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Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

Uplink Interference Management of High Bit Rate

Users in Evolved WCDMA

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Samuel Axelsson

LITH-ISY-EX-3706-2005

Linköping 2005

Department of Electrical Engineering Linköpings tekniska högskolaLinköpings universitet Linköpings universitetSE-581 83 Linköping, Sweden 581 83 Linköping

Uplink Interference Management of High Bit Rate

Users in Evolved WCDMA

Examensarbete utfört i Kommunikationssystem

vid Tekniska högskolan i Linköpingav

Samuel Axelsson


Handledare: Erik Geĳer Lundin

isy, Linköpings universitet

Gunnar Bark

Ericsson Research, Linköping

Examinator: Fredrik Gunnarsson

isy, Linköpings universitet

Linköping, 3 June, 2005

Avdelning, Institution

Division, Department

Division of Automatic ControlDepartment of Electrical EngineeringLinköpings universitetS-581 83 Linköping, Sweden

Datum

Date

2005-06-03

Språk

Language

� Svenska/Swedish

� Engelska/English

�

⊠

Rapporttyp

Report category

� Licentiatavhandling

� Examensarbete

� C-uppsats

� D-uppsats

� Övrig rapport

�

⊠

URL för elektronisk version

http://www.control.isy.liu.se

ISBN

—

ISRN


Serietitel och serienummer

Title of series, numberingISSN

—

Titel

TitleInterferenshantering med förbättrad WCDMA-upplänk för användare med högdatatakt

Uplink Interference Management of High Bit Rate Users in Evolved WCDMA

Författare

AuthorSamuel Axelsson

Sammanfattning

Abstract

The WCDMA air interface, used in the third generation mobile communicationsystems, is currently being evolved to improve the uplink, i.e. the radio links car-rying traffic from the mobile user to the fixed network. An enhanced uplink conceptis being developed to meet the expected needs from future applications like mul-timedia and video-streaming. This thesis studies interference management whenhigh bit rates are introduced in the enhanced uplink. The study is performedthrough theoretical assessments and simulations using WCDMA system simula-tors.

An optimization scheme using a basic system throughput based schedulingis derived to attain a theoretical assessment of bit rate limits. The throughputoptimization is achieved at the expense of user-experienced fairness. Users locatedon cell coverage area overlap show to be most complicated to manage.

The need for interference management is primary when the network deploy-ment consists of small cells while coverage requirements are most essential whenthe cell size increases. By exploiting the benefits of directional antennas the an-tenna tilt can be tuned to increase performance resulting in increased bit rates,increased system throughput and increased resource efficiency. The improvementsare attained without trade-offs and the different components of the study concurunanimously.

Nyckelord

Keywords 3G, WCDMA, EUL, E-DCH

Abstract

The WCDMA air interface, used in the third generation mobile communicationsystems, is currently being evolved to improve the uplink, i.e. the radio linkscarrying traffic from the mobile user to the fixed network. An enhanced uplinkconcept is being developed to meet the expected needs from future applicationslike multimedia and video-streaming. This thesis studies interference managementwhen high bit rates are introduced in the enhanced uplink. The study is performedthrough theoretical assessments and simulations using WCDMA system simula-tors.

An optimization scheme using a basic system throughput based schedulingis derived to attain a theoretical assessment of bit rate limits. The throughputoptimization is achieved at the expense of user-experienced fairness. Users locatedon cell coverage area overlap show to be most complicated to manage.

The need for interference management is primary when the network deploy-ment consists of small cells while coverage requirements are most essential whenthe cell size increases. By exploiting the benefits of directional antennas the an-tenna tilt can be tuned to increase performance resulting in increased bit rates,increased system throughput and increased resource efficiency. The improvementsare attained without trade-offs and the different components of the study concurunanimously.

v

Acknowledgements

First of all I would like to thank the LinLab research group at Ericsson for givingme the opportunity to conduct my master thesis there. I especially want to thankmy examiner Fredrik Gunnarsson for always taking the time to listen and helpme with questions and guidance. I would also like to thank my supervisors Gun-nar Bark and Erik Geĳer Lundin for all support and Eva Englund and Ke WangHelmersson for helping and encouraging me.

I would finally like to thank family and friends for all the support and reassurancegiven during this spring, especially Anna for picking up the pieces after a hardday’s work.

Linköping, June 2005Samuel Axelsson

vii

Abbreviations

3G 3rd Generation mobile communication system3GPP 3rd Generation Partnership ProjectACK AcknowledgementARQ Autonomic Repeat RequestBLER Block Error RateBPSK Binary Phase Shift KeyingCDF Cumulative Distribution Function, see Appendix CCDMA Code Division Multiple AccessCIR Carrier to Interference RatioCTIR Carrier to Total Interference RatiodB Decibel, see Appendix AdBd Decibel with a dipole antenna reference, see Appendix AdBi Decibel with an isotropic antenna reference, see Appendix AdBm Decibel with respect to 1 mW, see Appendix AdBW Decibel with respect to 1 W, see Appendix ADCH Dedicated ChannelDS-CDMA Direct Sequence CDMAE-DCH Enhanced Dedicated ChannelEUL Enhanced UplinkFTP File Transfer ProtocolGPRS General Packet Radio ServiceGSM Global System for Mobile communicationsHARQ Hybrid Autonomic Repeat RequestHSDPA High-Speed Downlink Packet AccessIS-95 Interim Standard 95kbps Kilobit per secondMb MegabitMbps Megabit per secondMB MegabyteMcps Megachips per secondMHz Megahertz

ix

x

NACK Negative AcknowledgementQoS Quality of ServiceRAN Radio Access NetworkRBS Radio Base StationRNC Radio Network ControllerSHO Soft HandoverTCP Transport Control ProtocolTDMA Time Division Multiple AccessTTI Transmission Time IntervalUMTS Universal Mobile Telecommunications ServicesWCDMA Wideband Code Division Multiple AccessWRAN WCDMA Radio Access Network

