-tiered hierarchical structure and

can be divided into two main subsystems: Network Switching Subsystem (NSS) and Base

Station Subsystem (BSS) as shown in Figure 2.3. The main element in the NSS is the

Mobile Switching Center (MSC), which contains the Visitor Location Register (VLR),

Home Location Register (HLR), and Authentication Center (AuC). The NSS is the

component of a GSM system that carries out call switching and mobility management

functions for mobile phones roaming on the network of base stations. It is owned and

deployed by mobile phone operators and allows mobile devices to communicate with

each other and telephones in the wider Public Switched Telephone Network (PSTN). The

MSC represents the edge toward the BSS and on the other side as Gateway MSC

(GMSC), the connection point to all external networks, such as the PSTN or Integrated

Services Digital Network (ISDN). GSM is a circuit-switched network, which means that

there are two different types of physical links to transport control information (signaling)

and traffic data (circuit).

17

Figure 2.3 GSM network architecture

Source: Kreher & Rüdenbusch 2005

In GSM, it is distinguished between cells and location areas. A cell is defined as

the area in which one can communicate with a certain base station. In other words, the

cell is related to the Base Station (BS). Cell coverage is formed by an antenna structure,

but the traffic within a cell is maintained by transceiver. The minimum number of

transceiver in a cell is one and the maximum implementations is four to six tranceiver per

cell (Kaaranen et al. 2005). By using multiple cells, a single network can handle a large

amount of simultaneous users on an otherwise limited number of radio frequencies. Based

on cell coverage, cell can be divided into Pico, Micro, Macro and Mega cell. Cell area

types are depicted in Figure 2.4.

18

Figure 2.4 Cell coverage area types

Source: Akyildiz et al. 1998

2.2.3 3G NETWORKS ARCHITECTURE

The area of 2G will be continuously used in UMTS. A new group of locations specifying

the UTRAN Registration Areas (URAs) is configured in UMTS Terrestrial Radio Access

Network (UTRAN) as shown in Figure 2.5. These areas will be smaller Routing or LAs

and will be maintained by UTRAN itself. The different areas are used for mobility

management tasks such as Location Update and Paging procedures.

Figure 2.5 UMTS areas

Source: Kreher & Rüdenbusch 2005

19

The UMTS basically contains four logical definitions:

a. Location Area

A location area is a set of cells throughout which a mobile or User Equipment (UE)

will be paged. The LA consists of cells: the minimum is one cell and the maximum is

all the cells under one VLR. A LA is defined as the area associated with one VLR. On

networks where there is a one-one mapping between MSCs and VLRS, the LA

corresponds to the area controlled by one MSC. On a change of LA, the UE need to

perform a location update in order to register its presence in the new VLR and erase

its presence in the old VLR. In this case, the HLR also needs to be updated. If the UE

is engaged in communication, a handoff must be performed between the different

MSCs. Note that handoff between MSCs belonging to different network-providers is

impossible.

The LA is identified by the Location Area Identity (LAI) within an active area and

consists of Mobile Country Code (MCC), Mobile Network Code (MNC), and

Location Area Code (LAC). The MCC and MNC have the same format as in the IMSI

(International Mobile Subscriber Identity) number. The IMSI acts as a unique

database search key in the HLR, VLR, AuC and Serving GPRS Support Node

(SGSN) as depicted in Figure 2.6. When the MS is roaming outside the home

network, the visited serving network is able to recognize the home network by

requesting this unique number.

20

Figure 2.6 International mobile subscriber identity

Source: Kaaranen et al. 2005

MCC and MNC numbering use the ITU E.212 standard (ITU 2008). For example,

MCC and MNC of network operators are shown in Table 2.1.

Table 2.1 MCC, MNC, and network operators

Country

Code

MCC MNC Network Operator Country

60 502 12 Maxis Malaysia

60 502 16 Digi Malaysia

60 502 19 Celcom Malaysia

62 510 01 Indosat (Satelindo) Indonesia

62 510 10 Telkomsel Indonesia

62 510 11 Excelcom Indonesia

62 510 21 Indosat-M3 Indonesia

Source: ITU 2008

21

MSIN is the Mobile Subscriber Identity Number that consists of 9 to10 digits (this

number is stored in the USIM card). The LAC is just a number identifying a LA.

The LAI is a globally unique number and within the same network the same LAC

should clearly not be repeated as a single VLR or cannot handle duplicate LAC.

The UE listens to the LAI(s) from the Broadcast Channel (BCH). For example, in

Indonesia, Telkomsel’s subscriber, MCC will be 510, MNC will be 10, and MSIN

can be unique number of 10 bit long, like 8126900406. So the number will be

MCC+MNC+MSIN = 510108126900406. This IMSI number, 510108126900406

then corresponding E.214 address will be formed by replacing MCC (510) by

Indonesia Country Code (CC), (62) and replacing MNC, (10) with National

Destination Code (NDC), (812) and keeping MSIN as is (as long as it is less than

equal to 15 digits). This number follows the ITU-T recommendation E.164 (ITU

2010) and ITU-T recommendation E.214 on numbering (ITU 2005). The IMSI

and LAI structure are shown in Figure 2.7 and Figure 2.8.

Figure 2.7 Structure of international mobile subscriber identity

Source: ITU 2008

Figure 2.8 Structure of location area identity

Source: ITU 2008

22

b. Routing Area

One or more Routing Area (RA) is controlled by the SGSN. Each UE informs the

SGSN about the current RA. RAs can consist of on one or more cells. Each RA is

identified by a Routing Area Identity (RAI). The RAI is used for paging and

registration purposes and consists of LAC and Routing Area Code (RAC)

The main radio 3G technology employed in UMTS is WCDMA whose variants

Frequency Division Duplex (FDD) and Time Division Duplex (TDD) were selected by

the European Telecommunications Standard Institute (ETSI) in 1998. Although, just like

traditional CDMA, the spread spectrum forms the underlying technique for WCDMA but

employing a different control channel and signaling of 3G systems, it is significantly

different from its counterpart. 3G systems promise faster communications services,

including voice, fax and Internet, anytime and anywhere with seamless global roaming.

In 3G technology, the networks have been developed in many release versions

since 1999. Figure 2.9 shows the basic structure of a UMTS Release 99 network. It

consists of two different radio access parts BSS and UTRAN and the Core Network (CN)

parts for circuit-switched and packet-switched applications. Release 99 (sometimes also

named Release 3) specifies the basic requirements to roll out a 3G UMTS RAN. All the

following release introduces a number of features that allow operators to optimize their

networks and to offer new services.

23

Figure 2.9 UMTS release 99 network architecture

Source: Kreher & Rüdenbusch 2005

3GPP Release 4 introduces some major changes and new features in the CN

domains and the GPRS/EDGE Radio Access Network (GERAN), which replaces GSM

BSS, as shown in Figure 2.10. Some of the major changes are separation of transport

bearer and bearer control in the CS-CN and introduction of new interfaces in CS- CN.

The main trend in Release 4 is the separation of control and services of CS connections

and at the same time the conversation of the network to be completely IP-based. In CS

CN, the user data flow will go through Media Gateway (MGW), which are elements

maintaining the connection and performing switching functions when required.

24

Figure 2.10 UMTS release 4

Source: Kreher & Rüdenbusch 2005

In 3GPP Release 5, all traffic incoming from UTRAN is designed to be IP-based.

By changing GERAN, the BSC will be able to generate IP-based application packets. In

this released, all interfaces will be IP-based rather than ATM-based. The databases known

from GSM/GPRS will be centralized in a Home Subscriber Server (HSS). In UMTS

Release 6, major improvements are made such as UMTS/WLAN Internetworking, IMS

“phase 2”, Push-to-Talk service, Packet-Switched Streaming Services (PSS), Multimedia

Broadcast and Multicast Service (MBMS), Network Sharing, Presence Service, and the

definition of various other new multimedia services. The UMTS development, at this

moment has reached Release 10.

In 3G network, two new network elements are introduced in UTRAN: Radio

Network Controller (RNC) and Node B. UTRAN as shown in Figure 2.11 is subdivided

into individual Radio Network Subsystem (RNS), where an RNC controls each RNS. The

RNC is connected to a set of Node B elements, each of which can serve one or several

25

cells. RNC controls usage and reliability of radio resources. Existing GSM network

elements, such as MSC, HLR, and SGSN, can be extended to adopt UMTS requirements.

RNC will become the replacement for BSC, and Node B fulfills nearly the same

functionality as BTS.

Figure 2.11 UTRAN architecture

Source: Kreher & Rüdenbusch 2005

2.3 RADIO RESOURCE MANAGEMENT

Radio Resource Management (RRM) is a system level control of co-channel interference

and other radio transmission characteristics in wireless communication systems, for

example cellular networks, wireless networks and broadcasting systems. RRM involves

strategies and algorithms for controlling parameters such as transmit power, channel

allocation, handover criteria, modulation scheme, error coding scheme, etc. Static RRM

involves manual as well as computer aided fixed cell planning or radio network planning.

Static RRM schemes are used in many traditional wireless systems, for example 1G and

2G cellular systems. Dynamic RRM schemes adaptively adjust the radio network

parameters to the traffic load, user positions, quality of service requirements, etc.

26

Dynamic RRM schemes are considered in the design of wireless systems, in view to

minimize expensive manual cell planning and achieve "tighter" frequency reuse patterns,

resulting in improved system spectral efficiency. Some schemes are centralized, where

several base stations and access points are controlled by a RNC. Others are distributed,

either autonomous algorithms in mobile stations, base stations or wireless access points,

or coordinated by exchanging information among these stations.

Gelabert et al. (2005) stated that RRM functions can be implemented in many

different algorithms which impacting on the overall system efficiency and on the operator

infrastructure cost. Additionally, RRM strategies are not subject of standardization, so

that they can be a differentiation issue among manufacturers and operators. RRM

strategies of legacy networks (GSM/GPRS) are of rather low dimensionality, such as only

a few parameters are needed to tune their optimality. In the case of UTRAN, it ought to

be mandatory to increase and harmonies the general knowledge on WCDMA RRM

strategies as long as multiple dimensions appears in the problem.

2.3.1 Cellular Network Database

In 2G and 3G cellular network, two-level hierarchy of databases, HLR and VLR are used

to record user’s data as shown in Figure 2.12. A HLR acts as the primary database

repository for subscriber information used to provide control and intelligence. HLR

subscriber information includes the IMSI, service subscription information, location

information (the identity of the currently serving VLR to enable the routing of mobile-

terminated calls), service restrictions and supplementary services information. The HLR

handles Signaling System Number 7 (SS7) transactions with both MSCs and VLR nodes,

which either request information from the HLR or update the information contained

within the HLR. SS7 is an out-of-band signaling system for the exchange of call control

information between network switching offices, in support of voice and non voice

services. When a user subscribes to the service, a permanent record is created in HLR.

The number of the records in the HLR is the number of the subscribers in the system. The

HLR also initiates transactions with VLRs to complete incoming calls and to update

27

subscriber data. Traditional wireless network design is based on the utilization of a single

HLR for each wireless network, but growth considerations are prompting carriers to

consider multiple HLR topologies.

Figure 2.12 Two-level hierarchy of databases: HLR and VLR

Source: Bejerano 2000

A VLR is a database which contains temporary information concerning the

mobile subscribers that are currently located in a given MSC serving area, but whose

HLR is elsewhere. When a mobile subscriber roams away from his home location and

into a remote location, SS7 messages are used to obtain information about the subscriber

from the HLR, and to create a temporary record for the subscriber in the VLR. Normally,

the capacity of a VLR is much smaller than that of a HLR. For example, the capacity

of a typical VLR in Taiwan is around 250,000 to 500,000 records, and the typical

size of an HLR in Taiwan is around a million records (Lin 2001). The VLR may over

flow if too many mobile users move into the LA in some time periods.

28

2.3.2 Calling Process

Unlike routing in a fixed network, where a terminal is wired to a central office, in

wireless technology, GSM and 3G users can roam nationally and even internationally.

The directory number dialed to reach a mobile subscriber is called the Mobile Subscriber

ISDN (MSISDN), which is defined by the E.164 numbering plan (ITU 2010). This

number includes CC and NDC which identifies the subscriber's operator. The first few

digits of the remaining subscriber number may identify the subscriber's HLR within the

home PLMN (Public Land Mobile Network).

When a mobile subscriber roams into a new location area (new VLR), the VLR

automatically determines that it must update the HLR with the new location information,

which it does using an SS7 Location Update Request Message. Then Location Update

Message is routed to the HLR through the SS7 network, based on the global title

translation of the IMSI that is stored within the SCCP Called Party Address portion of the

message. Signaling Connection Control Part (SCCP) is a routing protocol in SS7 protocol

suite in layer 4. The HLR responds with a message that informs the VLR whether the

subscriber should be provided service in the new location. Having determined the

appropriate HLR address, the MSC sends a routing information request to it.

When the HLR receives the Routing Information Request, it maps the MSISDN to

the IMSI, and ascertains the subscribers' profile including the current VLR at which the

subscriber is registered. The HLR then queries the VLR for a Mobile Station Roaming

Number (MSRN). The MSRN is essentially an ISDN telephone number at which the

mobile subscriber can currently be reached. The MSRN is a temporary number that is

valid only for the duration of a single call. The HLR generates a response message, which

includes the MSRN, and sends it back across the SS7 network to the MSC. Finally, the

MSC attempts to complete the call using the MSRN provided. As shown in Figure 2.13,

the most general routing procedure begins with the GMSC (Gateway Mobile Switching

Center) querying the called subscriber's HLR for an MSRN.

29

Figure 2.13 Call routing for a mobile terminating call

Source: Anon 2006

2.3.3 Signaling Process

As the number of subscribers increases given a fixed radio spectrum allocation, in order

to accommodate the higher subscriber densities, more signaling consumes scarce radio

bandwidth. To achieve and minimize signaling activities, a set of procedures of mobility

tracking is performed which its main goal is to locate mobile user. In 3G network, if a UE

is switched on for the first time in a cell of the UMTS network it starts to perform the

following initial UE Radio Access procedure that can be described in four steps as shown

in Figure 2.14 (Kreher & Rüdenbusch 2005).

a. UE reads the Primary Synchronization Channel (PSCH), which is not scramble

and spread by a predefined spreading code. By reading this, the UE becomes time

synchronic with the Node B.

b. UE reads the Secondary Synchronization Channel (SSCH), which is also not

scrambled. The SSCH will transmit five hex values, which come out of a table. By

reading these values the UE will synchronize its frame to Node B and will get the

scrambling group of the actual used Node B.

30

c. UE can now read the Common Pilot Channel (CPICH), which is scrambled with

one of eight primary scrambling codes of the scrambling group. It is a matter of

trial and error to find the correct code.

d. UE will read the Common Control Physical Channel (CCPCH), which uses the

same scrambling code as the CPICH, to get detailed information about UTRAN

and the CN, to allow the Primary Common Control Physical Channel (P-CCPCH)

to transport the BCH, and to be able to get paged, and to allow the S-CCPCH to

transport PCH. The system information in the BCH will also indicate the

secondary scrambling code of the actual Node B for further data transmission on

the Dedicated Transport Channels (DCH).

Figure 2.14 Initial UE radio access

Source: Kaaranen et al. 2005

The transition to the UTRAN Connected Mode from the Idle Mode can only be

initiated by the UE by transmitting a request for an RRC connection. The event is

triggered either by a paging request from the network or by a request from higher layers

in the UE. When the UE receives a message from the network that confirms the RRC

connection establishment, the UE enters the CELL_FACH or CELL_DCH state of

31

UTRAN Connected mode. In case of a failure, to establish the RRC Connection, the UE

goes back to Idle Mode. The possible causes are radio link failure, a received reject

response from the network, or lack of response from the network (time out).

