RESOURCE ALLOCATION SCHEME FOR FUTURE USER-CENTRIC

WIRELESS NETWORK

WAHEEDA JABBAR

UNIVERSITI TEKNOLOGI MALAYSIA

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RESOURCE ALLOCATION SCHEME FOR FUTURE USER-CENTRIC

WIRELESS NETWORK

WAHEEDA JABBAR

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Master of Engineering (Electrical)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

SEPTEMBER 2014

iii

Dedicated to my beloved parents, supervisor, seniors and friends.

iv

ACKNOWLEDGEMENT

First of all, I would like to express the highest gratitude to my supervisors, Dr.

Nasir Shaikh Husin and PM. Dr. Sharifah Kamilah Syed Yusof for their invaluable

support, sharing of their philosophy of life and continuous motivation which lead to

the successful completion of this work.

My sincerest appreciation goes to my seniors, Lee Yee Hui, and Tang Jia Wei

for their support and advice throughout my research journey. I would like to thank my

lab fellow Jeevan, and many others who have made this tough journey memorable and

full with laughter.

Last but not least, my warmest regards to my parents and brother for their

seamless caring and moral support that made this journey possible.

v

ABSTRACT

Current communication landscape focuses on the integration of networks,

where these networks exhibit heterogeneous characteristics. The motivation for such

network integration arises the advent of the smart end-user devices and revolutionary

advances in other network components, the telecommunication business models shift

focus from network-centric to the user-centric paradigm. This further dictates that

operators will find the profit windows in increasing the satisfied user pool, which

intuitively may be translated into meeting the user requirements. The shift towards new

paradigm gives birth to several major issues and suitability of the current bandwidth

sharing algorithms is one of them. The research work for the new paradigm is

confined to inter operator and intra operator levels only. The bandwidth allocation at

last mile, i.e. at user-end is not widely addressed. There is a need for an optimal

solution that considers user perceived Quality of Experience (QoE) in bandwidth

allocation at user-end. An optimal solution of this problem will maximize user

satisfaction and increase operator’s revenue. Adapting the economic components of

the existing user satisfaction function will capture the user behaviour more realistically

against multiple parameters. A user perceived QoE-based distributed decision making

algorithm at network level will be deployed at base station and the interdependency

would be captured. The characteristics of involved access technologies i.e. (WLAN,

UMTS) will be confined to capacity and coverage only. MATLAB implementation

is used to proof the concept. The performance gain of the proposed work was

investigated by using various performance evaluation criteria, such as call blocking

probability, operator’s revenue maximization and user satisfaction. The proposed

optimum bandwidth allocation algorithm is compared with non-optimum bandwidth

allocation algorithm in single radio interface as well as in heterogeneous environment.

The obtained results show that optimum bandwidth allocation model maximizes user

satisfaction and operator’s revenue and minimizes number of calls rejection.

vi

ABSTRAK

Landskap komunikasi semasa memberi tumpuan kepada integrasi rangkaian,

di mana rangkaian ini mempamerkan ciri-ciri yang berbeza. Motivasi bagi integrasi

rangkaian sebegini berpunca daripada kemunculan peranti pengguna akhir pintar dan

kemajuan dalam komponen rangkaian yang lain, model perniagaan telekomunikasi

mengalihkan fokus kepada paradigma rangkaian berteraskan pengguna. Ini seterusnya

menetapkan bahawa pengendali akan mendapati keuntungan dalam meningkatkan

bilangan pengguna yang berpuas hati yang secara intuitif boleh diterjemahkan ke

dalam memenuhi keperluan pengguna. Perubahan ke arah paradigma baru melahirkan

beberapa isu utama dan kebolehgunaan algoritma perkongsian jalur lebar semasa

adalah salah satu daripada isu-isu tersebut. Kerja-kerja penyelidikan bagi paradigma

baru ini adalah terbatas hanya kepada antara operator dan intra operator. Peruntukan

jalur lebar pada sambungan akhir, iaitu pada pengguna akhir tidak ditangani secara

meluas. Satu penyelesaian optimum yang mengambil kira Kualiti Pengalaman (QoE)