Contents

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4.1 WCDMA Capacity . . . . . . . . . . . . . . . . . . . . . . . 31.4.2 Resource Efficiency . . . . . . . . . . . . . . . . . . . . . . . 31.4.3 Load Estimation . . . . . . . . . . . . . . . . . . . . . . . . 4

1.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Third Generation Mobile Communication System 5

2.1 Wideband Code Division Multiple Access . . . . . . . . . . . . . . 52.2 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Soft and Softer Handover . . . . . . . . . . . . . . . . . . . 92.3 Enhanced Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.1 Short Transmission Time Interval . . . . . . . . . . . . . . . 102.3.2 Hybrid ARQ with Soft Combining . . . . . . . . . . . . . . 112.3.3 Fast Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Theoretical Assessments 13

3.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.1.1 Multipath Propagation . . . . . . . . . . . . . . . . . . . . . 153.1.2 Shannon’s Theorem . . . . . . . . . . . . . . . . . . . . . . 16

3.2 Soft and Softer Handover . . . . . . . . . . . . . . . . . . . . . . . 173.3 System Throughput Optimization . . . . . . . . . . . . . . . . . . 18

3.3.1 Equal Background Noise . . . . . . . . . . . . . . . . . . . . 193.3.2 Optimization Problem . . . . . . . . . . . . . . . . . . . . . 203.3.3 Linearity and Convexity . . . . . . . . . . . . . . . . . . . . 203.3.4 Equal Maximum Noise Rise . . . . . . . . . . . . . . . . . . 203.3.5 Nonlinear Optimization Problem . . . . . . . . . . . . . . . 213.3.6 Quadratic Optimization Problem . . . . . . . . . . . . . . . 21

xi

xii Contents

4 Simulation Models 27

4.1 Radio Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . 274.1.1 Distance Attenuation . . . . . . . . . . . . . . . . . . . . . 274.1.2 Shadow Fading . . . . . . . . . . . . . . . . . . . . . . . . . 284.1.3 Multipath Fading . . . . . . . . . . . . . . . . . . . . . . . . 284.1.4 Antenna Gain . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Antenna Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.3 Network Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . 294.4 Static Simulation Models . . . . . . . . . . . . . . . . . . . . . . . 29

4.4.1 Shadow Fading . . . . . . . . . . . . . . . . . . . . . . . . . 304.4.2 Multipath Fading . . . . . . . . . . . . . . . . . . . . . . . . 30

4.5 Dynamic Simulation Models . . . . . . . . . . . . . . . . . . . . . . 314.5.1 Enhanced Uplink . . . . . . . . . . . . . . . . . . . . . . . . 314.5.2 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . 314.5.3 Soft and Softer Handover . . . . . . . . . . . . . . . . . . . 314.5.4 Logging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.5.5 Hybrid ARQ with Soft Combining . . . . . . . . . . . . . . 324.5.6 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.5.7 G-RAKE Receiver Model . . . . . . . . . . . . . . . . . . . 32

5 Simulation Results 33

5.1 Path Gain Map Generation . . . . . . . . . . . . . . . . . . . . . . 335.1.1 Path Gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.1.2 Path Gain Requirement . . . . . . . . . . . . . . . . . . . . 355.1.3 Relative Path Gain . . . . . . . . . . . . . . . . . . . . . . . 355.1.4 Ratio of Hazardous Bins . . . . . . . . . . . . . . . . . . . . 365.1.5 Neighbor Cell Resource Consumption . . . . . . . . . . . . 37

5.2 Dynamic Traffic Simulations . . . . . . . . . . . . . . . . . . . . . . 405.2.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 405.2.2 System Capacity . . . . . . . . . . . . . . . . . . . . . . . . 415.2.3 Antenna Tilt . . . . . . . . . . . . . . . . . . . . . . . . . . 435.2.4 System Performance . . . . . . . . . . . . . . . . . . . . . . 445.2.5 General Performance . . . . . . . . . . . . . . . . . . . . . . 45

5.3 Results Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . 475.3.1 Theoretical Assessments and Dynamic Simulations . . . . . 475.3.2 Static and Dynamic Simulations . . . . . . . . . . . . . . . 48

6 Conclusions 49

6.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Bibliography 51

A Decibel 53

A.1 dB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53A.2 dBW and dBm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53A.3 dBi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53A.4 dBd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

B Taylor Expansion of the Objective Function 54

C Cumulative Distribution Function 55

D Dynamic Simulation Results 56

xiv Contents

Chapter 1

Introduction

As telecommunications are becoming more and more important, so are the re-quirements on available services. Mobile communications of today offer wirelesscommunications beyond accustomed speech services. This chapter presents an in-troduction to the interference management problem which is the focus of the thesis.The research approach and other work related to the problem are also presented inthis chapter.

1.1 Background

The evolvement of mobile telecommunication systems has come a long way sincethe first generation mobile communication system (1G). Even though 1G showedpoor stability, coverage and sound quality the interest in mobile communicationswas evident. The capacity of 1G was limited by the analogue technology em-ployed by the systems and when 2G was introduced using digital technology theimprovements were apparent. Coverage, stability and security capacities were allincreased, at the same time more users could be served and data services becameavailable. Examples of 2G systems are the Global System for Mobile communi-cations (GSM) and Interim Standard 95 (IS-95) adopted in Europe and in theUnited States respectively. 2G systems of today are pushed to their limits usingtechniques like General Packet Radio Service (GPRS), offering higher data ratesand thus supporting transmission of low resolution photos and limited multimediaapplications.