2.3.4 Connected Mode Status

The CELL_DCH state is shown in Figure 2.15 and charaterized by the following (Kreher

& Rüdenbusch. 2005):

a. A dedicated physical channel is allocated to the UE in uplink and downlink

b. Common/shared channels might be configured

c. The UE is known on cell level according to its current active set

d. Soft and Hard handover might be initiated

e. No cell update or URA update is initiated by the UE

f. The UE sends measurement reports to RNC according to the RNC setup

g. The UE can use DCH, downlink and uplink (TDD) shared Transport Channels

(TCH), and a combination of three transport channels.

Figure 2.15 UTRAN - connected mode states

Source: Kreher & Rüdenbusch 2005

32

The CELL_DCH state is entered from the Idle Mode through the setup of an RRC

connection, or by establishing a dedicated physical channel from the CELL_FACH state.

The CELL_FACH state is characterized by the following:

a. No dedicated physical channel is allocated to the UE

b. The UE continuously monitors a FACH in downlink

c. The UE assigned a default common or shared transport channel in the uplink (e.g.

RACH or CPCH) that it can use anytime according to the access procedure for

that transport channel

d. No Soft or Hard handover might be initiated

e. UTRAN knows the position of the UE on the cell level according to the cell where

the UE last made a cell update

f. The UE performs Cell Updates, but no URA updates

g. In TDD mode, one or several USCH or DSCH transport channels may have been

established

In the CELL_FACH sub-state, the UE performs the following actions:

a. Listen to all FACHs in the cell

b. Listen to the BCH transport channel of the serving cell for the decoding of system

information messages

c. Initiates a cell update procedure on cell change of another UTRA cell

d. Transmits uplink control signals and small data packets on the RACH

The CELL_PCH state is characterized by the following:

a. No dedicated physical channel is allocated to the UE

b. UE selects a PCH with an algorithm and uses DRX for monitoring the selected

PCH via associated PICH

c. DCCHs/DTCHs are configured but cannot be used

d. No Soft or Hard handover might be initiated

e. No uplink activity is possible (state change to Cell_FACH is needed)

f. The UE performs Cell Updates, but no URA updates

33

g. Position of the UE is known by UTRAN on the cell level according to the cell

where the UE last made a cell update in the CELL_FACH state

h. The UE sends Measurement Reports to RNC according to the RNC setup

In the CELL_PCH state the UE performs the following actions:

a. Monitor the paging occasions according to the DRX cycle and receive paging

information on the PCH

b. Listen to the BCH transport channel of the serving cell for the decoding of system

information messages

c. Initiates a cell update procedure on cell change

The DCCH logical channel cannot be used in this state. If the network wants to

initiate any activity, it needs to make a paging request on the PCCH logical channel in the

known cell to initiate any downlink activity.

The URA_PCH state is characterized by the following:

a. No dedicated channel is allocated to the UE

b. UE selects a PCH with an algorithm and uses DRX for monitoring the selected

PCH via an associated PICH

c. UE monitors Downlink PICH/PCH

d. No uplink activity is possible (state change to CELL_FACH is needed)

e. DCCHs/DTCHs are configured but cannot be used

f. Location of the UE is known on the URA level according to the URA assigned to

the UE during the last URA update in CELL_FACH state

In the URA_PCH state the UE performs the following actions:

a. Monitor the paging occasions according to the DRX cycle and receive paging

information on the PCH

b. Listen to the BCH transport channel of the serving cell for the decoding of system

information messages

c. Initiates a URA updating procedure on URA change

34

The DCCH logical channel cannot be used in this state. If the network wants to

initiate any activity, it needs to make a paging request on the PCCH logical channel

within the URA where the location of the UE is known. If the UE needs to transmit

anything to the network, it goes to the CELL_FACH state. The transition to URA_PCH

state can be controlled with an inactivity timer, and optionally, with a counter, which

counts the number of cell updates. When the number of cell updates has exceeded certain

limits (a network parameter), the UE will change to the URA_PCH state. URA updating

is initiated by the UE, which, upon the detection of the Registration Area, sends the

network the Registration Area update information on the RACH of the new cell.

In Figure 2.16 shows an example of RRC Signaling Connection that consists of

four main transactions:

a. The Initial Direct Transfer procedure is used in the uplink to establish signaling

connections and signaling flows. It is also used to carry the initial higher layer,

Non Access Stratum (NAS) messages over the radio interface. A signaling

connection comprises one or several signaling flows. This procedure requests the

establishment of a new flow, and triggers, depending on the routing and if no

signaling connection exists for the chosen route for the flow, the establishment of

a signaling connection.

b. The Downlink Direct Transfer procedure is used in the downlink direction to carry

higher layer (NAS) messages over the radio interface.

c. The Uplink Direct Transfer procedure is used in the uplink direction to carry all

subsequent higher layer (NAS) messages over the radio interface belonging to a

signaling flow.

d. The Signaling Connection Release request procedure is used by the UE to request

from the UTRAN that one of its signaling connections should be released. The

procedure may, in turn, initiate the signaling flow release or RRC connection

release procedure.

35

Figure 2.16 RRC signaling connection (message flow)

Source: Kreher & Rüdenbusch 2005

2.4 MOBILITY MANAGEMENT

Mobility Management (MM) is one of the major functions of a GSM or a UMTS network

that allows mobile phones to work. Mobility management is the process of keeping track

of and locating users so that calls arriving for them can be directed to their current

location (Brown & Mohan 1997). In GSM networks, MM is completely handled between

the MS and the Network Sub System (NSS). In UMTS networks, most MM functions are

handled equally between the UE and the CN (Kaaranen et al. 2005). The RNC partially

handles the UE’s movement within the RAN, using RRC procedures for this purpose. The

MM activities handled by the RNC are cell and URA updates.

The MM layer is built on top of the Radio Resource (RR) layer, and handles the

functions that arise from the mobility of the subscriber, as well as the authentication and

security aspects. Akyildiz et al. (1999) present a very comprehensive survey on all

aspects of mobility management. Fang & Ma (2004) have highlighted some schemes

related to mobility management such as IS-41 scheme, movement-based mobility

36

management, pointer forwarding scheme, two-level pointer forwarding scheme, two

location algorithm, location anchoring scheme, and location profile based scheme. Sun

and Sauvola (2002) discusses the effects of mobility on both the architectures and

protocols for network communications

Mobility is a change of position that does not entail a change of location or the act

of changing location from one place to another, for example, the movement of people

from the farms to the cities. In general, mobility capability is the main service feature in

modern mobile or cellular communication and can be classified into terminal and user

mobility. Evaluation studies involve the consideration of user mobility behavior;

therefore, the accuracy of the results heavily depends on the assumed mobility models.

Mobility modeling approaches in literatures can be divided into analytical models and

computer simulation studies. Reseachers use analytical model, based on simplifying

assumption, may provide useful conclusions regarding critical network dimensioning

parameters (Markoulidakis & Sykas 1995, Madhavapeddy 1994, Hong & Rappaport

1986) and more realistic analytical model studies indicate that closed form solutions can

be derived for simple cases only (for example highways at free flow) (Frullone et al.

1992, Seskar et al. 1992). Mobility modeling is involved in the analysis of

(Markoulidakis et al. 1997):

a. Aspects related to location management (such as location area planning and

paging strategies)

b. Aspects related to radio resource management (such as multiple access technique

and channel allocation schemes)

c. Aspects related to propagation (such as fading and handoff decisions)

2.4.1 Mobility Models in Cellular System

In mobile communication, mobility modeling is involved in several aspects related to

signaling and traffic analysis (Markoulidakis et al. 1997). Mobility models play a key role

in studying different mobility management features such as registration, paging, handoff,

and database approaches. A mobility model with minimum assumptions and simple to

37

analyze will be very useful under such circumstances (Chiang & Senoy 2004). Mobility

modeling is used in simulation attempt to portray the user mobility behavior and mobility

model should attempt to mimic the movement of real MSs. Choosing an appropriate

mobility model may not be a simple task. Bettstetter (2001) described a concept map

illustrating some criteria which can be for categorization mobility models as shown in

Figure 2.17. The shaded block, mobility model and border behavior model will be

implemented in this thesis simulation.

In mobility perspective, MS changes speed and direction. MSs are generated and

simulated not to travel in straight lines at constant speed throughout the course of the

entire simulation. In real situation, the direction of travel must change before reaching the

end destination. The speed of each interval must occasionally change and may even

decrease to zero. This chapter, only describe mobility models for cellular environment to

represent MSs movement.

Figure 2.17 A concept map of mobility models

Source: Bettstetter 2001

38

Mobility models are classified into two categories (Madany et al. 2009). The first

category is called entity mobility models, where all nodes in the system move

independently from each other. The second category is called group mobility models,

where sets of nodes move as groups. As noted in Hong et al. (1999), cellular mobility

models focus their attention on individual movements. Rarely do more complicated issues

such as group movement come into play. As a result, in cellular mobility models:

Random Walk Model, Constant Velocity Fluid-Flow Model, and Random Gauss-Markov

Model are very common to test the behavior of cellular strategies. Madany et al. (2009)

characterize mobility models and have reviewed as shown in Table 2.2. In attribute to be

added column show some possibility to improve the model.

Table 2.2 Mobility model matching real-world

Real-world deployment

Best model to be used

Attribute to be added

Airport Random Waypoint Long and short pause time

Speed and direction dependency

Boundary handling

Campus Trace-base model for Darmouth College

Long and short pause time

City Section City section model Long and short pause time Freeway Freeway model Conference room

Obstacle mobility model

Group Long and short pause time

Path

Source: Madany et al. 2009

2.4.2 Random Walk Mobility Model

The Random Walk Mobility Model (RWMM) has proven to be one of the most widely

used because it describes individual movements relative to cells (Bar-Noy et al. 1994,

Rubin & Choi 1997, Zonoozi & Dassanayake 1997). Many entities in nature move in

extremely unpredictable ways. Specifically, in this model, a MS moves from its current

location to a new location by randomly choosing a direction and speed in which to travel.

39

The RWMM as a memoryless mobility pattern has been described by Haas and Liang

(1999) because it retains no knowledge concerning its past location and speed values.

This characteristic inhibits the practicality of the RWMM because MSs typically have a

pre-defined destination and speed in mind, which in turn affects future destinations and

speeds. The new speed and direction are both chosen from pre-defined ranges, [speedmin,

speedmax] and [0, 2π] respectively. Each movement in random walk mobility model

occurs in a constant time interval t, at the end of which a new direction and speed are

calculated.

Many derivatives of the RWMM have been investigated including one-

dimensional, two-dimensional, three-dimensional, and d-dimensional random walks. In

1921, Polya proved that a random walk on a one or two-dimensional lattice returns to the

origin with a probability of 1.0 (Weisstein 2009). This characteristic ensures that the

random walk precisely represents a mobility model that test the movements of entities

around their starting points, without worry of the entities wandering away never to return.

Unfortunately, the simplicity of the RWMM is not always sufficient to produce realistic

results in our complex world.

In a 1-D RWMM, we imagine a gymnast standing in the middle of an infinitely

long balance beam. Given the results of a coin flip, the gymnast moves in a particular

direction at a random speed for time period, t. For example, if the coin flip results in head,

the gymnast moves to the right at the ramdomly chosen speed. In contrast, if the coin flip

results in tail, the gymnast moves to the left. After repeating this pattern for a large

number of times, a 1-D random walk is mapped.

In a 2-D RWMM, we visualize the same gymnast moving on a plannar surface.

For example, using a similar method as that mentioned in the 1-D random walk model,

we generate a 2-D model. Specifically, instead of visualizing a gymnast on a balance

beam we expand our environment to include an infinite floor mat. Instead of flipping a

coin, the gymnast uses a spinning ball. After spinning the dial, the gymnast moves in the

direction pointed to by the needle at a random speed for time t. In doing so, the gymnast

40

randomly moves around a 2-D surface thus creating a 2-D random walk. Figure 2.18

shows an example of the movement observed from a 2-D model. In special case of

RWMM, a MS no longer travels for a constant time period t before changing direction. A

modified 2-D model that MS changes direction after travelling a specified distance is

illustrated in Figure 2.18.

Figure 2.18 Random walk mobility model

2.4.3 Fluid Flow Mobility Model

According to Lam et al. (1997), Fluid Flow Mobility Model (FFMM) describes

macroscopic movements instead of individual or microscopic movements. The behavior

of the generated traffic is similar to fluid or water flowing through a pipe. As a result, the

FFMM best represent traffic on highways and other similar situations with a constant

flow of MSs. In other word, the model is unable to accurately represent the movements of

individual MSs. As an example, a deterministic FFMM is used in Leung et al. (1994) to

represent the behavioral characteristic of traffic on a one-way, semi-infinete highway.

Hać and Sheng (1996) studied the influence of user movement of database placement

using fluid flow mobility model. Cars enter dan exit the highway at various locations.

Haas and Liang (1999) confirms that the FFMM is insufficient for individual movement

including stopping and starting, action commonly associated with an individual walking

around town or from class to class.

41

2.4.4 Random Gauss-Markov Model

The Random Gauss-Markov Model (RGMM) is a model that uses one tuning

parameter to vary the degree of randomness in the mobility pattern (Madany et al.

2009). RGMM was introduced in order to circumvent the unsiderable results as

mentioned in RWMM and FFMM. Liang and Haas (2003) stated that Gauss–Markov

model represents a wide range of user mobility patterns, including, the random-walk and

the constant velocity fluid-flow models. Gauss–Markov model captures the essence of the

correlation of a mobile’s velocity in time. Li, et al. (2006) propose a Gauss–Markov

process based fluid model that it is suitable for both vehicle traffic on highways and

pedestrian in street. In the RGMM, the velocity of a MS at time is given by the equation:

�� � �. ���� � �1 � � � √1 � ��. ���� (2.1)

where a is the tuning parameter used to vary the randomness, µ is a constant representing

the mean value of νn as n → ∞, and χn-1 is a random variable from a Gaussian distribution.

Totally random values are obtained by setting a = 0 and linear motion is obtained by

setting a = 1. Intermediate levels of randomness may be obtained by varying the value of

a between 0 and 1 (Tolety 1999). Further, the displacement of a MS is given by the

equation � � ∑ �����

��� . By allowing past velocities and directions to influence future

velocities and directions, the RGMM eliminates the problems encountered in the RWMM

and also in the FFMM.

2.4.5 Random Direction Mobility Model

The Random Direction Mobility Model (RDMM) was created in order to overcome a

flaw discovered in the Random Waypoint Mobility Model (Royer, et al. 2001). MSs using

Random Waypoint Mobility Model (RWyMM) often choose new destination and the

probability of choosing destination that is located in the center of the simulation area or

requires travel through the middle of the simulation area is high. In RDMM, MSs choose

42

a random direction in which to travel instead of a random destination. After choosing a

random direction, a MS travels to the border of the simulation area. As soon as the

boundary is reached the MS stops for a certain period of time, choose another angular

direction (between 0 to 180 degrees) and continues to travel.

Royer et al. (2001) proposed Modified Random Direction Mobility Model

(MRDMM) which is a slight modification to the RDMM. In this modified version, MSs

continue to choose random directions but they are no longer forced to travel to the

simulation boundary before stopping to change direction. Instead, a MS chooses a random

direction and selects a destination anywhere along that direction of travel.

2.4.6 City area, Zone area and Street Unit Mobility Model

Markoulidakis et al. (1997) take an in-depth look at desirable characteristics of mobility

models including required inputs/outputs and issues that should be considered when

designing a specific mobility model. The represent a basic mobility model with a set of

input parameters, Sin and a set of output parameters, Sout. Sin includes a population, P,

which represent specific groups of MSs, a geographical area, G organized into regions,

and a time period, T. Sout includes a collection of functions that determine the location of

a MS, p over the set G at time t. By combining these elements with transportation theory,

three models were created and defined as: the city area, area zone, and street unit models.