diperlukan untuk peruntukan jalur lebar pada perspektif pengguna akhir. Penyelesaian

optimum masalah ini akan memaksimumkan kepuasan pengguna dan meningkatkan

keuntungan pengendali. Menyesuaikan komponen ekonomi untuk fungsi kepuasan

pengguna yang sedia ada akan merekod perilaku pengguna yang lebih realistik

terhadap pelbagai parameter. Algoritma untuk membuat keputusan berasaskan QoE di

peringkat rangkaian akan digunakan di stesen pengkalan dan hubungan antara proses

ini direkod. Ciri-ciri teknologi akses yang terlibat (iaitu WLAN dan UMTS) adalah

terhad kepada kapasiti dan liputan sahaja. Model ini diimplementasi dengan MATLAB

untuk membuktikan konsep. Peningkatan prestasi untuk kerja yang dicadangkan telah

disiasat dengan menggunakan pelbagai kriteria penilaian prestasi, seperti penyekatan

panggilan, memaksimumkan hasil pendapatan pengendali dan kepuasan pengguna.

Algoritma jalur lebar optimum yang dicadangkan dibandingkan dengan algoritma

jalur lebar bukan optimum dalam radio antara muka tunggal dan dalam persekitaran

pelbagai. Keputusan menunjukkan model pembahagian jalur lebar optimum berupaya

memaksimakan kepuasan pengguna dan pendapatan operator dan meminimumkan

jumlah pangglian yang ditolak.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF ABBREVIATIONS xv

LIST OF SYMBOLS xviii

1 INTRODUCTION 1

1.1 Future Wireless Paradigm 1

1.1.1 Resource Sharing Dilemma 1

1.2 Background on User Centric Paradigm 1

1.3 Statement of The Study 2

1.4 Research Objectives 3

1.5 Scope of Work 3

1.6 Research Contributions 4

1.7 Thesis Organization 4

2 LITERATURE REVIEW 6

2.1 Introduction 6

2.2 Wireless Local Area Network (WLAN) 6

2.3 Universal Mobile Telecommunication Systems

(UMTS) 7

2.3.1 Network Structure 7

2.4 Heterogeneous Wireless Access Network 8

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2.4.1 Integration of Wireless Network Archi-

tectures 9

2.4.2 Resource Allocation for Heterogeneous

Networks 10

2.4.3 User-Centric Resource Allocation for

Heterogeneous Networks 11

2.4.4 User Perceived QoE based Functions 13

3 RESEARCH METHODOLOGY 15

3.1 Introduction 15

3.2 User-Centric Resource Allocation Approach 15

3.3 User Satisfaction Function 17

3.4 Network Level Optimal Resource Utilization Algo-

rithm 19

3.4.1 The Proposed Solution 19

3.4.2 User Satisfaction Function 20

3.4.3 Network Technology Level 20

3.5 Developing a Mathematical Model 21

3.6 Solving Optimization Problem 21

4 ADAPTED USER SATISFACTION FUNCTION AND

GRAPHICAL MODEL 23

4.1 Introduction 23

4.2 Adapted User Satisfaction Function 23

4.2.1 User Satisfaction Against QoS 25

4.2.2 User Satisfaction Against Service Mone-

tary Cost 25

4.2.3 User Satisfaction Against Power Con-

sumption Cost 26

4.2.4 User Satisfaction Against Handover Cost 27

4.3 Graphical Representation of Heterogeneous Envi-

ronment 28

4.4 Chapter Summary 33

5 NETWORK-CENTRIC BANDWIDTH ALLOCATION

MODEL 35

5.1 Overview of the Model Development 35

5.2 The System Model 35

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5.3 Network-Centric Bandwidth Allocation Model 37