In order to satisfy the expected needs from future applications like multimediaand video-streaming the third generation mobile communication system (3G) willreplace its predecessors. The 3G system used in Europe is called Universal MobileTelecommunications Services (UMTS) and a similar system called CDMA2000 isused in the United States. The air interface used in UMTS is Wideband Code-Division Multiple Access (WCDMA) [1]. The first fully commercialized WCDMAservice was operational in 2001 and since then an ongoing evolvement has takenplace to increase resource utilization. WCDMA Release 5 [3] introduced the high-speed downlink packet access (HSDPA) to improve downlink capacity, i.e. the

1

2 Introduction

radio links carrying the network-to-mobile traffic. The following release, WCDMARelease 6, offers the natural complement the enhanced uplink (EUL), i.e. the radiolinks carrying the mobile-to-network traffic.

The EUL standard is being developed within the telecommunications standard-ization body 3rd Generation Partnership Project (3GPP). The aim is to reducedelays, to improve capacity and feasible data rates up to 4 Mbps in the uplink.Data rates up to 4 Mbps bring different aspects to the cell1 planning compared tolower data rates allowed in previous releases. Users in a WCDMA network shareavailable resources and granting a user a high bit rate also means allowing it toutilize most of a cells resources. The problem becomes even more complicated dueto tight interference coupling between the cells in the network. Utilizing resourcesin one cell can also result in consumption of radio resources in neighboring cells ifuser location is unfortunate, e.g. on the boundary between two cells.

1.2 Problem Statement

The master thesis assignment is to study the consequences of introducing highuplink bit rates in WCDMA networks. Under what circumstances does the net-work deployment introduce significant bit rate limitation and how can interferenceissues be managed to attain an efficient system? What bit rates can actually beachieved in specific network scenarios without compromising network stability andcoverage?

The aim is to show that cell planning options like cell radius and antenna tiltcan be exploited to increase the system efficiency and achieve the EUL conceptgoals.

1.3 Research Approach

In order to answer the stated problem the following research approach is taken.

• Theoretical assessments of bit rate limitations of a system throughput basedscheduling for a simple network with the aim to bring intuition to scenarioswhere certain network deployments can be unfavorable.

• Path gain map generation of specific network deployments with different cellradius and antenna tilt. More realistic scenarios based on the derived net-work deployments from the theoretical assessments can thereby be studied.

• The derived deployments are studied in dynamic simulations with a moreadvanced scheduling than the basic throughput based scheduling. Dynamicsimulations with more realistic and advanced scheduling captures the dy-namics of a WCDMA network.

The three different phases capture different aspects of the problem as they growmore complex. The theoretical assessments describes the underlying principle andadditional layers of realism are added when conducting the simulations.

1A cell refers to the area being served by a certain antenna.

1.4 Related Work 3

1.4 Related Work

Since the EUL is a fairly new concept there are not many studies yet published.Most studies on WCDMA interference and capacity in the literature assume trafficgenerated by low bit rate mobile users, uniformly distributed within a cell. This isnot the case for high bit rate users, instead a scenario, where only a few potentialhigh bit rate users exist in each cell, is more relevant.

1.4.1 WCDMA Capacity

Dehghan et al. [7] and Owen et al. [16] study capacity and planning issues inWCDMA. The article in [7] discusses the importance of more complex simulationscompared to simplified theoretical approaches when optimizing a WCDMA net-work effectively. Theoretical approaches can be used to dimension and plan thenetwork but simulations are needed to optimize performance. This motivates theresearch approach described in Section 1.3 containing both theoretical aspects de-scribing the general principles and simulations verifying the derived theories. Thearticle in [16] focuses on capacity limited by intercell and intracell interference,i.e. interference caused by users in the own cell and interference caused by usersoutside the own cell respectively. A WCDMA network has a tight interferencecoupling between cells and the F-factor is derived to model this. The F-factoris the ratio between own cell interference and total cell interference. This ratiois crucial when high bit rates are adopted since a single user on the boundarybetween two cells can cause significant interference in neighboring cells.

Kim et al. [11] and Lei et al. [13] study the interference-based capacity in aCDMA cellular system. The second article focuses on the dependency betweenlocation and other-cell interference. It is concluded that prioritizing users close tothe center of the cell over users close to the boarder of the cell will result in highersystem capacity since users on the boundary generate more noise in neighboringcells. The expected conclusion results in a trade-off between capacity and fairness.The article does not consider high bit rates specifically but this will be even moreexplicit when applied to high bit rates. In order to achieve a theoretical assessmentof the bit rate limit the article proposes a throughput based scheduling.

1.4.2 Resource Efficiency

Oh et al. [14] and Zhang et al. [19] study the optimal resource allocation for uplinkdata services. Only a single cell is studied and the intercell interference aspect istherefore missing. The scheduling policy for users on the boundary between cellswill be crucial when using high bit rates. In the single cell case the articles con-clude that in order to achieve maximum system throughput, the optimal resourceallocation distributes maximum transmission power to the user with the most fa-vorable path gain and then the user with second best path gain and so on until theresource limit is reached, i.e. maximum noise rise is reached or the case when allusers in the cell transmit with maximum power. This single cell optimal allocationis not applicable in a multicell system due to intercell interference.

4 Introduction

1.4.3 Load Estimation

Geĳer et al. [8, 9] study uplink load estimation in WCDMA, which holds greatchallenges and is equally important to be able to utilize system resources efficiently.In this thesis theoretical limits are in focus rather than exact estimation which iswhy the true load will be assumed known both in the theoretical assessments andin the simulations even though not being available in reality.

The notation adopted in this thesis is similar to the one used in [8, 9]. Thenotation might seem cumbersome at first but it holds several advantages.