Transportation theory works to determine the load a system should carry given a

geographical area of service. In order to calculate a given load, many different variables

are considered (Markoulidakis et al. 1997):

a. The purpose of a trip

b. The exact route taken including starting and ending points

c. Population groups such as student and working people

d. Period of users activities

e. A transportation system’s capacity and usage costs, and

f. Popular area attracting

43

The city area model is represented of user mobility and traffic behavior within a

large-scale geographical area. A typical city area model possesses two key characteristics

according to transportation theory. First, cities usually develop in such a way that the

center of the city comprises a high concentration of work places and businesses.

Surrounding the center of the city is a fairly dense distribution of dwelling areas for the

people of the city, which commonly referred to as urban areas. As we move away from

the center of the city, we see gradual decrease in population density, thus representing

suburban and rural areas. The second key characteristic found in a typical city is a street

network that supports movements from center of the city through urban area and then into

the suburban and rural areas. Obviously, the focus in the city area model is to represent

large-scale flows of traffic within city limits.

The area zone model takes a slightly more redefined looking at mobility within a

city. Instead of looking at the entire city, the area zone model divides the city into

regions. This process is done using square-shaped building blocks and an orthogonal grid

representing a street network. Again, this model proves most useful for large-scale

interactions.

Finally, the street unit model attempts to model movements of individual MSs.

The authors (Markoulidakis et al. 1997) attempt to simulate realistic traffic conditions by

minimizing the travelling time for all MSs and implementing safe driving characteristics

such as a speed limit and a minimum distance allowed between any two MSs.

The city area, area zone, and street unit model lack specific details such as

calculations for the movements of MSs because of their theoritical models used to

describe simulation environments. Unfortunately, if obstacles and defined travel paths are

added to make these models more realistic, the high level of accuracy introduce an

overwhelming amount of computational effort and complexity if the mobility models are

simulated.

44

2.5 LOCATION MANAGEMENT

Location management is concerned with the procedures that enable the system to know

the current location of a powered-on mobile station so that incoming call routing can be

completed. Current techniques for location management involve database architecture

design and the transmission of signaling message between various components of a

signaling network (Akyildiz 1999). Xie et al. (1993) used a general cost function for

location update and paging based on call arrival rate and the location updating rate. The

evaluation of the algorithm uses different call arrival probability functions and plots

normalized cost functions comparing the proposed scheme with a fixed scheme. An

analytical evaluation of Tabbane’s proposal (1995) is given which assumes typical value

for certain parameters, such as cell size, average user velocity, and average number of call

arrivals and call originations. The mobility model used in the simulation is a simple one,

with a user moving with an average velocity and random direction, having a certain

probability of remaining in a certain paging area. The Mobility Predictability Level

(MPL) is a key parameter used in the comparisons to give an estimate of the randomness

of the mobility patterns. Seskar et al. (1992) proposed a traffic model which stimulates

vehicle movement based on the relationship between vehicle speed, vehicle density per

street length, and volume of vehicles.

Okasaka et al. (1991) proposed a two-layer modification at the VLR level of the

VLR/HLR architecture in PCS networks. The two layers of Registration Areas (RA)

overlap such that the borders of the RAs in one layer are covered by RAs in the other

layer. This technique effectively reduces the location updates caused by user

“oscillations” at the RA borders. Assouma et al. (2006) analyzed a new procedure for

intersystem registration, updating, and paging processes. Two-tier registration database:

Home Location Register (HLR) and Visitor Location Register (VLR) is used in this

evaluation. For 3G system, Xiao et al. (2004) proposed four parameters: the HLR location

update cost, the GLR location update cost, the VLR location update cost, and the paging

cost, used for cost calculation of location management. GLR is Gateway Location

Register. In this concept, the service area is partitioned into Gateway Location Areas

45

(GLAs) and then partitioned into Location Areas (LAs). An HLR location update is

performed when an UE crosses a boundary of a GLA; a GLR location update is

performed when an UE crosses a boundary of a LA. A VLR location update will happen

if a UE across multiple cells and exceeds a predetermined threshold value. An HLR

location update involves both a GLR location update and a VLR location update, and a

GLR location update involves a VLR location update.

CTotal = CHLR + CGLR + CVLR + CPaging (2.2)

Where, CTotal, CHLR, CGLR, CVLR, CPaging denotes the total cost, the HLR cost, the GLR

cost, the VLR cost, and the paging cost. If the value of CGLR is omitted, the formula will

be minimized. This can be achieved if no GLR is implemented. To evaluate the

calculation, the total cost is the sum of the LU cost (CLU), paging cost (CPaging), and cell

cost (CCell) as follows:

CTotal = CLU + CPaging + CCell (2.3)

2.6 LOCATION UPDATE

In daily life, many MSs travel and follow certain path or road. For example, a person

drives to his/her office every morning along a road, stays in the office most of the day,

and goes home after working along the same road; a mailman delivers mail along fixed

routes every day. If the network knows the mobile users’ daily route information, then the

location update signaling traffic burden can be mitigated.

In cellular networks, MS within a cell is tranparent to the netwok, and hence

location tracking is only required when the MS moves from previous cell to a new cell.

Before a MS gain access to services, the user has to register with the mobile network. To

validate the registration, the system will check the user identity and subscription status.

Registration is only required if there is a change of networks and therefore, a VLR of

current network has not yet issued a Temporary Mobile Subscriber Identity (TMSI) to the

46

user. This means that the user has to report to network using his IMSI and receives a new

TMSI by executing a location registration procedure. Although location registration and

location update have a different procedure, both of these database register mechanisms

are closely interrelated.

The location updating procedures, and subsequent call routing, use the MSC and

two location registers: HLR and VLR. Location updates are not usually sent every time a

MS enters a new cell, but depend on a predefined strategy. The location updating

procedures is executed if the user recognizes that it is in a new location area which leads

to updating the location information in the HLR record. Location update is initiated by

MS when it reports its current location to the mobile network. A procedure related to

location updating is the IMSI attach and detach. A detach lets the network know that the

mobile station is unreachable, and avoids having to needlessly allocate channels and send

paging messages. An attach is similar to a location update, and informs the system that

the mobile is reachable again. An example of location update diagram in GSM is shown

in Figure 2.19.

Figure 2.19 Location update diagram in GSM

47

LU algorithms can be divided into two main groups: static and dynamic. In static

scheme, LA boundaries are fixed; it can be zone-based (Saraydar 2000) or profile-based

(Tabbane 1995). The profile-based strategy proposed by Tabbane tries to reduce the

location tracking cost by taking advantage of most mobiles’ highly predictable patterns.

Although the performance of this strategy is much better than the fixed paging area

strategy currently adopted by most network operators, several important parameters such

as the time-varying probabilities and the approach to partition each MS moving period are

innocent. In dynamic scheme, the size of the LAs for user is not fixed but is optimized

according to its current arrival rate (Xie et al. 1993). In a static algorithm, LU is triggered

based on the topology of the network. Examples include the conventional LA based

scheme used in GSM systems. Akyildiz (1999) showed that static schemes have the

disadvantage that they cannot be adjusted according to the parameter of individual user.

For example, under the LA-based LU scheme, the LA size most suitable for one user may

be ineffective for another user. In a dynamic algorithm, LU is based on the user’s call and

mobility patterns. Some schemes that have investigated in recent studies are the distance-

based (Madhow et al. 1995, Ho & Akyildiz 1995), timer-based (Rose & Yates 1995), the

movement-based schemes (Akyildiz et al. 1996), and the activity-based (Scourias &

Kuhn 1999). Wong (2001) mentioned that the evaluations of various LU algorithms have

been proposed in the literatures are often performed under certain unrealistic assumptions.

In the following section, a survey of several location update schemes recorded in

literatures is described.

2.6.1 Registration Area Location Update Schemes

In location area registration method (Lo et al. 1994a & Lo et al. 1994b), the collection of

all cells in the system is partitioned into a number of disjoint location area. Each cell in a

LA broadcasts the location area ID to inform all MSs in which location area they reside

in. A MS is registered whenever it crosses the boundary of two LA and the location

management enables the network to track the location of user and its terminal, during a

call arrival. The network has to maintain the approximate location of each user. When a

connection needs to be established for a particular user, the network determines the

48

location of the mobile terminal, within the cell granularity. The network has two

operations for the current location of a MS: LU and paging. MS updates its LU whenever

it crosses a cell boundary, the network can maintain its precise location thus obviating the

need for paging. However, if the call arrival rate is low, the network wastes its resources

by processing frequent LU information and the MS waste its power transmitting the LU

signal. If the MS does not perform LU frequently, a large coverage area has to be paged

when a call arrives which wastes radio bandwidth. Thus the central problem of location

management is to find a minimum cost for overall LU and paging cost.

To maintain the location of the user, there are two strategies in LU: Static and

Dynamic. In static strategy, the network decides when and where the MS should report to

the network of its location. In dynamic strategy, the MS informs the network of its

location when and where it could be. The MS transmits update messages according to

their movement and not in predetermined cell. Dynamic strategies are time-based,

movement based and distance based.

2.6.2 Movement-based Scheme

In movement-based scheme, each MS counts the number of boundary crossings between

cells incurred by its movements. This scheme allows the dynamic selections of the

movement threshold on a per user basis. For implementation, the MS only needs a

counter to count the number of cell boundary crossing. The counter is reset if it reaches

the movement threshold. As shown in Figure 2.20, an example of a movement threshold

of 3 is used. In path B to C, MS has moves three times and this will be count by the

system. An analytical model was introduced by Akyildiz et al. (1996) to determine the

optimal movement threshold. The model applicable for mesh and hexagonal cell

configuration under the assumptions of a general cell residence time distribution and

symmetric random walk movement pattern. In 3G network, three level hierarchical

mobility database is used to study location update using movement base scheme (Ali et

al. 2007).

49

Figure. 2.20 Movement-based

Liang and Hass (1999) proposed to use and identify the cell named as the Cell

Identification Code (CIC). With CIC, each cell is assigned a code, which is not necessarily

unique. The code is used to identify the cell’s orientation relative to cells within the same

location area and periodically broadcasts its identification codes through the downlink

control channel. The MS uses this information to facilitate the update decision.

2.6.3 Timer-based Scheme

This scheme does not require a MS to record or process location information during the

time between updates. For implementation, the timer threshold can be programmed into

the MS by a hard or software timer. Bar-Noy et al. (1995) proposed a timer-based scheme

whereas each MS updates its location every T time units (such as T=1 hour). A MS

performs location updates periodically at a constant time interval. Then, a variation of

time based scheme called the adaptive threshold scheme has been proposed (Pollini &

Chih-Li 1997). Here, the MS transmits the update message every T time units. This

threshold is not constant but varies with the current signaling load on the uplink control

channel of the base station. Numerical results, under the assumptions of one directional

linear model and random walk mobility pattern shows that the adaptive threshold scheme

has better performance that the static timer based scheme. Another approach in analytical

model has been introduced in Rose (1996) to study the timer-based scheme. The timer-

50

based scheme is shown in Figure 2.21. As mentioned before in previous section, here, in

path B to C, MS has moves and this will be count by the system if the total threshold time

is reached or exceeded the threshold limit.

Figure 2.21 Timer-based

2.6.4 Distance-based Scheme

In this scheme, each MS performs a location update when its distance from the cell where

it performed the last location update exceeds a predefined value (distance threshold).

When the MS moves from cell to another cell, the MS needs to download a set of cell IDs

after each location update. In Bar-Noy et al. (1995), the authors compared the movement,

time and distance based schemes under the assumptions of the random walk mobility

movement and a ring topology of cells. The analytical result shows that the distance

based scheme gives the lowest management cost (Bhattacharya & Das 1999). Senzaki and

Chakraborty (2008) evaluates a combination of distance-based with selective paging.

Zhao et al. (2009) investigate the impact of call arrivals and the initial position of the MS

on the position of the LA. As shown in Figure 2.22, a MS travels from A to B and the

total distance can be longer than a direct route or a straight line from A to B. As it is not

limit the distance threshold, the system will discard its activity and no LU process is

recorded.

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Figure 2.22 Distance-based

2.6.5 Adaptive Direction-based Scheme

In this scheme (Ou et al. 2002), it is assumed that the movement of a MS can be divided

into steps and each step has a destination. A Gauss-Markov process is used to model the

movement of a MS in each step. The MS inspects its direction periodically and a location

update will be generated when its direction change is greater than the direction threshold

defined for the step. It is further assumed that the mobility pattern of a mobile terminal

may change with time and its mobility pattern over a long period of time can be divided

into a sequence of steps. The length of each step can be different and each step has a

destination such as shown in Fig. 2.23.

(a) (b)

Figure 2.23 Movement and timing

(a) MS in service area, (b) Timing diagram

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2.7 PAGING SCHEME

Paging is a mechanism to locate MS as a target when the network need to deliver a call.

In the current location areas scheme, to locate a mobile terminal within a location area, all

the cells within the location area are paged simultaneously. The paging cost will be the

maximum, and it is in proportion to the number of cells in the location area. If the paging

delay is not constrained, the cells in the location can be paged sequentially until the

mobile terminal is found. This will greatly reduce the number of cells to be paged,

thereby reducing the paging cost.

The paging delay is an important QOS (Quality of Service) metric in location

management (Senzaki & Chakraborty 2008). The paging delay cannot be arbitrarily large.

If the paging delay is large, the caller may perceive the delay. In addition, a mobile

terminal may move out of the current cell or even the current location area during the

paging process. In general, there is a trade-off between the paging cost and the paging

delay. If all the cells have to be paged simultaneously, the paging cost reaches the

maximum, whereas the paging delay is the minimum. On the other hand, if there is no

constraint on the paging delay, the cells can be paged sequentially in order of decreasing

probability, which leads to the minimal paging cost. Therefore, many researchers

proposed selective paging schemes to minimize the paging cost under an acceptable delay

constraint. Paging schemes can be grouped into two major types (Bar-Noy & Mansour

2004): delay bound and non-delay bound. Delay bound strategies are further classified as

blanket polling and sequential group paging. Non-delay constrainted strategies are

sequential and the shortest distance first paging.

2.7.1 Blanket Polling Paging Scheme

Existing systems use the blanket polling scheme, in which, when an incoming call arrives,

all cells in an location area are paged. In other words, the paging area is the same as the

location area. Such a scheme wastes significant bandwidth. Advantages of this scheme are

easy to use and reasonably fast (Xiao et al. 2007). This scheme is deployed on top of the

53

location area based update scheme. The drawback of this scheme is if the number of LAs

are large, then the paging cost becomes correspondingly high (Wong & Leung 2000).

2.7.2 Shortest Distance First Paging Scheme

In this paging scheme, the network pages the MS starting from the cell where the MS last

updated its location, and moving toward in a shortest distance first order (Akyildiz et al.

1996). The distance is measured in terms of the number of cells away from the last update

location. If a threshold based update scheme (e.g., distance or movement) is used, the

paging or residing area of the MS is bounded. The MS can be located within a fixed

number of polling cycles. Grouping cells at different distances can incorporate the paging

delay constraint for each polling cycle.

2.7.3 Sequential Paging Scheme

In sequential paging, a location area is divided into smaller areas called a paging area and

the group of cells in a paging area is searched in one polling cycle. A polling cycle in a

sequential paging scheme is defined by the round trip time from the time when a paging

message is transmitted to the time when the response is received. Sequential paging

scheme is efficient to reduce paging load, but it may increase paging delay exponentially

(Lee et al. 2004). In this paging scheme, the current location of the MS is predicted based

on its location probability distribution. Polling signals are sent only to those cells in

which the user is likely to be present. An intuitive result derived by Rose & Yates (1995)

state that given the probability distribution on user location under no paging delay

constraint, the paging cost is minimized by sequentially polling the cells in decreasing

order of probability. Clearly, uniform location distribution gives the highest paging cost

and delay. When there is a maximum paging delay constraint, a group of cells can be

polled together in each polling cycle. Rose & Yates (1995) obtained that the optimal

paging sequence resulting in minimum paging cost with average paging delay constraint.