5.3.1 Utility Function of Network Technologies 38

5.3.2 Utility Function of User Satisfaction 38

5.3.3 Utility Function of Network Operator

Profit 39

5.4 Standard Bandwidth Allocation Model 40

5.4.1 Fmincon Solution 42

5.5 Chapter Summary 44

6 IMPLEMENTATION OF BANDWIDTH ALLOCATION

MODEL 45

6.1 Introduction 45

6.2 Bandwidth Allocation in Single Radio Interface 45

6.2.1 Scenario Description 45

6.2.2 Bandwidth Allocation in Under Loaded

Scenario 46

6.2.3 Bandwidth Allocation in Fully loaded

Scenario 52

6.2.4 Bandwidth Allocation in Over loaded

Scenario 55

6.3 Bandwidth Allocation in Heterogeneous Radio

Interface 57

6.3.1 An Overview of Scenario 57

6.3.2 Bandwidth Allocation in Heterogeneous

Environment 58

6.4 Call Admission Control Algorithm 61

6.4.1 Call Admission Control Algorithm in

Under Loaded Scenario 61

6.4.2 Call Admission Control Algorithm in

Fully Loaded Scenario 62

6.4.3 Call Admission Control Algorithm in

Over Loaded Scenario 63

6.5 Call Blocking Probability 64

6.6 Chapter Summary 65

7 CONCLUSION 66

7.1 Conclusion Remarks 66

7.2 Contribution 67

7.3 Suggestions for Future Works 67

REFERENCES 69

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xi

LIST OF TABLES

TABLE NO. TITLE PAGE

1.1 An overview of the mobile communication evolution [1] 2

2.1 IEEE WLAN standard 6

3.1 QoE values [2] 19

6.1 Types of users and their corresponding bandwidth ranges in

bps 46

6.2 Parameter values 47

6.3 Different combination of users generated in five different

combinations 47

6.4 Total optimum values for 80 users in five different

combinations 51

6.5 Optimum values for each user (under flow scenario) 52

6.6 Different combination of users generated 52

6.7 Optimum bandwidth and user utility in different iterations 53

6.8 Total rejected calls and achieved profit in optimum and non

optimum bandwidth allocation 53

6.9 The five different combinations of generated users 55

6.10 Comparison of optimum vs non-optimum results 56

6.11 WLAN user satisfaction, allocated bandwidth, profit and

rejected calls 59

6.12 UMTS user satisfaction, allocated bandwidth, profit, and

rejected calls 59

xii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Sharing of QoE in user-centric network [3] 3

1.2 Overall network operation. 4

2.1 UMTS Network Architecture [4] 8

2.2 Heterogeneous wireless network architecture [5] 9

2.3 Process of bandwidth allocation for multiple services [5] 11

2.4 QoE model by PERIMETER project 12

3.1 Methodology 16

3.2 QoE rating input from a user to the system [6] 18

3.3 QoE measurement from user end to server end [2] 18

3.4 Optimization solvers in MATLAB 2013 21

4.1 User satisfaction against QoS cost 25

4.2 User satisfaction against monetary cost 26

4.3 User satisfaction against aggregated cost 27

4.4 User satisfaction against power consumption and monetary

cost 27

4.5 User satisfaction against handover cost 28

4.6 User satisfaction against QoS and handover cost 28

4.7 Graphical presentation of heterogeneous RATs 29

4.8 Users generated in heterogeneous RATs 29

4.9 Generated users on different intervals for Case 1 30

4.10 Total accepted and rejected FTP, voice, and video user

requests 30

4.11 User satisfaction, total resource utilized and operator’s

revenue 31

4.12 Generated users on different intervals for Case 2 31

4.13 User satisfaction level when some users do not get maximum

bandwidth 32

4.14 Operator’s revenue, total utilized bandwidth and user

satisfaction level against allocated bandwidth 32

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4.15 User satisfaction level against increased number of users