1.5 Thesis Outline

The remaining part of the thesis is arranged as follows. Chapter 2 gives a briefintroduction to WCDMA and especially to the enhanced uplink concept. Funda-mental techniques used in a CDMA system are described. The following chapter,Chapter 3, contains theoretical assessments with the aim to bring intuition to sce-narios where the network deployment can be unfavorable with respect to high bitrate users. Chapter 4 describes both the simulation models used for the path gainmap generation and the dynamic simulations since they are similar in many ways.Simulation results from the static and the dynamic simulations are presented inChapter 5. Finally conclusions and future work are presented in Chapter 6.

Chapter 2

Third Generation Mobile

Communication System

UMTS is the leading 3G technology today and the UMTS network use WidebandCode Division Multiple Access (WCDMA) as its air interface. WCDMA differsfundamentally from the Time Division Multiple Access (TDMA) air interface in2G networks like GSM where a user is granted transmission during a specific timeinterval and using a certain frequency spectrum. The WCDMA radio interfaceinstead allows the users to utilize the entire available frequency bandwidth at thesame time. The WCDMA system utilizes the available bandwidth more efficientlycompared to 2G systems like GSM and thereby increasing capacity, making higherbit rates available and allowing a higher number of users to be served. This chapterpresents an overview of the WCDMA air interface followed by an introduction tothe enhanced uplink concept. A more detailed description of WCDMA can be foundin [4, 10, 12, 15, 17].

2.1 Wideband Code Division Multiple Access

The users in a WCDMA system are neither separated in time nor in frequency,instead they are separated by applying a scheme called Direct Sequence CodeDivision Multiple Access (DS-CDMA). On the transmitting side the user data ismultiplied with a spreading code that has n times higher chip rate1 than the rateof the user data, resulting in a spread signal in the frequency spectrum, hence thename spreading code. The ratio between the spreading code chip rate and thedata rate is called the spreading factor, earlier denoted as n. A chip rate of 3.84Mcps results in a carrier bandwidth of approximately 5 MHz which is wider thanthe bandwidth of about 1 MHz used in CDMA, hence the additional wideband inWCDMA.

1Chip rate is the bit rate of the spreading code.

5

6 Third Generation Mobile Communication System

Example 2.1: Maximum data rate per spreading code

A spreading factor of n = 8, will result in a maximum data rate of 480 kbpsassuming binary phase shift keying (BPSK) modulation. In a BPSK system thephase of the carrier signal is switched between two phase values corresponding tothe binary symbols 1 and 0 respectively.

3.84 Mcps8

= 480 kbps

Multiple simultaneous codes must be used to attain bit rates greater than this,assuming the spreading factor is not lowered instead. The lower the spreadingfactor, the fewer available spreading codes since the spreading codes are orthogonalwith respect to each other.

On the receiver side the spread signal is again multiplied with the same uniquespreading code and the original data sequence can be retrieved, this procedure istherefore called despreading. The spreading and despreading procedure is illus-trated in Figure 2.1.

+1

+1

+1

+1

+1

-1

-1

-1

-1

-1

Spreading

Despreading

Data

Spreading Code

Data × Code

Code

Data

Chip

Symbol

Figure 2.1. Spreading and despreading procedure. The spreading factor, n, is set to 8.Courtesy of Holma et al. [10].

If the spread signal is despread using the same spreading code as the original,the data sequence is retrieved. If any other spreading code orthogonal to theoriginal is used to despread the result will be perceived as noise or interference.The procedure is illustrated in Figure 2.2.

Since all other users will be perceived as interference, see Figure 2.3, care mustbe taken so that the interference caused by other users does not exceed the ownsignal energy. If that is the case the own signal will drown in interference anddisable the data retraction in the despreading procedure.

A fast power control, permitting users to transmit with a certain transmissionpower, is employed to control the interference caused by each user. The power

2.1 Wideband Code Division Multiple Access 7

+1

+1

+1

+1

+1

-1

-1

-1

-1

-1

Own Data

Code

Own Signal

Code

Despreaded Data

Integrated Data

Other Signal

+1

-1Integrated Data

+1

-1

+1

-1

Figure 2.2. Despreading with the original spreading code retrieves the data whiledespreading using another spreading code than the original, or the original spreadingcode used on another signal, results in noise. Courtesy of Holma et al. [10].

Spreading

Despreading

Power

Frequency Frequency

Frequency Frequency

Power

Power

Power

Figure 2.3. Multiple users are able to transmit using the same frequency band. Theown data can be retrieved as long as interference produced by other users do not exceedthe own signal energy. The shaded bars in the figure represent different user signals.Courtesy of Holma et al. [10].


control is essential to handle the near-far problem and also compensates for dif-ferences and variations in the radio channel. The near-far problem appears whentwo users are transmitting with equal power but one user experiences considerablylower path gain, i.e. higher signal attenuation. This user will then drown in in-terference caused by the user with high path gain. The power control can adjusttransmission power so this problem is reduced.

2.2 Network Architecture

The UMTS network architecture consists of two major elements, the core networkand the WCDMA Radio Access Network (WRAN). The core network routes trafficto external networks like the Internet while the WCDMA Radio Access Networkhandles the radio traffic. The WRAN consists of the mobile users which connectto the Radio Base Stations (RBS) which are controlled by the Radio NetworkControllers (RNC), see Figure 2.4. The RBS handles all radio signalling to themobile users while the RNC with its more central role handles communicationwith the core network. Resource allocation like user-service requests is shared bythe RNC and the RBS.

RNC

RBS

User

RBS

Core Network

Figure 2.4. The UMTS network architecture. The base stations use three sector an-tennas resulting in three cells per base station.