To determine the optimal group size to minimize paging cost can be used dynamic

programming (Putterman 1994).

54

2.7.4 Selective Paging Scheme

In the selective paging scheme, its process consists of some iteration steps (Wan & Lin

1998). In each step, a subset of the cells is selected for paging according to a

predetermined selection criterion (such as distance). Casares-Giner and Garcia Escalle

(2009) applied aselective polling paging strategy based on the expected trajectory of MS.

A dynamic selective paging strategy was introduced into the location areas scheme in

(Abutaleb & Li 1997). The goal is also to minimize the paging cost, subject to a

constraint on the paging delay. The paging process terminates as soon as the MS is found

(Akyildiz et al. 1996). The paging delay is the major problem with this scheme.

2.8 APPLICATION OF OPTIMIZATION TECHNIQUES

In this section, graph theory is described and used in this thesis to evaluate the mobility

behavior. The rest, some application of optimization techniques are given to overview the

selected mechanism that can be used to solve the problems in location management. Only

fuzzy logic, genetic algorithm and neural network are explored and then evaluate. The

selected technique will be used in the next chapter.

2.8.1 Graph Theory

Pattern recognition is concerned with the classification of patterns into categories. This

field of study was devel oped in the early 1960s, and it plays an important role in many

engineering fields, such as medical diagnosis, computer vision, character recognition,

data mining, communication. There are two main categories of classification methods

(Leski 2004): supervised (discrimination) and unsupervised (clustering) ones. In

supervised classification, a set of data, called the training set, with class labels associated

with each datum. In Okasaka et al. (1991) proposal, the subscribers are grouped with

similar behavior. An approach using graph called the Individual Profile Graph (IPG) used

by Chuon et al. (2005) to evaluate location update and paging.

55

Graph theory has a variety of applications which use a node for a vertex and a link

for an edge to fit the common terminology. A graph G with n vertices and m edges

consists of the vertex set V(G)={v1, v2, ..., vn} and edge set E(G)= {e1, e2, ..., em}, where

edge consists of two (possibly equal) vertices called endpoints. An element in V(G) is

called a vertex of G and an element in E(G) is called an edge of G. A simple graph is a

graph having no loops or multiple edges. For a graph G = (V,E), the underlying simple

graph UG is the simple graph with vertex V and (x, y) ∈E(UG) if and only if x ≠ y and

(x, y) ∈E. A graph is finite if its vertex set and edge set are finite.

Given a graph or digraph G with vertices indexes as V(G)={v1, v2, ..., vn}, the

adjacent matrix of G, written A(G), is the matrix in which entry aij is the number of copies

of the edges (vi, vj) in G. If vertex v belongs to edge e, then v and e are incident. The

incidence matrix M(G) of a loopless graph G has rows indexed by V(G) and columns

indexed by E(G), with mij = 1 if vertex vi belongs to ej; otherwise mij = 0. For a loopless

digraph, mij = +1 if vi is the tail of ej, mij = -1 if vi is the head of ej, and mij = 0 if

otherwise. An example is shown in Figure 2.24.

Figure 2.24 Graph and value

An isomorphism from G to H is a bijection f: V(G) → V(H) such that (u, v) ∈

E(G) if and only if (f(u), f(v)) ∈E(H). We say “ G is isomorphic to H,” written G≅ H, if

there is an isomorphism from G to H. When G is isomorphic to H and H is also

isomorphic to G, we may say G and H are isomorphic (to each other). Because an

adjancency matrix encodes the adjacency relation, isomorphism can be described using

w x y z

w 0 1 0 0

x 1 0 1 0

y 0 1 0 1

z 0 0 1 0

56

adjacency matrices. The graph G and H are isomorphic if and only if we can apply

permutation to the rows of A(G) and the same permutation to the columns of A(G) to

obtain A(H). From the Figure 2.24, it can be transformed to make a new matrix as shown

in Figure 2.25.

Figure 2.25 Graph matrices

The graph G and H drawn in Figure 2.26 are 4-vertex paths. Define the function f: V(G)

→ V(H) by f(w)=a, f(x)=d, f(y)=b, f(z)=c. To show that f is an isomorphism, we check

that f preserves edges and non-edges. Note that rewriting A(G) by placing the rows in the

order w, y, z, x and the columns also in that order yields A(H), as illustrated in Figure

2.26; this verifies that f is an isomorphism. Another isomorphism maps w, x, y, z to c, b, d,

a, respectively.

y zw x w y z x a b c d

w

x

y

z

0 1 0 0

1 0 1 0

0 1 0 1

0 0 1 0

w

y

z

x

0 0 0 1

0 0 1 1

0 1 0 0

1 1 0 0

a

b

c

d

0 0 0 1

0 0 1 1

0 1 0 0

1 1 0 0

Figure 2.26 Isomophism graph and matrices

w x y z

w 0 1 0 0

x 1 0 1 0

y 0 1 0 1

z 0 0 1 0

a b c

w 0 0 1

x 0 1 1

y 1 1 0

z 1 0 0

57

2.8.2 Fuzzy Logic

Fuzzy logic is an approach to computer science that mimics the way a human brain thinks

and solves problems. The idea of fuzzy logic is to approximate human decision making

using natural language terms instead of quantitative terms. It is formally defined as a form

of knowledge representation suitable for notions that cannot be defined precisely, but

which depend upon their contexts. It enables computerized devices to reason more like

humans (Bih 2006). Fuzzy logic is a superset of conventional (Boolean) logic that has

been extended to handle the concept of partial truth - truth values between "completely

true" and "completely false". Fuzzy logic is a form of many-valued logic; it deals with

reasoning that is fixed or approximate rather than fixed and exact. It was introduced by

Dr. Lotfi Zadeh of UC/Berkeley in the 1960's as a means to model the uncertainty of

natural language (Cirstea 2002). A basic simple fuzzy control system is simply

characterized. It accepts numbers as input, then translates the input numbers into

linguistic terms such as Slow, Medium, and Fast (fuzzification). Fuzzification is the

process of changing a real scalar value into a fuzzy value. Rules then map the input

linguistic terms onto similar linguistic terms describing the output. Finally, the output

linguistic terms are translated into an output number (defuzzification). Defuzzification is

the process of producing a quantifiable result in fuzzy logic, given fuzzy sets and

corresponding membership degrees.

A review of some works that have been done by some authors in using Fuzzy

logic is presented in this section. Astrain and Villadangos (2004) used fuzzy technique to

encode MS movement. MS trajectories are stored in the terminal as a dictionary and then

used to measure the signal power in order to obtain the fuzzy symbol. The calculation will

find the similarity between the string containing the path followed and the possible paths

contained in the hybrid dictionary. This works measured the QoS and a number of

interruption and blocking probabilities were presented. In more comprehensive paper,

Astrain et al. (2004) showed the trajectories using different cell scenarios using fuzzy.

Rea and Pesch (2005) investigated the effect of selecting mobility model on protocol

performance. A Fuzzy distance-based location management scheme proposed by Zhu and

58

Leung (2006b) to dynamically adjust the distance threshold. A fuzzy approach to change

the update period based on time-based scheme is performed by Ryu et al. (1999). In this

works, twelve if-then rules is accomplised. Wang and Chen (2006) investigated the QoS

performance for 4G heterogeneous networks. The proposed scheme employed to evaluate

the traffic problems. In the following part of this section, fuzzy logic theory is presented.

a. Fuzzy Set Theory

Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with

reasoning that is approximate rather than precise. Fuzzy sets were for a long time not

accepted by the Artificial Intelligent (AI) community. Now they have become highly

evolved and their techniques are well established. Fuzzy sets are sets whose elements

have degrees of membership that introduced by Lotfi A. Zadeh at the University of

California in 1965 (Somasundaram & Beaula 2009). In classical set theory, the

membership of elements in a set is assessed in binary terms according to a bivalent

condition - an element either belongs or does not belong to the set. By contrast, fuzzy set

theory permits the gradual assessment of the membership of elements in a set; this is

described with the aid of a membership function valued in the real unit interval [0, 1].

Fuzzy sets generalize classical sets, since the indicator functions of classical sets are

special cases of the membership functions of fuzzy sets, if the latter only take values 0 or

1 (Dubois & Prade 1988). Classical bivalent sets are usually called crisp sets.

Aziz and Parthiban (1996) described a comprehensive of fuzzy set theory.

Bivalent set theory can be somewhat limiting if we wish to describe a 'humanistic'

problem mathematically. For example the use of transistors instead of vacuum tubes is a

paradigm shift - likewise the development of fuzzy set theory from conventional bivalent

set theory is a paradigm shift. A paradigm is a set of rules and regulations which defines

boundaries and tells us what to do to be successful in solving problems within these

boundaries. For example, Figure 2.27 illustrates bivalent sets to characterize the

temperature of a room.

59

Figure 2.27 Fuzzy set to characterize the room temperature

Source: Aziz and Parthiban 1996

b. Fuzzy Set Operations

The basic connective operations in classical set theory are those of intersection, union and

complement. These operations on characteristic functions can be generalized to fuzzy sets

in more than one way. However, one particular generalization, which results in operations

that are usually referred to us as standard fuzzy set operations, has a special significance

in fuzzy set theory. The following operations can be defined:

1. Union

The Union operation in Fuzzy set theory is the equivalent of the OR operation in

Boolean algebra. The fuzzy intersection operator � (fuzzy OR connective) applied

to two fuzzy sets A and B with the membership functions ��� and

�� is

��� � �����

��, ���, � � � (2.4)

The membership function of the Union of two fuzzy sets A and B with

membership functions ��� and

�� respectively is defined as the maximum

of the two individual membership functions as shown in Figure 2.28.

60

Figure 2.28 Union operation

Source: Aziz and Parthiban 1996

2. Intersection

The Intersection operation in Fuzzy set theory is the equivalent of the AND

operation in Boolean algebra. The fuzzy intersection operator � (fuzzy AND

connective) applied to two fuzzy sets A and B with the membership functions

��� and

�� is

���� � �in����, ���, � � � (2.5) The membership function of the Intersection of two fuzzy sets A and B with

membership functions ��� and �� respectively is defined as the minimum of

the two individual membership functions as shown in Figure 2.29.

61

Figure 2.29 Intersection operation

Source: Aziz and Parthiban 1996

3. Complement

The Complement operation in Fuzzy set theory is the equivalent of the NOT

operation in Boolean algebra. The membership function of the Complement of a

Fuzzy set A with membership function ���is defined as the negation of the

specified membership function as shown in Figure 2.30. This is called the

negation criterion.

�� �� � � � ���, � � � (2.6)

Figure 2.30 Complement operation

Source: Aziz and Parthiban 1996

62

Defuzzification is the process of producing a quantifiable result in fuzzy logic,

given fuzzy sets and corresponding membership degrees. It is an important operation in

fuzzy sets theory typically needed in fuzzy control systems. It transforms fuzzy set

information into numeric data information. This will has a number of rules that transform

a number of variables into a fuzzy result, that is, the result is described in terms of

membership in fuzzy sets. For example, rules designed to decide how much pressure to

apply might result in "Decrease Pressure (15%), Maintain Pressure (34%), and Increase

Pressure (72%)". Defuzzification is interpreting the membership degrees of the fuzzy sets

into a specific decision or real value. Jiang and Li (1996) stated that defuzzification is

realized by a decision-making algorithm that selects the best crisp value based on a fuzzy

set. Zadeh first noticed the shortness of systematic defuzzification procedures (Zadeh

1968). There are three defuzzification strategies: center of area or center of gravity, mean

of maximum, and max criterion.

A common and useful defuzzification technique is center of gravity. First, the

results of the rules must be added together in some way. The most typical fuzzy set

membership function has the graph of a triangle. Now, if this triangle were to be cut in a

straight horizontal line somewhere between the top and the bottom, and the top portion

were to be removed, the remaining portion forms a trapezoid. The first step of

defuzzification typically "chops off" parts of the graphs to form trapezoids (or other

shapes if the initial shapes were not triangles). For example, if the output has "Decrease

Pressure (15%)", then this triangle will be cut 15% the way up from the bottom. In the

most common technique, all of these trapezoids are then superimposed one upon another,

forming a single geometric shape. Then, the centroid of this shape, called the fuzzy

centroid, is calculated. The x coordinate of the centroid is the defuzzified value.

c. Fuzzy Expert System

A fuzzy expert system is an expert system that uses fuzzy logic instead of Boolean logic

(Horstkotte 2000). Expert systems are computer programs, designed to make available

some of the skills of an expert to non experts. There are two general types of fuzzy expert

systems: fuzzy control and fuzzy reasoning (Siler & Buckley 2005). Although both make

63

use of fuzzy sets, they differ qualitatively in methodology. Fuzzy process control was first

successfully achieved by Mamdani with a fuzzy system for controlling a cement plant

(Mamdani 1977).

In other words, a fuzzy expert system is a collection of membership functions and

rules that are used to reason about data. Unlike conventional expert systems, which are

mainly symbolic reasoning engines, fuzzy expert systems are oriented toward numerical

processing. The rules in a fuzzy expert system are usually of a form similar to the

following:

if x is low and y is high then z = medium

where x and y are input variables, z is an output variable, low is a membership function

(fuzzy subset) defined on x, high is a membership function defined on y, and medium is a

membership function defined on z. The part of the rule between the if and then is the

rule's premise or antecedent. This is a fuzzy logic expression that describes to what

degree the rule is applicable. The part of the rule following the then is the rule's

conclusion or consequent. This part of the rule assigns a membership function to each of

one or more output variables. Most tools for working with fuzzy expert systems allow

more than one conclusion per rule. In Matlab, expert system which developed and name

in the fuzzy tool box are Mamdani and Takagi-Sugeno. Mamdani is well suited to human

input (Sivanandam et al. 2007). In this thesis, Mamdani was selected to evaluate the

location management cost of the proposed strategies.

A Fuzzy Inference System (FIS) is a way of mapping an input space to an output

space using fuzzy logic and it’s available in Matlab software. A FIS tries to formalize the

reasoning process of human language by means of fuzzy logic (that is, by building fuzzy

If-Then rules). FIS are used to solve decision problems, i.e. to make a decision and act

accordingly. The Mamdani-style fuzzy inference process is performed in four steps:

fuzzification of the input variables, rule evaluation (inference), aggregation of the rule

outputs (composition), and defuzzification. As shown in Matlab help (Anon 2010e), for

instance:

If the service is good, even if the food is not excellent, the tip will be generous

64

d. Structure of a fuzzy inference system

Fuzzy model consists of four modules. The first module is the fuzzification that

transforms the crisp value(s) into the fuzzy values. The fuzzy values are inferences based

upon the rule base incorporate in knowledge based. These rules are supplied by the

domain expert(s). All the outputs obtained from the inference engine are integrated and

defuzzied by the defuzzification module that transform the fuzzy output to crisp value(s).

The first two parts of the fuzzy inference process, fuzzily the inputs and applying the

fuzzy operator, are exactly the same. In general as shown in Figure 2.31, a fuzzy

inference system consists of four modules (Ramirez & Mayorga 2008):

1) Fuzzification module: transforms the system inputs, which are crisp numbers, into

fuzzy sets. This is done by applying a fuzzification function.

2) Knowledge base: stores IF-THEN rules provided by experts.

3) Inference engine: simulates the human reasoning process by making fuzzy

inference on the inputs and IF-THEN rules.