generated 33

4.16 Operator’s revenue, total utilized resources and user

satisfaction level against increased number of users 33

5.1 Bandwidth allocation for an MT in heterogeneous network 36

5.2 Typical work flow 37

5.3 Network coverage of different wireless access technologies 38

5.4 Optimization Toolbox 43

5.5 Optimization Solver Returned Message 44

6.1 Non-optimum allocated bandwidth 48

6.2 Optimum allocated bandwidth 48

6.3 Non-Optimum user satisfaction 49

6.4 Optimum user satisfaction 49

6.5 Non-optimized network profit vs optimum network profit

against different user types 49

6.6 Total non-optimized network profit vs total optimum network

profit 50

6.7 Optimum allocated bandwidth for 80 users in different

combinations 50

6.8 Optimum user satisfaction for 80 users in different

combinations 51

6.9 Optimum network profit for 80 users in different combina-

tions 51

6.10 Optimum allocated bandwidth for 102 users in different

combinations 53

6.11 Optimum network profit for 102 users in different

combinations 54

6.12 Optimum user satisfaction for 102 users in different

combinations 54

6.13 Optimum allocated vs non-optimum allocated bandwidth 54

6.14 Optimum vs non-optimum user satisfaction 55

6.15 Rejected calls by user types in optimum and non-optimum

bandwidth allocation 57

6.16 Optimum bandwidth allocated (WLAN AP) 59

6.17 Optimum bandwidth allocated (UMTS BTS) 60

6.18 Optimum user satisfaction (WLAN AP) 60

6.19 User satisfaction (UMTS BTS) 60

6.20 Monotonically increasing profit(WLAN AP) 61

6.21 Monotonically increasing profit(UMTS BTS) 61

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6.22 Call admission control algorithm 62