2.3 Enhanced Uplink 9

2.2.1 Soft and Softer Handover

WCDMA supports soft and softer handover. A user in soft handover is connectedto multiple cells belonging to different base stations while a user in softer handoveris connected to multiple cells belonging to the same base station, see Figure 2.5.This feature is crucial since a user in handover experiences roughly the samepath gain to multiple cells and is thereby able to cause considerable interferencein multiple cells. This can occur when a user is located in an overlapping cellcoverage area from multiple cells. The soft and softer handover features enableall cells to which the user is connected, i.e. the active set, to power control theuser and thus reducing interference issues. A user moving away from one cell intoanother cell would increase transmission power to reach the original cell and inflictunnecessary interference if soft and softer handover were not used. The soft andsofter handover features also imply that uplink data transmissions can be receivedat multiple cells2.

RBS

User in soft handover

RBS

User in softer handover

RNC

Figure 2.5. Soft and softer handover.

2.3 Enhanced Uplink

As mentioned in Chapter 1 the evolvement of the WCDMA standard is an ongo-ing process illustrated in Figure 2.6. WCDMA Release 5 introduced the enhanceddownlink, HSDPA, and the main goal with the following release, WCDMA Re-lease 6, is to complement the improved downlink with the enhanced uplink. Amain requirement of the enhanced uplink is that it must be able to coexist withexisting WCDMA releases and should be possible to introduce in the already de-ployed WCDMA networks. A new transport channel is therefore introduced, the

2Downlink transmissions can also be received from multiple cells.


Enhanced Uplink

Additional enhancements

Enhanced Downlink(HSDPA)

Rel 4 Rel 5 Rel 6

WCDMAWCDMA EvolvedEvolvedWCDMAWCDMA

R99

Figure 2.6. The evolution of WCDMA.

Enhanced Dedicated Channel (E-DCH), alongside the existing Dedicated Channel(DCH). E-DCH is intended for best effort services rather than real time serviceslike speech. The main EUL goals are similar to the goals set for the HSDPA inWCDMA Release 5:

• Reduced delays

• Increased data rates

• Increased capacity

• Increased high data rate availability

The main objective is not the increase of peak data rates but rather the avail-ability of high data rates, i.e. increased high data rate coverage. Reduced delays,increased data rates and increased capacity will add to the experienced qualityof service (QoS) and support more demanding applications. The general featurein the EUL concept is to move functionality closer to the user, i.e. move deci-sion making functions from the RNC to the RBS, resulting in less signaling andreduced delays. Less information is however available because of this decentral-ization, making the design of efficient system functions more complicated. Othermore specific features the EUL concept is based on are:

• Short TTI

• Hybrid ARQ with soft combining

• Fast scheduling

The general principles behind these features are described below.

2.3.1 Short Transmission Time Interval

The available radio resources are allocated between the users accessing the sys-tem at certain intervals, the length of these intervals are called transmission timeinterval (TTI). Each user is signaled a maximum bit rate during each TTI. Themaximum rate results in a transmission power controlled by the fast power controldescribed in Section 2.1. A short TTI reduces round trip time and enables betterchannel adaptation to improve the transmission performance. Channel adaptationis needed in response to changing channel conditions due to user mobility.

2.3 Enhanced Uplink 11

2.3.2 Hybrid ARQ with Soft Combining

Automatic Repeat Request, ARQ, is an error control method that repeats thetransmission of an erroneous data block. If a data block is received correctly thecell replies by transmitting an acknowledgement (ACK) to the mobile user andthe next block is transmitted. If the data block is erroneous the cell responds witha negative acknowledgement (NACK) and the data block is retransmitted. A userin soft handover only requires an ACK from one of its multiple connected cells.

Instead of discarding the erroneous block completely the Hybrid ARQ (HARQ)scheme combines the previous transmission attempts with the current retransmis-sion, this procedure is called soft combining. Even though the previous trans-mission attempt could not be decoded correctly there is still usable informationpresent in the received signal. Short TTI lengths enable more retransmission at-tempts for a given time period and thus larger gain when using HARQ.

Example 2.2: The general principle of the HARQ feature

Assume a packet being sent with a certain bit rate resulting in a certain probabilityof being received correctly. If the bit rate is increased with a factor x, this allowsthe packet to be retransmitted x times during the same time as the original bitrate required. The probability of the packet being received correctly is lower foreach of the x transmission attempts but since the channel quality varies betweenthe attempts, a HARQ scheme with soft combining can terminate the transmissionin advance if transmission quality is good. The variations in channel quality cantherefore be utilized better resulting in less than x needed transmission attemptson average and thereby a more efficient system.

2.3.3 Fast Scheduling

Scheduling is the mechanism that grants users transmission permission. For eachTTI, the scheduler assigns a maximum bit rate that the user may use. The user’sfinal choice of rate depends on available transmission power and, if in handover,rate limitations imposed from the other cells in the active set. A well designedscheduling algorithm ensures efficient use of the interference resource. The schedul-ing function is moved from the RNC to the RBS in the EUL concept. Movingfunctionality closer to the user results in reduced delays and faster radio channeladaptation. The algorithm will however be more complicated to design since lessinformation is available in the RBS.

Chapter 3

Theoretical Assessments

Theoretical approaches might not capture the complex characteristics or the dy-namics of a WCDMA network but they bring intuition to potential problems andincrease the ability to interpret simulation results. This chapter covers theoreticalassessments with the aim to create understanding for potential interference issuesin a WCDMA network.

3.1 Definitions

The quality of service (QoS) experienced by users in a network is the main per-formance measurement. No matter how central the QoS is, it is at the same timevery ambiguous. Dropped calls, delays or poor sound quality are difficult to weightagainst each other since it depends on the user experiencing it. In the theoreti-cal assessments performance is measured in system throughput and no concern istaken to neither fairness nor delay. This might seem crude but it is justified by thefact that focus is on high bit rate users. The number of users will be moderate inorder to attain high bit rates. Few users in the system also implies large scatteringof the users, resulting in less competition and justification of the throughput basedperformance measurement.