4) Defuzzification module: transforms the fuzzy set obtained by the inference engine

into a crisp value.

Figure 2.31 Fuzzy inference system

65

Fuzzy set of parameters used to determine the domain of a variable used in the

fuzzy set of policy rules, for example as shown in Figure 2.32. Most of the researchers

said that in order to determine the parameters of the fuzzy set should take into account the

opinion of an expert with strong knowledge of the problem to be solved. Questionnaire

and interviews with experts are how to obtain the necessary opinions. However, in some

cases, to get the parameters with high accuracy, the determination of the value of the

parameter can not be used fully expert opinion, but requires a method of optimizing the

function of specific goals. Examples of fuzzy sets with specific parameters are as follows:

Figure 2.32 An example of fuzzy sets

2.8.3 Neuro-Fuzzy

In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural

networks and fuzzy logic. Neural networks rely heavily on an extensive historical

database, and relatively little on a domain expert. Neuro-fuzzy system based on fuzzy

inteference system is trained using a learning algorithm derived from neural network

system. Thus, neuro-fuzzy system has all the advantages possessed by the fuzzy inference

system and neural network systems. From its ability to learn the neuro-fuzzy systems are

often referred to as Adaptive Neuro Fuzzy Inference Systems (ANFIS).

1

0

a b c d

66

Neuro-fuzzy hybridization results in a hybrid intelligent system that synergizes

these two techniques by combining the human-like reasoning style of fuzzy systems with

the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is

widely termed as Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS) in the

literature. Neuro-fuzzy system incorporates the human-like reasoning style of fuzzy

systems through the use of fuzzy sets and a linguistic model consisting of a set of If-Then

fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal

approximators with the ability to solicit interpretable If-Then rules.

2.9 SUMMARY

In this chapter, a brief market and technology of cellular of communication system is

described. Mobile phones or cellular handsets are changing with the changing trends in

mobile phone technology. Today mobile phones have everything ranging from the

smallest size, largest phone memory, speed dialing, video player, audio player, and

camera and so on. On the network side, operators have to upgrade their existing network

to the latest technology to maintain their operation and business. Although GSM

operational is still exist, to compete with the cellular technology and market

attractiveness, operators are competing to install and offer 3G services. The growth of

cellular users is another reason for network providers to deploy new cellular technology.

To optimize the quality of services in terms of mobility management, cellular network

providers have to maintain the two important operations in relation to mobility, location

update and paging process. These processes are part of location management. A survey of

location update scheme, paging scheme and also mobility model are described. At the end

of this chapter, graph theory and fuzzy logic are reviewed and the selected technique will

use later in the analysis. Also, a brief description of neuro-fuzzy is presented. In the next

chapter, the research methodology will be described.

CHAPTER III

METHODOLOGY

3.1 INTRODUCTION

In this chapter, simulation environment for the proposed mobility models to evaluate the

location management performance will be covered. MATLAB is used to generate the

MSs for the proposed mobility models: random walk mobility model and street lane

mobility model. Chiang and Shenoy (2004) investigated the number of location update

and derive dwell time. This model describes individual movements relative to cells (Bar-

Noy et al. 1994, Rubin & Choi 1997, Zonoozi & Dassanayake 1997). In literature,

random walk mobility model is used as a model and has been described in Chapter 2.

Street lane mobility model is proposed as the real street condition. Of the proposed

mobility model, the behaviors of mobility and location management aspect are the two

main evaluations conducted in this thesis. To obtain the data and to perform the results,

MATLAB is used as the simulation tools. An additional macro functions is developed in

Microsoft Excel to facilitate the process of calculation for location management

optimization.

3.2 SIMULATION ENVIRONMENT

The hexagon shape model is used in wireless communication network to describe the

coverage while in practical the shape of the coverage might be irregular. In this context,

the hexagon shape called as cell. The cell size is one of the essential parameter in

designing cell layout and the whole service area for the simulation environment. It can

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affect the number of uses that can be served and indirectly reflected the cell residence

time value. The hexagonal cell geometry is shown in Figure 3.1. The total area of each

hexagonal cell, Ahex is depended on the scale of cell radius, r and can be calculated using

(Rappaport 2002):

Ahex ≈ 2.598 r2 (3.1)

Figure 3.1 Hexagonal cell geometry

In terms of mobility, MS can be classified into mobile and stationary users.

Mobile user means that MS has capabilities to move freely in the service area while no

mobility activity has been done by stationary in the service area. If MS reaches the border

of the simulation area, MS will be reflected back to service area. MS will always move

from one point to another point and its duration is limited by simulation time. The

simulation area consists of 49 hexagonal cells as illustrated in Figure 3.2. The simulation

environment is developed to study the active mobile user.

For this simulation, MSs were generated randomly at different initial position

within service area. These values were proposed to reduce the simulation processing time.

69

Each cell is designed with a radius of 10 km. The vehicles speed in this simulator are

ranging in [0, 90] km/h. With these cell radius and speed limit, it was predicted that MS

can travel to border of a cell in 1/9 hour or equal to 6.67 minutes or 0.4 millisecond. By

using all this initial parameter to the proposed mobility model, a hundred of MSs will be

generated and simulated in 180 unit time. For each mobility model, the matrix size will be

a 100 x 180. The simulation is run with personal computers using the Pentium 4 processor

with 256 MB of RAM.

Figure 3.2 Simulation service area

3.3 CELL CLUSTERING MODEL

A cellular network is a radio network made up of a number of cells, each served by at

least one fixed-location transceiver known as base station. When joined together these

70

cells provide radio coverage over a wide geographic area. Cellular networks are

inherently asymmetric with a set of fixed main transceivers each serving a cell and a set

of distributed transceivers which provide services to the network's users.

In this thesis, the service area is constructed using cluster of hexagonal cells

concept and then the whole coverage area is divided into LAs. All these LAs are

connected to base station and controlled by network system. The communication

transaction in the service area will be registered in database system, HLR and VLR. To

simulate the user mobility model in the service area, two types of cell clustering models:

symmetrical clustering and asymmetrical clustering are designed. Both of these models

have seven clusters. In symmetrical clustering, in each cluster has uniform number of cell

which consists of seven cells. This model called as balanced-cell model. It assumed that

the entire clusters have moderate density and the channel availability is enough to handle

the entire request signaling transaction. The number of cells in each cluster in

asymmetrical has different quantities and in this thesis called as unbalance-cell model. It

is assumed that for each cluster has different cell capacity and the number size of each

cluster is varied, 5 to 9 cells. The bigger cluster size has more channel capacity. This

example can be found in a cluster of unbalanced-cell model. Both of these models are

shown in Table 3.1 and Table 3.2.

Table 3.1 Symmetrical cell clustering

Cell Cluster Cell Number LA 1 1, 2, 3, 4, 5, 6, 7 LA 2 8, 9, 10, 11, 12, 13, 14 LA 3 15, 16, 17, 18, 19, 20, 21 LA 4 22, 23, 24, 25, 26, 27, 28 LA 5 29, 30, 31, 32, 33, 34, 35 LA 6 36, 37, 38, 39, 40, 41, 42 LA 7 43, 44, 45, 46, 47, 48, 49

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Table 3.2 Asymmetrical cell clustering

Cell Cluster Cell Number LA 1 1, 2, 3, 4, 5, 6, 7 LA 2 8, 9, 10, 11, 12, 14, 16, 21 LA 3 15, 17, 18, 19, 20 LA 4 22, 23, 24, 25, 26 LA 5 27, 28, 29, 30, 33, 34, 35, 39, 40 LA 6 36, 37, 38, 41, 42 LA 7 13, 43, 44, 45, 46, 47, 48, 49

A proposed balanced-cell model as described in Table 3.1 is shown in Figure 3.3

and an alternative unbalanced-cell in Table 3.2 is illustrated in Figure 3.4.

Figure 3.3 Balanced-cell model

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Figure 3.4 Unbalanced-cell model

3.4 MOBILITY MODEL

Two types of mobility model, Random Walk Mobility Model and Street Lane mobility

Model, are described in this section. In literature, three-direction mobility model is used

by Abutaleb (1993) to study the mobility management with grid cellular architectures in

which there are four direction possibilities of MS to move. For random walk model, it is

assumed MSs are in the city and can access many streets. In these models, directions are

dynamic and the direction is depended on the implemented strategy. To evaluate the

mobility behavior, direction possibilities are extended. In street lane model, the direction

is limited. The details of these two proposed models are described in the following

section.

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3.3.1 Random Walk Mobility Model

In this random walk model is varies in terms of the number of direction. The first

proposed model, each MS will choose direction randomly (North, South, West or East)

with equal probability of ¼. In terms of angle, the selection has 0, π⁄2, π and 3π/2. In

degree can be writes as 0, 90, 180, and 270. The direction selection is controlled by

threshold value, α, and angle are shown in Table 3.3. Three direction schemes based on

this threshold value is designed. Weight of the threshold value selected based on criteria:

integers (scheme A) and the probability values (scheme B and C). Selection probability

value in scheme B and C are different in which to scheme B based on uniform

distribution, while scheme C is based on the velocity distribution.

Table 3.3 Threshold value of movement direction

Direction

(Angle in

degree)

Angle

(in degree) Threshold Value, α

Scheme A Scheme B Scheme C

East 0 1 0.75 ≤ α < 1 76.5 ≤ α < 90

North 90 2 0.50 ≤ α < 0.75 45 ≤ α < 76.5

West 180 3 0.25 ≤ α < 0.50 22.5 ≤ α < 45

South 270 4 0 ≤ α < 0.25 0 ≤ α < 22.5

In the second model, each MS has 1/8 probabilities. The directions are North,

South, East, West, Northeast, Southeast, Northwest, and Southwest. This model provides

an opportunity to move more freely with MS compared with the model of four directions.

Both of these random walk models are shown in Figure 3.5. Both of those models are

used to evaluate mobility behavior.

74

(a) (b)

Figure 3.5 Random walk with certain direction:

(a). 4-directions, (b). 8-directions models

3.3.2 Street Lane Model

In lane street mobility model, all the MS is assumed to be using the highway. To reduce

the computational complexity problem, the number of lanes on the simulator that was

developed is limited to three lanes as shown in Figure 3.6. Tables of the street lane and

density lane database are generated. The direction in the simulation is only allowed MS to

move forward or not moving. Highway not always has a straight path; sometimes the road

will be curved at certain point. In the highway, MS was only allowed to move in one

direction, so that the user is not possible to reverse. MS is not allowed to jump directly

from lane 1 to lane 3 and vice versa. MS only allowed shifting from lane 1 to lane 2, from

lane 2 to lane 3 and vice versa. The process to turn left or right so that it can turn 90

degrees is not possible. To switch lanes, then the MS can move forward with changing

the orientation direction of a few degrees.

75

Figure 3.6 Street lane layout

Under conditions of normal density, a user can drive is allowed to reach up to

maximum speed. Although rare, occasionally the condition of highway becomes

congested when there are accidents or other incidents that could hamper its speed

vehicles. In this thesis, the density weighting value, ρ, is designed and assumed in

accordance with highway crowded conditions which are divided into three categories: not

crowded (ρ < 0.35), moderate (0.35 ≤ ρ ≥ 0.7), dense (ρ ≥ 1). Moderate condition is a

situation where the density of vehicles still allows the user to drive vehicles with an

average speed conditions influenced by the density weighting value.

76

3.5 MOBILITY MANAGEMENT EVALUATION

In this thesis, the effect of mobility will be studied. One of the interesting aspects of

mobility to be studied is the behavior of mobility. Evaluation of behavioral analysis

conducted based on distribution of data generated by the mobility that occurred in the

area of simulation. Of the two models adopted in the simulation model, the MS

distribution generated by random walk model is more varied. In this model, the

possibility of inter-cell movement becomes more open and especially for pedestrian, MS

can be located anywhere, including in the building. On the other hand, the distribution

model of MS in the street lane which requires MS remains in the highway.

Location management cost can be derived from the total cost of location update

and paging activities. In this thesis, all dynamic location update schemes are evaluated.

The location update costs depend on the pre-defined threshold to each tested schemes. In

general, the cost will be calculated if the MS update its existence according to the latest

location where the specified threshold limit has been exceeded. The paging scheme will

process any incoming signal by identifying the cell id. The system will count as a cost if

the current cell id is different to the previous one.

To establish the analytical model for the location management with HLR/VLR

network architectures, based on location data updating in HLRs and in VLRs, the

signaling transaction can be calculated using (3.2). Denote that i is the number of MS and

k is the amount of simulation time. Based on (3.2) and (3.3), the total cost for evaluating

LU and Paging in both registration system, VLR and HLR can be analyzed using (3.4).

Location Update:

,1 1

( )jl

LU HLR VLR i kk i

C C C= =

= +∑∑ (3.2)

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Paging:

,1 1

( )jl

p VLR Cell i kk i

C C C= =

= +∑∑ (3.3)

Total Cost:

������ � ��� � �� (3.5)

where, CLU is the cost of location update, Cp is the cost of paging, CHLR is the cost of

HLR, CVLR is the cost of VLR, and CTotal is the summation of location update and paging

cost.

To analyze the mobility behavior, a concept of graph theory and mathematics are

used. Behavior patterns can be investigated by using graph theory to obtain the identity of

the resulting matrix. The result can be classified in isomorphism pattern if it produces the

same pattern. Another way is by using the concept of permutations. A permutation is a

rearrangement of the elements of an ordered list Ѕ into a one-to-one correspondence with

Ѕ itself. The number of permutations on a set of n elements is given by n! (Uspensky

1937). A permutation of objects is an arrangement of those objects in some order; that is,

some object is placed in the first position, another in the second position, and so on, until

all objects have been placed. There are two ways that can be described by using this

concept, a recurring patterns and permutations without repetition.

To figure out the result and ease the computational aspect, the 100 x 180 data,

then divide into four-pattern range. By using this technique, the matrix will reduce to 100

x 45. The number of permutations of n distinct objects taken r at a time, denoted by �, is

given by

a. Permutation with repetition: nr (3.6)

b. Permutation without repetition : �!

�����! (3.7)

To characterize the mobility pattern of this model, a set of matrix of 100 x 180 is

generated. For 4-directions of random walk mobility model, n = 4 and r = 4, using

78

equation (3.6), a 4 x 256 matrix is developed. The matrix size will be reduced to 4 x 24 if

the concept of permutations without repetition is applied. There are four direction

indexes, 1 for East direction (turn right), 2 for North direction (forward), 3 for West

direction (turn left) and 4 for South direction (reverse). A 4 x 4096 matrix with repetition

number will be developed for 8-directions of random walk mobility model. It can be

achieved with n = 8 and r = 4. In this matrix, the number sequence is 1, 2, 3, 4, 5, 6, 7,

and 8 to represent East, Northeast, North, Northwest, West, Southwest, South, and

Southeast. In permutation without repetition as shown in equation (3.7), the size will be

shrinking to 4 x 1680. A brief of 4-directions of random walk mobility model matrix is

shown in Table 3.4. For the complete table of the entire random walk mobility model

matrix will be shown in Appendix B.

Table 3.4 Pattern index

Pattern Index Direction Index

1 1 1 1 1

2 1 1 1 2

3 1 1 1 3

… … … … …

254 4 4 4 2

255 4 4 4 3

256 4 4 4 4

3.6 COMPUTER SIMULATION DEVELOPMENT

In this simulation, a dynamic location management scheme is used. The simulation flow

chart is shown in Figure 3.7. After initializing the parameters, MSs are generated in the

service area of 49 hexagonal cells. All the MSs is assumed in the active mode. In the real

situation, for the inactive status, the alert will be sent to the caller. Then those generated

MSs move according to the implemented mobility model. MS sends a BCH to hear the

feedback from the network for the available channel. Presumably, at least there is one

79

channel available. System will locate the MS by giving the best signal. To locate the

nearest and best BS, in this work used cell coordinate system to measure the distance

between BS to the last reported cells of MS, with the assumption that every BS is located

in the cell centre. The location of MS position to the nearest BS in two dimensional plane

can be calculated using equation (3.8). In graph theory the distance between two vertices

is the length of the shortest path between those vertices. To calculate the MS-BS distance,

analytic geometry can be used and the distance between two points of the xy-plane can be

found using the distance formula. The distance between (x1, y1) and (x2, y2) is given by

(Gray et al. 2006):

� � ��� ��� � ��� ���� (3.8)

where, x1 is origin point, x2 is end point, and d is distance between points.