6.23 Calls shifted by WLAN AP to UMTS BTS 63

6.24 Calls by user types shifted by WLAN to UMTS 64

6.25 Average user satisfaction of admitted calls by UMTS BTS 64

6.26 Call blocking probabilities 65

xv

LIST OF ABBREVIATIONS

3G – 3rd Generation

4G – Fourth Generation

ANDSF – Access Network Discovery and Selection Function

AHP – Analytical Hierarchy Process

ACR – Absolute Category Rating

AP – Access Point

ABC – Always Best Connection

BS – Base Station

BTS – Base Transceiver Station

CBR – Constant Bit Rate

CCK – Complementary Code Keying

CDMA – Code Division Multiple Access

CDF – Cumulative Density Function

CN – Core Network

DBPSK – Differential Binary Phase Shift Keying

DQPSK – Differential Quadrature Phase Shift Keying

DSSS – Direct Sequence Spread Spectrum

FHSS – Frequency Hopping Spread Spectrum

FDD – Frequency Division Duplexing

FTP – File Transfer Protocol

GPRS – General Packet Radio Service

GSM – Global system for Mobile Communication

GGSN – Gateway GPRS Support Node

IP – Internet Protocol

IrDA – Infrared Data Association

IWU – inter-working unit

ISDN – Integrated Services Digital Network

IMS – IP (Internet Protocol) Multimedia Subsystem

xvi

JCAC – Joint Call Admission Control

LAN – Local Area Network

MT – Mobile Terminal

MS – Mobile Station

MN – Mobile Node

MAUT – Multi-Attribute Utility Theory

MOS – Mean Opinion Score

MNB – (Measuring Normalizing Blocks

Node-Bs – UMTS Base Stations

NRT – Non-Real Time

OSI – Open System Interconnection

OFDM – Orthogonal Frequency Division Multiplexing

PS – Packet Switched

PSQM – Perceptual Speech Quality Measure

PESQ – Perceptual Evaluation of Speech Quality

QoE – Quality of Experience

QoS – Quality of Service

RNSs – Radio Network Subsystems

RNCs – Radio Network Controllers

RANS – Radio Access Network Station

RAN – Radio Access Network

RAN – Radio Access Network

RT – Real Time

RAT – Radio Access Technology

RATs – Radio Access Technologies

RSS – Received Signal Strength

SMART – Smart Multi-Attribute Rating Technique

SGSN – Serving GPRS Support Node

SRNS – Serving Radio Network System

SDF – Satisfaction Degree Function

TDD – Time Division Duplexing

UMTS – Universal Mobile Telecommunications System

UE – User Equipment

UTRAN – UMTS Terrestrial Radio Access Network

VBR – Variable Bit Rate

xvii

WCDMA – Wide-band Code Division Multiplexing

WLAN – Wireless Local Area Network

WMAN – Wireless Metropolitan Area Network

WLAN – Wireless Local Area Network

xviii

LIST OF SYMBOLS

γnk– Amount of bandwidth allocated to n number of users of type k

lγ – Lower bound of the variable γ

uγ – Upper bound of variable γ

Bt – Current bandwidth capacity of network technology

β – A parameter that controls to sensitivity of allocated bandwidth

Q(γnk) – Sum of unit resource cost incurred by network technology

Bαω(x) – Vector of total allocated bandwidth by network ω in coverage

area α k type of a user ,i.e. Excellent, Good, Fair

γmin – Minimum bandwidth allocation limit

γmax – Maximum bandwidth allocation limit

P (γnk) – Network profit against allocated bandwidth γ to n number of

users of type k

π – Resource revenue attained from service consumer

µ – Maximum user utility threshold

Uω – Utility of RAN ω

Cj – Capacity of network j

Oj – Coverage area of network j

µ0 – User private valuation

CHAPTER 1

INTRODUCTION

1.1 Future Wireless Paradigm

Current communication landscape focuses on the integration of networks,

where these networks exhibit heterogeneous characteristics. Due to advent of the

smart end-user devices and revolutionary advances in other network components,

the telecommunication business models shift focus from network-centric to the user-

centric paradigm. This business model dictates that operators should maximize

satisfied user pool, which intuitively may be translated into meeting the user

requirements, in order to increase their profits [2, 5, 7, 8]. On the other hand, a

single network access technology may not be able to cope with the bandwidth hungry

applications of the future. This forms the basis for convergence of heterogeneous

wireless access technologies [9,10]. This convergence gives birth to a resource sharing

dilemma.

1.1.1 Resource Sharing Dilemma

Integrated technologies are based on differentiation of multiple parameters

such as medium access, network capacity, network coverage, physical resources, core

network and radio network [3]. To further elaborate the resource sharing dilemma,

Table 1.1 highlights the parameters on which one technology is differentiated from

another. These diverse technologies create a big challenge for a network operator to

share resources efficiently.

1.2 Background on User Centric Paradigm

Current practices in the telecommunication business still tie the users to a

single operator even though there are many operators in the market. The users tend to

manually combine their subscriptions to multiple operators in order to take advantage

of different offers that are suited for a variety of services.

2

Table 1.1: An overview of the mobile communication evolution [1]

Gen Technology Frequency Throughput Coverage Muxing

2G GSM/TDMA 200KHz 14.4Kbps 35km TDMA/

CDMA

2.5G GSM/GPRS 800MHz (Europe) 14.4Kbps 35km TDMA/

1900MHz (US) CDMA

3G UMTS/WCDMA 15 MHz - 20MHz 2Mbps 2km CDMA

4G LTE/WiMAX > 100MHz 200Mbps 10km CDMA/

OFDMA

BB WLAN 2.4GHz - 5GHz (54,11,600)Mbps 100m OFDM/

DSSS

For example, a user might hold two SIM cards/phones from two different

operators, one of which provides a flat-rate national calling plan while the other

provides low cost, high-quality international calling with pay-as-you-go option.

Extending this example to a case where there are a large number of operators with

a multitude of service options and offers in future all-IP telecommunication networks,

manual handling of such multi-operator service combinations is clearly tedious and

impractical for the user [3].

In its most generic sense, the user-centric view in telecommunications

considers that the users are free from subscription to any one network operator and

can instead dynamically choose the most suitable transport infrastructure from the

available network providers for their terminal and application requirements. In this

approach, the decision of interface selection is delegated to the mobile terminal

enabling end users to exploit the best available characteristics of different network

technologies and network providers, with the objective of increased satisfaction [3].

In user-centric networking paradigm for future telecommunication networks,

the users not only make network-selection decisions based on their local Quality of

Experience (QoE) evaluation but also share their QoE evaluations with each other for

increased efficiency and accuracy in network selection, as depicted in Figure 1.1 [3].