Notation and a number of definitions used in the thesis will be declared inthis section. In the theoretical assessments a system with B base stations and M

mobile users is assumed. Each base station has one cell and base station and cellwill therefore refer to the same object. All B cells are serving the same centralnode, i.e. the same radio network controller (RNC), of which there exists onlyone in the theoretical assessments. Since only one RNC is assumed the set of cellscontrolled by this RNC and the complete set of cells are the same.

Perfect power control is assumed in the theoretical assessments, i.e. the sig-nalled CIR is attained instantly, and the static situation at a certain instancein time is studied. The time index, t, is therefore not explicitly written in thefollowing sections of this chapter.

A signal propagating through any medium will be attenuated, this is furtherexplained in Section 4.1. In the theoretical assessments the signal attenuation is

13

14 Theoretical Assessments

captured in the path gain matrix defined as (3.1). It is the ratio between thepower of the received signal and the the power of the transmitted signal and thusalways less than one.

Definition 3.1 Path gain matrix

G ,

g1,1 . . . g1,B

.... . .

...gM,1 . . . gM,B

M×B

< 1 (3.1)

where gi,j is the uplink path gain experienced between user i and cell j.

There is always background interference due to thermal noise. The total in-terference power in a cell j, Itot

j , is the sum of the received signal power added tothe background noise power. Interference power and background noise power willalso be referred to as interference and background noise respectively.

Definition 3.2 Total interference

Itotj ,

M∑

i=1

Ci,j + Nj =

M∑

i=1

pigi,j + Nj (3.2)

where pi is user i’s transmission power and Ci,j = pigi,j is the received power incell j from user i. Nj is the background noise power in cell j.

A different way of expressing the total interference is to describe it as rise overthermal noise or noise rise. It is basically the ratio between total interference, Itot,and background noise, N .

Definition 3.3 Noise rise

Λj ,Itotj

Nj

=

∑M

i=1 pigi,j + Nj

Nj

= 1 +

∑M

i=1 pigi,j

Nj

≥ 1 (3.3)

The mobile user equipment has a limited transmission power of about 21 dBm1

due to battery capacity and antenna features. The basic WCDMA principlesdescribed in Section 2.1 showed that in order to retrieve the data from a spreadsignal the interference must not be too high. In order to ensure system stabilityand coverage the maximum allowed interference is to be limited.

The quality of a communication link is a function of the received signal powerand the ambient noise power. The carrier to interference ratio (CIR) is the ratiobetween own signal energy and experienced interference, i.e. total interferenceexcluding the own signal power.

Definition 3.4 Carrier to Interference Ratio (CIR) from user i to cell j

γi,j ,Ci,j

Itotj − (1 − αi,j)Ci,j

(3.4)

The orthogonality factor, αi,j , is described in Section 3.1.1.

1See Appendix A.

3.1 Definitions 15

3.1.1 Multipath Propagation

Due to propagation mechanisms like reflection, diffraction and scattering, a trans-mitted signal can reach the receiver through multiple simultaneous propagationpaths. A transmitted signal can therefore interfere with itself because of this mul-tipath propagation. The different components reach the receiver with differentphases and different amplitudes. The orthogonality factor describes the receiver’sability to handle the own signal interference and varies between users and in timedepending on user mobility. Perfect own signal interference handling, i.e. no ownsignal interference, corresponds to an α = 0. The own signal interference will limitthe attainable bit rates considerably as shown in Example 3.1.

Example 3.1: Limitation due to own signal interference

The following holds for the CIR defined in (3.4),

γi,j =Ci,j

Itotj − (1 − αi,j)Ci,j

<1

αi,j

.

This results in a significant constraint on available bit rates as shown in Figure 3.1.In this example maximum noise rise is set to 7 dB and the potential bit rate of asingle user in a cell is plotted versus the orthogonality factor. Intercell interference,i.e. interference caused by users in neighboring cells, is not taken into accountresulting in a theoretical maximum. Equation (3.6) is used to map CIR values tobit rates.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

1

2

3

4

5

6

Orthogonal factor, α

Bit

rate

[Mbp

s]

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1−5

0

5

10

CIR

, γ [d

B]

Bit rateCIR, γ

Figure 3.1. Theoretical bit rate limitation due to own signal interference. Maximumnoise rise is set to 7 dB.


3.1.2 Shannon’s Theorem

When Shannon published his Communication Theory in the late 40s the perspec-tive on wireless communication changed. Focus shifted from the basic physicalprinciples like electromagnetic propagation to a systems approach. Among otherthings he formulated Shannon’s theorem [18]. Shannon’s theorem defines a theo-retical bit rate limit based on available bandwidth and CIR.

Theorem 3.1 Shannon’s Theorem, The maximum attainable bit rate

R = W log2 (1 + γ) [Mbps], (3.5)

where R is the maximum bit rate in Mbps, W is the bandwidth in MHz and γ theexperienced CIR.

This is a theoretical restriction and this bandwidth efficiency can never be achievedin a real system. The theorem will however be used as a model to map CIR-valuesto corresponding bit rates using (3.6).

R = C ln (1 + γ) [Mbps] (3.6)

where C is a constant set to normalize the maximum available bit rate to 4 Mbpssince that is the goal set for the enhanced uplink. Note that log2 (x) = ln(x)

ln(2) andthat the actual value of C is set to is not crucial in this analysis but rather themodel in general.

−10 −8 −6 −4 −2 0 2 4 60

0.5

1

1.5

2

2.5

3

3.5

4

4.5

CIR [dB]

Bit

rate

[Mbp

s]

Figure 3.2. Model of end-user bit rate as a function of experienced CIR.