For the complete procedure of the simulation is described in Figure 3.7. It starts

from a defined initial parameters, draw the service area, generate MS, run the proposed

both mobility models and then make an analysis. Location management cost calculations

will be performed if the MS exceeds a specified threshold value. To support the

simulation process of street lane mobility model, a switching-lane database and traffic

density with a weighing factor is generated. Also, a pattern database is built to be used to

recognize the pattern in graph analysis. The psedo-code of the simulation steps are

attached (Appendix C).

80

Figure 3.7 Simulation flow chart

81

3.7 DEVELOPMENT DESIGN USING FUZZY LOGIC TECHNIQUE

Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal with

reasoning that is approximate rather than precise. Fuzzy logic starts with and builds on a

set of user-supplied human language rules. The fuzzy systems convert these rules to their

mathematical equivalents. Fuzzy logic models, called fuzzy inference systems, consist of

a number of conditional "if-then" rules. A fuzzy expert system is an expert system that

uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to

reason about data. The rules in a fuzzy expert system are usually of a form similar to the

following:

if x is low and y is high then z = action

where x and y are input variables, z is an output variable, low is a membership function

(fuzzy subset) defined on x, high is a membership function defined on y, and action is a

membership function defined on z. The antecedent (the rule's premise) describes to what

degree the rule applies, while the conclusion (the rule's consequent) assigns a membership

function to each of one or more output variables. The set of rules in a fuzzy expert

system is known as the rule base or knowledge base.

In this thesis, an expert system was developed in MATLAB using fuzzy logic. In

the development fuzzy logic expert system in location management, three parameters

related in user’s mobility are defined. These three parameters are are Speed, Density, and

Residence Time.

a. Membership Functions

A fuzzy logic is fully defined by its membership function. The membership function

selection process is done with trial and error and it runs step by step which is too long

in completing the problem. In this design, each fuzzy variable is assigned to three

input membership functions and one output membership function in triangular shape:

1. Speed variable, the membership functions are low, average, and fast.

2. Density variable, the membership functions are low, average, and high.

3. Residence Time (ResTime) variable, the membership functions are short, aver

and long.

Triangular function

where a < m < b

function and x and

The membership functions give a degree or grade of membership within the range:

speed: [0, 90], density: [0, 1], and residence time: [0 150].

membership functions for input: speed, density and residence time (Restime

defined as shown in Figure 3.

Density variable, the membership functions are low, average, and high.

Residence Time (ResTime) variable, the membership functions are short, aver

Triangular function is defined by a lower limit a, an upper limit

as shown in Figure 3.8. The figure is only to model a triangular

and y axis is not defined yet.

Figure 3.8 Triangular function

The membership functions give a degree or grade of membership within the range:

0], density: [0, 1], and residence time: [0 150].

membership functions for input: speed, density and residence time (Restime

defined as shown in Figure 3.9, Figure 3.10 and Figure 3.11.

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Density variable, the membership functions are low, average, and high.

Residence Time (ResTime) variable, the membership functions are short, average,

, an upper limit b, and a value m,

. The figure is only to model a triangular

The membership functions give a degree or grade of membership within the range:

0], density: [0, 1], and residence time: [0 150]. The shapes of the

membership functions for input: speed, density and residence time (Restime) are

83

Figure 3.9 Membership function of input variable “Speed”

Figure 3.10 Membership function of input variable “Density”

84

Figure 3.11 Membership function of input variable “ResTime”

To express the result, the linguistic expression of Location Update (LU) and No Location

Update (NLU) is defined in fuzzy using triangular membership function. The results are

known as LU in the range [5 10] and NLU activities if the result lower than 5 is shown in

Figure 3.12.

Figure 3.12 Membership function of output variable “Results”

85

b. Rules:

Fuzzy sets and fuzzy operators are the subjects and verbs of fuzzy logic. These if-then

rule statements are used to formulate the conditional statements that comprise fuzzy

logic. In general, the input to an if-then rule is the current value for the input variable

and the output is the entire fuzzy set. The output fuzzy sets for each rule are then

aggregated into a single output fuzzy set. Finally the resulting set is defuzzified, or

resolved to a single number. In this thesis, 10-set of rules are defined to evaluate the

cost of location management. For maximum rules, there are 81 rules. The reduction of

the total available rules, the proposed 10-set of rules is designed to minimize the high

computational process and this design is met the research objective.

3.8 SUMMARY

To investigate the MS mobility is a challenging problem in cellular network. Therefore,

we need to figure out more practical mobility problems in network environments. In this

section, the simulation environment with initial parameters are defined and will use in the

simulation. Two mobility models will be developed for the evaluation, Random Walk

Mobility Model and Street Lane Mobility Model. Random Walk Mobility Model is built

in two direction variations. The first model has four directions and the other model has

eight directions. In these models, MS can freely move in the service area. In the street

lane model, MS will stay only on the street and has to follow some restriction like no

return and always go forward. To figure out the result, a simulation flow chart and fuzzy

logic technique are discussed. In the next section, the performance of the simulation will

be presented.

CHAPTER IV

LOCATION UPDATE AND PAGING RESULTS

4.1 INTRODUCTION

In this thesis, the mobility of a mobile station is measured by tracking its speed and

movement. Tracking of movement is accomplished by identifying the change of mobile

station direction which depends on the mobility model. Data concerning the base station

visited and duration of stay of the mobile station are recorded by the network while the

mobile station status is active.

In this chapter, the performance of location management cost using two types of

mobility model will be evaluated. One of the most important goals of location

management studies ist obtained the efficient utilization of the radio bandwidth. This

requirement can be achieved by minimizing the number of location management cost:

location update cost and paging cost. To measure the cost, a simulation environment build

and then run using Matlab is defined. Random walk mobility model is chosen in this

analysis which is the most model used in many literatures. This model is very suitable for

low mobility users such as pedestrians. For users who currently drive with high speed, so

street lane mobility model used to represent users who drive on the highway. At the end

of this chapter, an analysis using graph theory and fuzzy logic are evaluated. Graph

theory as described in Chapter 2 is used to assess the patterns of user behavior in

simulation and the application of fuzzy logic technique is intended to obtain optimal

results from the evaluation of the performance location management cost. An in-depth

study for the dynamic location management scheme and the development of the

87

simulation model has been carried out in Chapter 3. The results from the simulation for

the evaluation of the schemes are presented in this chapter.

4.2 MOBILITY MODELS ANALYSIS

The initial MSs position are generated in this simulation as shown in Figure 4.1. A

population of a hundred of MS are used in this thesis to examine the proposed mobility

models and their behavior and effect to location management operation. A total of 49

cells are designed as service areas in the area of simulation and then will be partitioned

according to the proposed strategy. All the MSs will be in the simulation area and has

been programed that no MS will out of the service area.

Figure 4.1 Initial MSs location in the simulation area

88

4.2.1 Random Walk Mobility Model Analysis

In this thesis, Random Walk Mobility Model, there are two direction models proposed.

Four- and eight-directions are used to evaluate the location update and paging and pattern

analysis. An example of MS trajectory using random walk model is shown in Figure 4.2.

For all the proposed models, MS travels to certain degree as defined in the model. In four-

direction each MS will choose direction randomly with equal probability of ¼. In terms

of angle, the selection has 0, π⁄2, π and 3π/2. For random walk mobility model, the speed

of MS is varied. Pedestrian can travel with low speed. User who drives can move faster

and has to follow the speed limit rules and traffic regulations.

Figure 4.2 An example of MS trajectory using random walk mobility model

4.2.2 Street Lane Mobility Model Analysis

In contrast to random walk mobility model, in highway MSs have to follow the flow of

traffic. In Figure 4.3, an example of six highways model are presented. MSs movement

are generated and indicated as different market plot on the highways. Normally, highway

89

has many lanes. In this thesis, only three lanes are proposed. In the figure, no lane is

shown. Lanes are constructed in algorithm and run in the simulation environment.

Figure 4.3 An example of highway model for the proposed model

4.3 LOCATION MANAGEMENT COST

The study of location management is one of the fundamental issues in cellular networks.

It deals with how to track subscribers on the move. This study aims to reduce the

overhead required in locating mobile devices in a cellular network. In location

management perspective, network cost is affected by signaling activities of LU and

paging transactions. In a static scheme, there is a predetermined set of cells at which, MS

regardless of its mobility must generate a LU. In a dynamic scheme, MS in any cell

depending on its mobility can generate a LU based on threshold value. Among dynamic

LU schemes: movement-based (Bar-Noy 1995), time-based (Rose 1996) and distance-

based (Ho & Akyildiz 1995 and Madhow 1995), distance-based was selected to evaluate

the performance of location management cost in this work.

90

4.3.1 Location Update Cost

The basic idea of this distance-based LU algorithm used in this thesis is as follows. Each

cell having a base station and having its own id (identifier), with MS resides in each cell

for some time interval before moving on the next cell. In random walk mobility model, it

assumed that MS has equal probability that any one of the immediate neighboring cells is

selected as the destination. Destination cell may not be in the same LA. By setting a

threshold limit value equal to one is the most easy and simple. But this approach causes

the value of LU will increase drastically.

To reduce the increase of LU drastically, a study has been conducted indicate that

the threshold limit, d=3 is optimal for less directive travelling pattern and d=4 is optimal

for more directive travelling pattern (Tung 2004). The distance-based LU (Ho & Akyildiz

1995 and Madhow 1995) was selected among the schemes in this work. In this thesis, the

threshold limit is d=4. Four different LU strategies are implemented in this work.

Denoting the base station in every cell as BS1 until BS49 and suppose that the initial

location of the MS is in cell 47, the system will register the MS as BS1 data. Every time

MS moves to a different cell, the system will count as a cost. Figure 4.4 shows a sketch of

a sample path, showing MS travels from cell 47→ 38→ 6→ 5→ 4→ 1. The → symbol

means a path from the beginning point to end point.

91

Figure 4.4 An example of MS travels path

The strategies are as follows.

1. If the current cell ID (n) is different to (n-1) the cost is LUA. This strategy is

similar to Lin (1997) and Abutaleb (1997).

2. If the current cell ID (n) is different to (n-1) and (n+1), the cost is LUB.

3. If the current cell ID (n) is different to (n-1) and (n-2), the cost is LUC.

4. If the system can record larger data set for each user state (ni), where i = 1 to m

and m is the maximum of length of the data set. The difference in cell ID in the

data set gives the cost, LUD.

The distance-based scheme is determined by measuring the cell-to-cell distance. The

updating cost will be processed only if the MS location is changed and the specified

threshold limit has been exceeded. In the same way also applied to calculate the updating

cost for movement-based and time-based.

92

4.3.2 Paging Cost

For paging scheme, a simple analysis using single PA and zone-based strategy are

employed in this work. In zone-based, the system is divided into equal size (weight) area,

among MSCs to keep the network overhead minimal. For this purpose, seven

symmetrically LA of equal size, in which each cluster consists of seven cells is defined

and shown in chapter 3. The paging scheme will process any incoming signal by

identifying the cell ID. The system will count a cost if the current cell ID is different to

the previous one. The algorithms are as follow:

a. Based on zone area, the system will check if MS belong to a certain LA, if it is

different, the system will count as a paging cost, Pg A.

b. The system will detect individually to the paged MS in a single cell, the paging

cost Pg B.

c. Based on the zone concept, but assuming the system can store previous

information and detect if MS stays in different PA, the paging cost is Pg C.

4.3.3 Location Update and Paging Cost Analysis

The proposed LU and paging strategies are used to evaluate the running mobility model

and location management cost. A lot of data transactions are recorded and to ease the

analysis process, a statistical approach named Cumulative Distribution Function (CDF) is

used. This function describes a statistical distribution. This technique describes the

probability that a real-valued random variable X with a given probability distribution will

be found at a value less than or equal to x.

Figure 4.5 shows the Cumulative Distribution Function (CDF) for the four

location update strategies. In strategy LUA, the total LU activities are the highest and in

the Figure 4.5 can be read that its value is almost 2.5 x105 activities. With LUA as the

benchmark, the investigation show LUD is superior with respect to LUB and to LUC in this

93

order, with each cdf values of 76%, 52%, and 30% respectively. Even though, LUD gives

the best result, however, it requires larger memory size, and this will obviously incur an

additional cost to the system. For this reason, LUB algorithm can be considered as an

optimum choice if the cost of memory is a taken into consideration.

Figure 4.5 Location update performance

In strategy Pg B, the total paging activities are the highest and in the Figure 4.6

shows that its value is almost 2.5 x105 activities. Figure 4.6 shows CDF of the average

number of paging for the three paging strategies described. With Pg B as the benchmark,

the cdf for Pg C is 93% and the CDF for Pg A is 65%. Pg C is shown to be superior than Pg

A and Pg B respectively. The result shows that, using paging strategy only for one cell will

waste the network resources and increase the paging cost.

94

Figure 4.6 Paging performance

In Chapter 3, two strategies namely balanced-cell and unbalanced-cell are already

defined. In the balanced-cell, the service area is partition into an equal size area in terms

number of cell. In the unbalanced-cell, the number of cell for each service area is

different. For this strategy, it is assumed that each service area has different subscriber

densities (which depend on the density of the housing estate, offices, shopping mall and

open area). To evaluate those strategies performance, a single-cell strategy is developed.

In this strategy, the service area is a single cell, which corresponds to 49 service areas.

For each strategy, each LA is weighted with a particular value called the cell cost (CCell).

CLU and CPaging are the cost for LU and Paging respectively. To evaluate the performance

of the total cost, the proposed formula as follows:

CTotal = CLU + CPaging + CCell (4.1)

Since, each signal transmission is recorded in a registration system, LU activities

will have to consider the HLR and VLR cost for registering and deregistering MS status

in the network. However, for paging procedure, only the VLR cost is considered. As

95

stated in the previous paragraph, an evaluation of the number of signal transactions in

HLR and VLR in terms of network resources is studied and presented.

Figure 4.7 and Figure 4.8 show the signaling transactions in the VLR and HLR for

the three strategies. The letters A, B, and C following the underscore after the HLR and

VLR represents the single-cell, balanced-cell, and unbalanced-cell strategies respectively.

As shown in Figure 4.7, the unbalanced-cell strategy has higher registration signaling

compared to single-cell and balanced-cell strategies for VLR registration. However, for

the HLR registration as shown in Figure 4.8, the single-cell strategy has higher

registration signaling compared to unbalanced-cell and balanced-cell strategies. The

balanced-cell strategy has been shown to have the least registration signaling in both VLR

and HLR registration compared to the other two strategies. The number of MS’s signaling

transaction in the network for single-cell, balanced-cell and unbalanced-cell strategies are

44.22%, 24.39%, and 31.39% respectively for HLR registration and 34.92%, 22.23%, and

42.85% respectively for VLR registration.

Figure 4.7 Signaling in VLR system

0

5000

10000

15000

20000

25000

30000

VLR_A VLR_B VLR_C

Sig

nal

ing

VLR Registration

96

Figure 4.8 Signaling in HLR system

Figure 4.9 shows the combination of the HLR and VLR cost for each strategy as

the total cost. It is clear from the figure that the balanced-cell outperforms in terms of

registration signaling compared to the other two strategies. The percentage of registration

signaling for these strategies are 35.16% for Total A, 22.28% for Total B, and 42.56% for

Total C.