1.3 Statement of The Study

In user-centric paradigm, the users will have short term contracts with the

operators and utilizes much smaller time quanta each time the users use the network

[2, 7, 9–12] Therefore the applicability of current bandwidth allocation algorithms for

the new envisioned paradigm is a big question. Researchers have suggested a lot of

good works on bandwidth allocation dilemma in user-centric paradigm [2,9,11,13,14].

However, these algorithms are confined to inter-operator and intra-operator levels only.

The bandwidth allocation at last mile, i.e. at user end is not widely addressed. There

is a need for an optimal solution that considers user perceived QoE in bandwidth

allocation at last mile in downlink. An optimal solution of this problem will definitely

maximize user satisfaction and increase operator’s revenue at the same time.

3

Figure 1.1: Sharing of QoE in user-centric network [3]

1.4 Research Objectives

The research work focuses on the envisioned user-centric telecommunication

paradigm, which will influence operators to enhance their profit windows by increasing

the satisfied user pool. The aforementioned facts motivate us to carry out the research

on modeling the decision instances of resource allocation decisions at the network

technology level. In order to achieve the goal, the sub-objectives are as below

i. To adapt the existing user satisfaction function and add an economic

component for the function in order to measure user satisfaction more

realistically.

ii. To optimize the resource allocation (at last mile) by considering the

modified user satisfaction function (i.e. user perceived QoE) for user-centric

telecommunication paradigm.

1.5 Scope of Work

The scope of the work is confined to radio resource allocation decisions at

network technology level by considering single radio interface and heterogeneous

radio interface environment. The overall system for network operation is depicted

in Figure 1.2. Where the interdependency of different parameters is captured to

make resource allocation decision at network level. The algorithm considers user

perceived QoE and it is deployed at base station. In this research, without loss of

generality, we assume only two access technologies, i.e. Wireless Local Area Network

(WLAN) and Universal Mobile Telecommunications System (UMTS). We solve the

resource allocation problem that are formulated using MATLAB, by utilizing its linear

programming optimization solver. The performance gain of the proposed work is

4

investigated by using three performance evaluation criteria, which are call blocking

probability, profit maximization and user satisfaction.

Figure 1.2: Overall network operation.

1.6 Research Contributions

We formulated a new resource allocation problem specifically for upcoming

user-centric telecommunication infrastructure. We restrict our work only to bandwidth

allocation. The problem, when solved, gives optimal bandwidth allocation to all,

or majority of users, while minimizing call-blocking, achieves high user satisfaction

levels, and maximizing operator profits at the same time. In the problem formulation,

the user satisfaction function is made more realistic by adding a cost component.

1.7 Thesis Organization

This thesis is divided into seven chapters; introduction, literature review,

research methodology, adapted user satisfaction function and front view of the model,

standard bandwidth allocation model development, implementation of bandwidth

allocation model and lastly the conclusion chapter.

The first chapter includes the introduction on future wireless network paradigm

in which future user-centric environment is explained along with resource sharing

5

issues, research background on user-centric paradigm, research objectives, limitations

of the proposed system as well as the contributions of this research work.

Second chapter contains the theoretical background and critical reviews on

current and prior works. The chapter contains introduction to UMTS cellular network

and WLAN data network. The architecture of the two network technologies are

briefly discussed along with introduction to interworking of these two network access

technologies. Resource allocation approaches in heterogeneous and user-centric

environment are reviewed in detail. We also discuss the QoE-based user satisfaction

function.

Methodology applied for the formulation of the optimized resource utilization

problem, as well as the description of optimization tools and mathematical model

design for solving the optimization problem, are included in Chapter 3.

Chapter 4 is divided into two sections. First section describes adapted user

satisfaction function, second section discusses front view of the model to help in

understanding the whole concept of heterogeneous wireless resource sharing.

System model, bandwidth allocation model and standard bandwidth allocation

model is covered in Chapter 5.

The sixth chapter describes the implementation procedures of the proposed

mathematical model presented in Chapter 5. Experimental results and system

performance analysis in terms of user satisfaction, call blocking probability and

operator profit maximization are also discussed in Chapter 6.

The last chapter concludes the presented work and gives suggestions for future

works.

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