Similar to the carrier to interference ratio is the carrier to total interferenceratio (CTIR). The difference being that the own signal signal power is relatedto the total interference in the cell instead of the interference experienced withrespect to the own signal.

3.2 Soft and Softer Handover 17

Definition 3.5 Carrier to Total Interference (CTIR) from user i to cell j

βi,j ,Ci,j

Itotj

=pigi,j

∑M

l=1 plgl,j + Nj

< 1 (3.7)

Resulting in a nonlinear relationship between the CIR and the CTIR accordingto,

γi,j =βi,j

1 − (1 − αi,j)βi,j

. (3.8)

Example 3.2: Pole Capacity

Equation (3.2) can in the single cell case be expressed using (3.3) and (3.7),

Itot =

M∑

i=1

pigi + N

1 =

M∑

i=1

βi +1

Λ

Λ =1

1 −∑M

i=1 βi

,

where∑M

i=1 βi < 1. When∑M

i=1 βi becomes close to 1 the corresponding noiserise approaches infinity and the system reaches its pole capacity. Even if infinitetransmission power, pi, would be available the system capacity cannot exceed thistheoretical limit. The uplink is therefore regarded as interference power limited.

3.2 Soft and Softer Handover

Best performance is obtained when all received signals from a user in softer han-dover are maximum ratio combined, this is a scheme resulting in the followingCIR,

γi =∑

j∈B

γi,j , (3.9)

where B is the set of cells in the active set. This is possible when a user is connectedto multiple cells in the same base station. Since each base station is assumed tosupport only one cell, the softer handover feature is not adopted in the theoreticalassessments.

In the case of soft handover the best performance is obtained by using selectioncombining. This is a scheme where the received signal with highest CIR is the CIRexperienced by the user,

γi = maxj∈B

γi,j . (3.10)


In the theoretical assessment the users are assumed to be connected to all cellsin the system while there in reality is a limitation to the maximum number ofconnected cells. This yields,

βi = maxj∈B

βi,j = maxj∈B

pigi,j

Itotj

. (3.11)

Ultimately maximization of the system throughput is the main objective of thetheoretical assessments, which will result in maximum allowed total interference,I

tot,maxj , in each cell2. This can be used to approximate the soft handover feature

by choosing the cell j that power controls user i, ji, as follows,

ji = arg maxj∈B

pigi,j

Itot,maxj

, (3.12)

where arg maxx f (x) is the argument x that maximizes f (x). When the maximumnoise rise in each cell is reached, i.e. when Itot

j = Itot,maxj ∀j, the following holds,

βi = maxj∈B

pigi,j

Itotj

= maxj∈B

pigi,j

Itot,maxj

=pigi,ji

Itot,maxji

The soft handover feature is therefore adopted by setting ji as in (3.12).

3.3 System Throughput Optimization

A throughput based scheduling is assumed in the theoretical assessments. Thescheduling is done by solving an optimization problem and this section is devoted todefining this optimization problem. The definitions stated in Section 3.1 are usedto derive a theoretical assessment of the bit rate limit with the system throughputmaximization as main objective. By setting setting ji as in (3.12) and solving forpi in (3.7) results in,

pi =βiI

totji

gi,ji

, (3.13)

where αi, βi and γi refers to αi,ji, βi,ji

and γi,jirespectively in the following

sections of this chapter. Inserting this expression in (3.3) gives,

Λj = 1 +

∑M

i=1 pigi,j

Nj

= 1 +

M∑

i=1

βi

gi,j

gi,ji

Itotji

Nj

. (3.14)

Equation (3.14) expresses the noise rise in cell j due to background noise andtransmitting users in the system3. We see that an interesting ratio is that betweengi,j and gi,ji

. This motivates the following definition.

2Assuming that there are enough users in each cell to utilize the entire available noise riseresource.

3Not only users in the own cell but all users in the system.

3.3 System Throughput Optimization 19

Definition 3.6 The relative path gain between user i and cell j is,

zi,j ,gi,j

gi,ji

. (3.15)

In order to achieve a compact notation of the relative path gain the normalizedpath gain matrix is defined as (3.16). The relative path gain for each user i withrespect to each cell j is found in the i:th row and j:th column of the matrix.

Definition 3.7 Normalized path gain matrix

Z ,

z1,1 . . . z1,B

.... . .

...zM,1 . . . zM,B

M×B

=

g1,1

g1,j1

. . .g1,B

g1,j1

.... . .

...gM,1

gM,jM

. . .gM,B

gM,jM

M×B

≤ 1 (3.16)

3.3.1 Equal Background Noise

The background noise in a cell depends on the thermal temperature. The thermalproperties of the cells can be assumed equal and therefore also the backgroundnoise. If equal background noise in all cells is assumed, i.e. Nj = N ∀j, (3.14)can be expressed as,

Λj = 1 +

M∑

i=1

βi

gi,j

gi,ji

Λji. (3.17)

This expression can be used to define a system of equations describing the noiserise in all cells in the system.

Λ1

...ΛB

B×1

=

1...1

B×1

+ ZTB×Mdiag

(

Λj1 · · · ΛjM

)

M×M

β1

...βM

M×1

(3.18)

where diag(

Λj1 · · · ΛjM

)

M×Mis a diagonal matrix with the noise rise of the

cell to which user i is connected in each diagonal element. The product betweenthe variables Λj and βi makes this a nonlinear system of equations. Assumingthat the maximum noise rise will be achieved yields the following expression.

Λmax1...

ΛmaxB

≥

1...1

B×1

+ ZT diag(

Λmaxj1

· · · ΛmaxjM

)

β1

...βM

, (3.19)

where the equality in (3.18) has been replaced with an inequality for optimizationsimplicity. Equation (3.19) has the users’ CTIR as only free variable. Due tothe approximation in the derivation of (3.19) the system of equations is thereforelinearized.