Fig. 4.9 Total of signaling transaction

0

100

200

300

400

500

600

700

800

HLR_ A HLR_B HLR_C

Sign

alin

g

HLR Registration

0

5000

10000

15000

20000

25000

30000

A B C

Sign

alin

g j

Total of Location Management Strategies

97

Figure 4.10 shows the network efficiency performances for the proposed

strategies. The network performance is determined by obtaining the service area density

in each implemented strategy. Based on the network perspective, the results show that the

single-cell strategy has the least average load resources usage and hence is more efficient

compared to the other two strategies. Single-cell has an average result of 2.04% while the

balanced-cell and unbalanced-cell have the average results of 14.29% and 12.89%

respectively in using network more signaling.

Figure 4.10 Network efficiency performances of proposed strategies

From the proposed strategies, LU and paging that have been already investigated

in this section; twelve possible combinations can be employed for the mixed LU-paging

strategies. Figure 4.11 shows performance of the mixed strategies where each LU and

Paging combination is mixed as pair in term of signaling activities. As shown in this

figure, the PG_B and LU_A combination is the worst pair and the PG_C and LU_D is the

best pair in terms of paging and updating cost reductions.

0%

2%

4%

6%

8%

10%

12%

14%

16%

A B C

Net

wor

k U

sage

Location Management Strategies

98

Figure 4.11 A mixed LU and Paging signaling activities

Based on paging results which give the lowest activities, four best mixed LU-Paging pair

is selected to analyze as shown in Table 4.1. Figure 4.12 shows the CDF plot of the four

strategies. With strategy D as the benchmark, the result show that the CDF of strategy A,

B, and C are 61%, 30%, and 22% respectively. This result shows that the mixed strategy

A is superior to others that will give the lower signaling activities and the best choice to

implement.

Table 4.1 Strategy for mixed LU-Paging Pair Strategy Mixed LU-paging pair

A LU_D and PG_C

B LU_B and PG_C

C LU_D and PG_A

D LU_C and PG_C.

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

1,00

PG_A PG_B PG_C

Nor

mal

ized

Val

ue

The number of Signaling Activities

Combined LU:

LU_A

LU_B

LU_C

LU_D

99

Figure 4.12 Comparison of mixed LU-Paging strategies performance

4.4 SUMMARY

In this chapter, location update and paging scheme and strategy using two hierarchical

registration systems have been evaluated. Result shows that the performance of the

network is affected by the signaling registration; in general the cost is higher in VLR

compared to HLR system. This results, as using random walk mobility in which the MS

frequently across the cell or LA border that degrades the network performance.

Simulation results show that the number of MS activities is higher in the LA border

especially in single-cell in HLR compared to others strategies. In contrasts, the balanced-

cell strategy has the least VLR and HLR updating. This strategy outperforms the other

two strategies in terms of percentage of registration signaling of 22.28% while for single-

cell and unbalanced-cell strategies, the percentage of registration signaling are 35.16% in

and 42.56% respectively. Also, a practical mixed strategy of LU and Paging has been

studied and evaluated. The cost function of the analysis is counted as a number of

signaling activities in LU and paging strategies in cumulative distribution function.

100

Simulation results showed the performance of LU case: Strategy LU_D gives superior

performance (76%) with respect to the other three (LU_B, LU_C and LU_A)

respectively. In the Paging case: the result of Strategy PG_C gives the best performance

(93%) with respect to others, Paging strategy PG_A and PG_B. Both of these LU and

Paging strategy employed larger data set. The strength of these strategies is that it

minimizes the cost of calculating the redundant data. Simulation results show that

strategy A, a combination of LU_D and PG_C, give the lower signaling activities with

cumulative distribution function (cdf) value of 61% with respect to the worst case

combination of strategy D. The results show that less than 10% signaling activities in

position analysis and on the other hand location analysis shows more than 50% signaling

activities in the system. The greater value in location analysis is growing as the effect of

subscribers’ mobility behavior within the logical structure of the network. The result of

street lane mobility model shows that 36% of subscribers are distributed in cluster-1 of

balanced location area and 42% of subscribers are stayed in cluster-1 of unbalanced

location area. Furthermore, the results of mobile subscribers using eight direction random

walk mobility is 46% distributed in cluster-3 of unbalanced location area while mobile

subscribers using four direction random walk mobility is 39% distributed in cluster-1 of

balanced location area. These outcome means that the role of mobility model has

influenced to the number of signaling activities in service area which using in the

network.

CHAPTER V

FUZZY LOGIC-BASED MOBILITY MANAGEMENT SCHEME

5.1 INTRODUCTION

In this chapter, the graph analysis and fuzzy logic approach to solve mobility

management problem will be described. The direction schemes and the cell clustering

strategies that designed in the Chapter 3 are used to evaluate the behavior of the generated

MS in the simulation area. Then, graph theory is used to characterize the patterns of the

direction of MS in accordance with the proposed mobility. In the last section, the

application of fuzzy logic technique is used to optimize the location management

operation. To obtain the result, three parameters: density, residence time and speed are

used to evalute the performance of fuzzy logic technique.

5.2 GRAPH ANALYSIS

In Chapter 3, direction schemes have been described as shown in Table 3.3. For all of the

schemes designed, MS has ¼ probability to select the direction in random walk mobility

model depended on direction threshold, α. In terms of graph analysis as shown in Table

3.4, all the graph direction of the simulation can be reduced to 24 unique pattern as shown

in Figure 5.1. MS can start and go to its destination or end point and can go back to its

previous starting point. The reduction can be done by using the isomorphism as described

in section 2.8.1 of Chapter 2. Using this technique, the user behavior can be described

easily. Repeated and non repeated pattern are to analyze the behavior of MS of the

proposed mobility models. Repeated pattern is a description of MS behavior and its

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direction, for example always direction 1 is repeated and for non repeated pattern, MS

will travel with different direction for every observed pattern slot.

Figure 5.1 Unique pattern of 4-directions

From the result of mobility for each the proposed direction schemes, most of the

evaluation schemes show that repeated pattern more than 75% as shown in Table 5.1.

From the table, the simulation results show that scheme C has the highest pattern

distribution for repeated pattern. This behavior describes that MS has a limited travel

distance and has more signaling on the same network compared to non-repeated pattern.

For all the users, the average result shows that about 8.78%, 8.09%, 5.24% respectively to

scheme A, scheme B and scheme C, users repeated the path.

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Table 5.1 Pattern distributions

Pattern Distribution

Scheme A Scheme B Scheme C

Repeated Pattern 77.78% 80.00% 86.67%

Non-repeated Pattern 22.22% 20.00% 13.33%

Also, in this section, these schemes are analyzed based on clustering approach of

cell strategy: single-cell, balanced-cell and unbalanced-cell. The results show that MS has

the highest mobility in the service area with distributions for each strategy 39%, 51% and

47% respectively (The figures of these data are shown in Appendix B). These results

indicate that MS is not taking all the existing cells within the service area. Based on

balanced-cell strategy that used seven location areas as service area, the result shows that

only six location area are occupied. The highest occupation of MS is 36% in Scheme A,

35% in Scheme B and 27% in Scheme C. All of these schemes are implemented for

balanced approach as shown in Figure 5.2 to Figure 5.4.

Figure 5.2 MS distribution for scheme A using balanced-cell strategy

14%

27%

36%

12%9%

2%0%

0%

10%

20%

30%

40%

1 2 3 4 5 6 7

Location Area (Balanced)

MS Distributions - Scheme A

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Fig. 5.3 MS distribution for scheme B using balanced-cell strategy

Figure 5.4 MS distribution for scheme C using balanced-cell strategy

In unbalanced-cell analysis, the result quite similar to balanced-cell analysis that a

location area is not utilize to all the service area. In unbalanced-cell, the highest

occupation of MS is 32% in Scheme A, 32% in Scheme B and 28% in Scheme C. All of

these schemes are implemented for unbalanced approach as shown in Figure 5.5 to Figure

5.7. From the figures, the results show that the highest occupation for unbalanced are in

16%

35%

19% 19%

7%4%

0%

0%

10%

20%

30%

40%

1 2 3 4 5 6 7

Location Area (Balanced)

MS Distributions - Scheme B

23% 22%

27%

14%10%

4%

0%

0%

10%

20%

30%

1 2 3 4 5 6 7

Location Area (Balanced)

MS Distributions - Scheme C

105

cluster 2 in scheme A, cluster 3 in scheme B and cluster 3 in scheme C. In term of

mobility characteristic, the results show that 90% of service areas are occupied by MS for

both balanced and unbalanced strategies while in cell strategy approach the distribution

for all the schemes the result is less that the above strategies in terms of service.

Figure 5.5 MS distribution for scheme A using unbalanced-cell strategy

Figure 5.6 MS distribution for scheme B using unbalanced-cell strategy

8%

29%32%

17%

10%

4%0%

0%

10%

20%

30%

40%

1 2 3 4 5 6 7

Location Area (Unbalanced)

MS Distributions - Scheme A

11%

32%28%

16%

8%5%

0%

0%

10%

20%

30%

40%

1 2 3 4 5 6 7

Location Area (Unbalanced)

MS Distributions - Scheme B

106

Figure 5.7 MS distribution for scheme C using unbalanced-cell strategy

To characterize the mobility pattern of these schemes, the evaluation will be done

by pairing the pattern to the Table 3.3. The movement direction pattern for each

implemented schemes for a portion of overall plot is shown in Figure 5.8. The sequences

of each movement direction show that MS moves depend on threshold value and compare

the all the schemes, the pattern is unique. In the figure, the movement direction index: 1,

2, 3 and 4 are representing to East, North, West and South respectively.

Figure 5.8 Movement pattern of the tested schemes

19%

28%

19%21%

11%

2%0%

0%

10%

20%

30%

1 2 3 4 5 6 7

Location Area (Unbalanced)

MS Distributions - Scheme C

107

In Figure 5.9 shows the comparison of the three schemes. Each scheme has its

own pattern sequences and compare to each other, we found that no a single scheme

repeat the same pattern.

Figure 5.9 Direction scheme pattern distributions

5.3 FUZZY LOGIC ANALYSIS

Fuzzy logic is used to optimize the previous result that shown in Chapter 4 using location

management approach. Three parameters: Speed, Density and Residence time are used

for fuzzy evaluation. As described in Chapter 3, three input of membership functions are

defined within the range: speed interval [0, 90], density interval [0, 1], and residence time

interval [0 150]. In Malaysia, the speed limit regulation on the highway is adopted in this

simulation. The value of density will be 1 if the street condition is congested. The MS is

assumed to be in a cell with the maximum time is 150. These parameters will be the input

of fuzzy environment and process with 10-set of fuzzy rules to perform the location

update result as shown in Table 5.2.

00,050,1

0,150,2

0,250,3

0,350,4

A B C

Dir

ecti

on

dist

ribu

tion

(%)

Direction Schemes

East North West South

108

Table 5.2 The proposed 10-set of fuzzy rules

No. Speed Density ResTime Results

1 Low Low Short No Update

2 Low Low Average No Update

3 Low Average Short No Update

4 Low High Average No Update

5 Average Low Average LU

6 Average Average Average LU

7 High Short No Update

8 High Average LU

9 Fast LU

10 Long LU

The rules that shown in Table 5.2 then constructed in if-then formulation to be

used in fuzzy environment is shown in Figure 5.10.

Figure 5.10 If-then structures in fuzzy environment

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By optimizing the FIS Membership Functions (MFs) with respect to a

performance criterion, the resulting FIS can lead to an optimal solution with respect to

that criterion. Once all the MFs have been properly defined and the FIS reasoning and

defuzzification method are selected, the process to optimize the FIS parameters can begin.

The proposed FIS parameter optimization methodology consists of a set of simple steps,

which includes:

a. Selection of the appropriate fuzzy reasoning and defuzzification method

b. Implementation of the selected fuzzy reasoning and defuzzification method

c. Optimization of the FIS

d. Evaluation of results from the optimization process

In the previous chapter, LU and paging have been calculated. In this chapter, the

all data which record in the previous result then feed to this fuzzy. To obtain the result,

the fuzzy mechanism will work with the threshold limit. Figure 5.11 to Figure 5.13 show

the surface of the combination of input threshold.

Figure 5.11 Surface of residence time and density

110

Figure 5.12 Surface of speed and residence time

Figure 5.13 Surface of speed and density

111

Location update cost is the concern of this simulation result. As stated in the rules,

only five conditions will perform location update and calculate as a cost if:

a. Speed is fast

b. Residence time is long

c. Density is High, Residence time is Average

d. Speed is Average, Density is Average, Residence time is Average

e. Speed is Average, Density is Low, Residence time is Average

The example of the result is presented as shown in Figure 5.14. In this figure, the

result is 1.72 which mean no update activity has been done. The threshold for calculating

a update cost if it value is greater or equal to 5. The overall calculation are presented in

the following section in this chapter

Figure 5.14 An example of result using fuzzy logic

5.4 PERFORMANCE EVALUATION

Two techniques have been done in this thesis to evaluate the performance of mobility

management schemes, numerical results which carry out using simulation and the result

112

using fuzzy technique. In Chapter 4, many findings are presented and here the previous

results are analyzed and compared to fuzzy results. As shown in Table 5.3, three

strategies: cell, balanced-cell and unbalanced-cell using two types mobility models,

random walk and street lane model are presented. For random walk, there are two models

with ¼ and 1/8 directions as shown in Figure 3.5.

Table 5.3 Conventional and fuzzy results comparison

Strategy Mobility Models Conventional Fuzzy-based

Cell Random walk 4-directions 67.5% 44.7%

8-directions 67.6% 44.3%

Street Lanes 66.9% 45.1%

Balanced-cell 4-directions 23.2% 22.6%

8-directions 23.3% 22.1%

Street Lanes 21.5% 20.9%

Unbalanced-cell 4-directions 20.3% 20.0%

8-directions 22.0% 21.1%

Street Lanes 21.0% 20.6%

As shown in Table 5.3, in cell strategy, fuzzy-based has achieved about 20% lower in

signaling cost compared to conventional approach. Otherwise, in balanced-cell and

unbalanced-cell, also all fuzzy-based results outperform the conventional mechanism with

a significant reduction.

5.5 SUMMARY

In this chapter, graph theory is used to evaluate the behavior of user mobility. The

analysis has shown that repeated pattern more activities than non-repeated pattern. Also,

user distribution as the result simulation is evaluated. For both of balanced-cell and

unbalanced-cell, result shows that only one location area is not utilized at location area 7

which is 0%. Later, Fuzzy logic is proposed to evaluate location management operation

using mobility modeling this thesis. All the results were used in the Chapter 4 then

evaluated and compared to fuzzy logic technique. The results of using fuzzy logic

113

technique show that location update cost using fuzzy logic is decreased. The reductions

are varied depended on the strategies implementation. This result validates the optimality

of the implemented fuzzy logic technique and show that this technique outperforms the

conventional mechanisms for the entire tested mobility model.

CHAPTER VI

CONCLUSION

6.1 INTRODUCTION

This chapter concludes the entire research on mobility and location management in

cellular radio network. Geographical user distribution and mobility have an important

effect on cellular network capacity. The mobility of users offer a great flexibility to make

and receive calls anywhere and anytime while in mobile activity as long as the service is

available. To handle and maintain all the mobile communication transaction, mobility

management has played an important task. Mobility management has two tasks: handoff

management and location management.

Location management schemes are essentially based on users' mobility and

incoming call rate characteristics. There are two important tasks in location management:

location update and paging. The location update procedure allows the system to keep

location knowledge more or less accurately in order to find the MS in case of an incoming

call, for example. Location registration also is used to bring the user's service profile near

its location and allows the network to rapidly provide the MS with services. The paging

process achieved by the system consists of sending paging messages in all cells where the

MS could be located.

To evaluate the performance of location management operation, two mobility

models have been developed, Random Walk Mobility Model with a direction threshold

and Street Lane Mobility Model. Random Walk Mobility Model is constructed in two

115

direction variations. The first model has four directions and the other model has eight

directions. In these models, MS can freely move in the service area. In the street lane

model, MS will stay only on the street and has to follow some restriction like no return

and always go forward.

6.2 CONCLUSION

The main goal of the research is to achieve the minimum cost of location management

using mobility management concept in cellular communication system. Result shows that

the performance of the network is affected by the signaling registration; in general the

cost is higher in VLR compared to HLR system. For example, using random walk

mobility in which the MS frequently across the cell or LA border has degraded the

network performance. Simulation results show that the number of MS activities is higher

in the LA border especially in single-cell in HLR compared to others strategies, balanced-

cell and unbalanced-cell. In contrasts among the strategies, the balanced-cell has the least

VLR and HLR updating. This strategy outperforms the other two strategies 22.28% of

registration signaling while for single-cell and unbalanced-cell strategies, the percentage

of registration signaling are 35.16% in and 42.56% respectively. Also, a practical mixed

strategy of LU and Paging has been studied and evaluated. The cost function of the

analysis is counted as a number of signaling activities in LU and paging strategies in

cumulative distribution function. Simulation results showed the performance of LU case:

strategy LU_D gives superior performance (76%) with respect to the other three (LU_B,

LU_C and LU_A) respectively. In the paging case: the result of strategy PG_C gives the

best performance (93%) with respect to paging strategy PG_A and PG_B. Both of these

LU and paging strategy employed larger data set. The strength of these strategies is it

minimizes the cost of calculating the redundant data. Simulation results show that

strategy A, a combination of LU_D and PG_C, give the lower signaling activities with

cumulative distribution function value of 61% with respect to the worst case combination

of strategy D.

116

The results show that LU signaling activities have been reduced in the system.

Mobility models and the proposed service area strategies have also impact the result. The

result of street lane mobility model shows that subscribers are distributed in the center

cluster of simulated balanced and unbalanced location area. Furthermore, the results of

MSs using eight direction random walk mobility is concentrated at certain edge-cluster of

simulated service area of both balanced and unbalanced location area models. These

outcome means that the role of mobility model has influenced to the number of signaling

activities in service area which using in the network. Random walk has shown greater

signaling activities compared to street lane model. This can be influenced as the effect of

subscribers’ mobility behavior within the logical structure of the network. The results of

using fuzzy logic technique show that location update costs are decreased. These results

validate the optimality of the implemented fuzzy logic technique and show that this fuzzy

logic technique outperforms the conventional approach for the entire tested mobility

model.

6.3 FUTURE WORK

For future work and based on the limitation of the simulation, the following are possible

subjects:

a. Applying method that can reduce the signaling overhead on the radio link

produced by location management operation using optimization technique such as

the neural network. The method should consider the current network and obtain

the real subscriber data for the evaluation. In this approach mobility modeling can

be omitted.

b. Study and quantify the effect of the hot spots on the total cost of location

management for and on the overall utilitiztion of cellular network. A detailed

understanding of hotspot can help in conducting more realistic simulations and

enable improved network design.

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APPENDIX A

LIST OF PUBLICATIONS

Journal Rizal Munadi, Mahamod Ismail, Mardina Abdullah & Norbahiah Misran. 2011. Cost

Reduction Strategy in Location Management in Cellular Networks. International Journal on Electronic and Electrical Engineering (IJEEE). In press.

Rizal Munadi, Mahamod Ismail, Mardina Abdullah & Norbahiah Misran. 2011. Location

Management Cost Strategies in Cellular Networks. International Journal Computer Technology Application 2(1): 188-192.

Proceeding Paper

Rizal Munadi, Zainol Abidin Abdul Rashid, & Mahamod Ismail. 2003. An Optimized

Location Management Technique for 3G Wireless Networks: A Brief Review of Literature and Proposed Work. Prosiding Seminar Pelajar Siswazah (SPS 2003), Fakulti Kejuruteraan, Universiti Kebangsaan Malaysia.

Rizal Munadi, Zainol Abidin Abdul Rashid, & Mahamod Ismail. 2004. A Mixed Strategy

of Cost Reduction in Location Management in Cellular Networks. The Sixth Industrial Electronics Seminar (IES 2004).

Rizal Munadi, Zainol Abidin Abdul Rashid, & Mahamod Ismail. 2005. A Signaling Cost

Analysis in Location Management in Cellular Networks. Konferensi Nasional Sistem Informasi (KSNI 2005). Institut Teknologi Bandung.

Rizal Munadi, Zainol Abidin Abdul Rashid, & Mahamod Ismail. 2005. Network

Performance Analysis in Location management Scheme for PCS. International Conference on Instrumentation Communication and Information Technology (ICICI 2005).

Rizal Munadi, Zainol Abidin Abdul Rashid, & Mahamod Ismail. A 2005. Evaluation of

Distance-based Location Update and Sequential Paging in PCS Registration System. Proc. the 7th IEEE Malaysia International Conference on Communications (MICC 2005).

128

Rizal Munadi, Mahamod Ismail & Mardina Abdullah. 2007. Characterization of User Mobility Behavior in Personal Communication Services Network. International Conference on Instrumentation Communication and Information Technology (ICICI 2007), Institut Teknologi Bandung (Indonesia).

Rizal Munadi, Mahamod Ismail & Mardina Abdullah. 2007. The Evaluation of User

Mobility Behavior in Personal Communication Service Network. Proc. of the 5th Student Conference on Research and Development (SCOReD 2007), Bangi (Malaysia). pp. 1-5.

Rizal Munadi, Mahamod Ismail, Mardina Abdullah & Norbahiah Misran. 2007. The

Impact of User Mobility in Personal Communication Service Network. Proc. of the 3rd IMT-GT Regional Conference on Mathematics, Statistics and Applications (RCMSA 2007), Penang. pp. 714-720.

Rizal Munadi, Mahamod Ismail, Mardina Abdullah & Norbahiah Misran. 2011. Location

Management Cost Reduction using Fuzzy Logic in Cellular Radio Network. Accepted in IconSpace2011, Penang, 12-13 July 2011.

129

APPENDIX B

PATTERN INDEX

No. No. No. No. No.1 1 1 1 1 52 1 4 1 4 103 2 3 2 3 154 3 2 3 2 205 4 1 4 12 1 1 1 2 53 1 4 2 1 104 2 3 2 4 155 3 2 3 3 206 4 1 4 23 1 1 1 3 54 1 4 2 2 105 2 3 3 1 156 3 2 3 4 207 4 1 4 34 1 1 1 4 55 1 4 2 3 106 2 3 3 2 157 3 2 4 1 208 4 1 4 45 1 1 2 1 56 1 4 2 4 107 2 3 3 3 158 3 2 4 2 209 4 2 1 16 1 1 2 2 57 1 4 3 1 108 2 3 3 4 159 3 2 4 3 210 4 2 1 27 1 1 2 3 58 1 4 3 2 109 2 3 4 1 160 3 2 4 4 211 4 2 1 38 1 1 2 4 59 1 4 3 3 110 2 3 4 2 161 3 3 1 1 212 4 2 1 49 1 1 3 1 60 1 4 3 4 111 2 3 4 3 162 3 3 1 2 213 4 2 2 1

10 1 1 3 2 61 1 4 4 1 112 2 3 4 4 163 3 3 1 3 214 4 2 2 211 1 1 3 3 62 1 4 4 2 113 2 4 1 1 164 3 3 1 4 215 4 2 2 312 1 1 3 4 63 1 4 4 3 114 2 4 1 2 165 3 3 2 1 216 4 2 2 413 1 1 4 1 64 1 4 4 4 115 2 4 1 3 166 3 3 2 2 217 4 2 3 114 1 1 4 2 65 2 1 1 1 116 2 4 1 4 167 3 3 2 3 218 4 2 3 215 1 1 4 3 66 2 1 1 2 117 2 4 2 1 168 3 3 2 4 219 4 2 3 316 1 1 4 4 67 2 1 1 3 118 2 4 2 2 169 3 3 3 1 220 4 2 3 417 1 2 1 1 68 2 1 1 4 119 2 4 2 3 170 3 3 3 2 221 4 2 4 118 1 2 1 2 69 2 1 2 1 120 2 4 2 4 171 3 3 3 3 222 4 2 4 219 1 2 1 3 70 2 1 2 2 121 2 4 3 1 172 3 3 3 4 223 4 2 4 320 1 2 1 4 71 2 1 2 3 122 2 4 3 2 173 3 3 4 1 224 4 2 4 421 1 2 2 1 72 2 1 2 4 123 2 4 3 3 174 3 3 4 2 225 4 3 1 122 1 2 2 2 73 2 1 3 1 124 2 4 3 4 175 3 3 4 3 226 4 3 1 223 1 2 2 3 74 2 1 3 2 125 2 4 4 1 176 3 3 4 4 227 4 3 1 324 1 2 2 4 75 2 1 3 3 126 2 4 4 2 177 3 4 1 1 228 4 3 1 425 1 2 3 1 76 2 1 3 4 127 2 4 4 3 178 3 4 1 2 229 4 3 2 126 1 2 3 2 77 2 1 4 1 128 2 4 4 4 179 3 4 1 3 230 4 3 2 227 1 2 3 3 78 2 1 4 2 129 3 1 1 1 180 3 4 1 4 231 4 3 2 328 1 2 3 4 79 2 1 4 3 130 3 1 1 2 181 3 4 2 1 232 4 3 2 429 1 2 4 1 80 2 1 4 4 131 3 1 1 3 182 3 4 2 2 233 4 3 3 130 1 2 4 2 81 2 2 1 1 132 3 1 1 4 183 3 4 2 3 234 4 3 3 231 1 2 4 3 82 2 2 1 2 133 3 1 2 1 184 3 4 2 4 235 4 3 3 332 1 2 4 4 83 2 2 1 3 134 3 1 2 2 185 3 4 3 1 236 4 3 3 433 1 3 1 1 84 2 2 1 4 135 3 1 2 3 186 3 4 3 2 237 4 3 4 134 1 3 1 2 85 2 2 2 1 136 3 1 2 4 187 3 4 3 3 238 4 3 4 235 1 3 1 3 86 2 2 2 2 137 3 1 3 1 188 3 4 3 4 239 4 3 4 336 1 3 1 4 87 2 2 2 3 138 3 1 3 2 189 3 4 4 1 240 4 3 4 437 1 3 2 1 88 2 2 2 4 139 3 1 3 3 190 3 4 4 2 241 4 4 1 138 1 3 2 2 89 2 2 3 1 140 3 1 3 4 191 3 4 4 3 242 4 4 1 239 1 3 2 3 90 2 2 3 2 141 3 1 4 1 192 3 4 4 4 243 4 4 1 340 1 3 2 4 91 2 2 3 3 142 3 1 4 2 193 4 1 1 1 244 4 4 1 441 1 3 3 1 92 2 2 3 4 143 3 1 4 3 194 4 1 1 2 245 4 4 2 142 1 3 3 2 93 2 2 4 1 144 3 1 4 4 195 4 1 1 3 246 4 4 2 243 1 3 3 3 94 2 2 4 2 145 3 2 1 1 196 4 1 1 4 247 4 4 2 344 1 3 3 4 95 2 2 4 3 146 3 2 1 2 197 4 1 2 1 248 4 4 2 445 1 3 4 1 96 2 2 4 4 147 3 2 1 3 198 4 1 2 2 249 4 4 3 146 1 3 4 2 97 2 3 1 1 148 3 2 1 4 199 4 1 2 3 250 4 4 3 247 1 3 4 3 98 2 3 1 2 149 3 2 2 1 200 4 1 2 4 251 4 4 3 348 1 3 4 4 99 2 3 1 3 150 3 2 2 2 201 4 1 3 1 252 4 4 3 449 1 4 1 1 100 2 3 1 4 151 3 2 2 3 202 4 1 3 2 253 4 4 4 150 1 4 1 2 101 2 3 2 1 152 3 2 2 4 203 4 1 3 3 254 4 4 4 251 1 4 1 3 102 2 3 2 2 153 3 2 3 1 204 4 1 3 4 255 4 4 4 3

256 4 4 4 4

DirectionsDirections Directions Directions Directions

130

APPENDIX C

PSEDO CODE

Procedure Generate Mobile Station in simulation area (Random Walk, 4 direction)

for MS=1, max(MS) for sim_time=1, max(time) generate MS at random position (x,y) Select direction Case 1 xpos=ms_pos+speed*sim_time*cos(0 degree) ypos=ms_pos+speed*sim_time*sin( 0 degree) Case 2 xpos=ms_pos+speed*sim_time*cos( 90 degree) ypos=ms_pos+speed*sim_time*sin( 90 degree) Case 3 xpos=ms_pos+speed*sim_time*cos( 180 degree) ypos=ms_pos+speed*sim_time*sin( 180 degree) Case 4 xpos=ms_pos+speed*sim_time*cos(270 degree) ypos=ms_pos+speed*sim_time*sin( 270 degree) If xpos, ypos are outside the simulation border the xpos=xpos-1 ypos=ypos-1 Else Store to MS table

Procedure Generate Mobile Station in simulation area (Random Walk, 8 direction)

for MS=1, max(MS) for sim_time=1, max(time) generate MS at random position (x,y) Select direction Case 1 xpos=ms_pos+speed*sim_time*cos(0 degree) ypos=ms_pos+speed*sim_time*sin( 0 degree) Case 2 xpos=ms_pos+speed*sim_time*cos( 45 degree) ypos=ms_pos+speed*sim_time*sin( 45 degree) Case 3 xpos=ms_pos+speed*sim_time*cos(90 degree) ypos=ms_pos+speed*sim_time*sin( 90 degree)

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Case 4 xpos=ms_pos+speed*sim_time*cos(135 degree) ypos=ms_pos+speed*sim_time*sin( 135 degree) Case 5 xpos=ms_pos+speed*sim_time*cos(180 degree) ypos=ms_pos+speed*sim_time*sin(180 degree) Case 6 xpos=ms_pos+speed*sim_time*cos(225 degree) ypos=ms_pos+speed*sim_time*sin(225 degree) Case7 xpos=ms_pos+speed*sim_time*cos(270 degree) ypos=ms_pos+speed*sim_time*sin( 270 degree) Case 8 xpos=ms_pos+speed*sim_time*cos(315 degree) ypos=ms_pos+speed*sim_time*sin( 315 degree) if xpos, ypos are outside the simulation border the xpos=xpos-1 ypos=ypos-1 else Store to MS table

Procedure Data Street lane

for MS=1, max(MS) generate initial random lane_data of 1,2,3 for sim_time=1, max(time) if lane_data=1 then generate lane_data of 1,2 elseif lane_data=2 then generate lane_data of 1,2,3 else generate lane_data of 2,3

Procedure Data Density lane

for MS=1, max(MS) for sim_time=1, max(time) if data_streetlane=3 then density is low elseif data_streetlane=2 then density is average else density is high

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Procedure combination of pattern set Begin combn End combn Procedure MS distance to BS

for MS=1, max(MS) for sim_time=1, max(time) for cells=1, max(cell) locate x, y position measure the distance (x,y) find the minimum distance to BS find RSSI find channel availability select the best channel by optimizing: distance, RSSI and channel

Procedure Pattern table

for MS=1, max(MS) for sim_time=1, max(time) for cells=1, max(cell) select MS pattern

Procedure Fuzzy construct fuzzy environment with 10 rules for sim_time=1, max(time) evaluate fuzzy input(speed, restime, density)