3.3.2 Optimization Problem

By maximizing the sum of the users’ bit rates,∑M

i=1 Ri, the throughput of thesystem is maximized. Finding the maximum throughput can therefore be writtenas a nonlinear optimization problem, where the constraints are linear but theobjective function is nonlinear.

max

M∑

i=1

Ri =

M∑

i=1

C ln

(

1 +βi

1 − (1 − αi)βi

)

(3.20a)

subject to

Λmax1 − 1

...Λmax

B − 1

≥ ZT diag

(

Λmaxj1

· · · ΛmaxjM

)

β1

...βM

(3.20b)

0 ≤ βi ≤ min

{

pmaxi gi,ji

Itot,maxji

, 1

}

∀i (3.20c)

where the βis are seen as variables. Yet another constraint is incorporated on βi

since a maximal transmission power for each user i, pmaxi , is introduced.

3.3.3 Linearity and Convexity

Due to the nature of the nonlinearity of the objective function in (3.20) the op-timization problem is neither linear nor convex. A convex optimization problemimplies that a local maximum is also a global maximum. Nonlinear and nonconvexoptimization problems are generally very hard to solve. Further approximationswill be done in the following sections to simplify the optimization problem.

3.3.4 Equal Maximum Noise Rise

By assuming equal conditions in all cells justifies choosing equal maximum noiserise in all cells, i.e. Λmax

j = Λmax ∀j. Equation (3.20b) can then be simplifiedto,

Λmax − 1...

Λmax − 1

B×1

≥ ZT ΛmaxIM×M

β1

...βM

,

where I is the identity matrix and Λmax is the scalar representing the maximumnoise rise. This can be rewritten as,

Λmax − 1

Λmax

1...1

B×1

≥ ZT

β1

...βM

where the scalar multiplying the vector on the left hand side can be identified asthe relative load, L, defined in (3.21).


Definition 3.8 Relative load

Lj , 1 −1

Λj

(3.21)

Equal maximum noise rise also implies the cell having maximum relative load,

Λmax =1

1 − Lmax=

Imax

N. (3.22)

3.3.5 Nonlinear Optimization Problem

With the assumption from Section 3.3.4 and the definition of relative load, theoptimization constraint in (3.20b) can be written as,

Lmax1B ≥ ZT β

where 1B =(

1 . . . 1)T

B×1and β =

(

β1 . . . βM

)T. This simplifies (3.20) to,

max

M∑

i=1

C ln

(

1 +βi

1 − (1 − αi)βi

)

(3.23a)

subject to Lmax1B ≥ ZT β (3.23b)

0 ≤ βi ≤ min

{

pmaxi gi,ji

Itot,max, 1

}

∀i (3.23c)

in the case of equal maximum noise rise and equal background noise in all cells.

3.3.6 Quadratic Optimization Problem

Due to the complex nature of the optimization problem the objective function isapproximated with a quadratic function. There is extensive theory in [6], coveringquadratic optimization problems or quadratic programming problems but thatis beyond the scope of this thesis. Results from that theory is however usedto approximate the optimization problem. The derivation of the approximatedobjective function is found in Appendix B and the resulting quadratic optimizationproblem is,

max

M∑

i=1

C

(

βi +1 − 2αi

2β2

i

)

(3.24a)

subject to Lmax1B ≥ ZT β (3.24b)

0 ≤ βi ≤ min

{

pmaxi gi,ji

Itot,max, 1

}

∀i. (3.24c)

Equation (3.24) is used in the following examples to study basic scenarios withenlightening interference issues.


Example 3.3: Two Cell System with Four Users

Let a system have four users i, i ∈ {1, 2, 3, 4}, who are all able to connect to oneof two cells j, j ∈ {1, 2}. Assume that users in cell 1 can not be heard in cell 2but users in cell 2 are heard in both cells. The system then has the following pathgain matrix,

G =

−119 −∞−122 −∞−130 −119−135 −119

[dB].

User 1 and 2 will connect to cell 1 and user 3 and 4 will connect to cell 2. Theresulting Z-matrix is,

Z =

1 01 0

0.08 10.03 1

.

Equation (3.14) is thus

Λ1 = 1 + β1Λ1 + β2Λ1 + β3g3,1

g3,2Λ2 + β4

g4,1

g4,2Λ2

Λ2 = 1 + β3Λ2 + β4Λ2

,

in the separate cells. Note that equal background noise is assumed in all cells andthat β1 and β2 are not present in the second equation since the corresponding usersare not heard in that cell. Actual noise rise is replaced with a maximum noise rise,Λmax = 7 [dB], in both cells. Together with the above system of equations thisyields the inequality below.

Λmax − 1

Λmax

(

11

)

=

(

Lmax

Lmax

)

≥

(

1 1g3,1

g3,2

g4,1

g4,2g1,2

g1,1

g2,2

g2,11 1

)

β1

β2

β3

β4

= ZT β

Assume a maximal transmission power, pmax = 21 [dBm], for all users. Solvingthe optimization problem in (3.24) with the above parameters yields,

β =

0.6610.1120.1400.661

.

This solution results in maximum noise rise in both cells and the transmissionpower for each user is,

p =

21.016.314.321.0

[dBm].


Note that this is a special case where the optimization is done with relative easedue to the nature of the chosen path gain matrix and maximal transmission power.The users’ corresponding CIR values are

γ =

1.9470.1260.1631.947

.

The CIR for each user is mapped to a bit rate using (3.6). This results in thefollowing bit rate for each user.

R =

2.700.300.382.70

[Mbit/s]

The resulting average bit rate per user is thus 1.52 Mbps. This result takes nofairness into consideration which is clearly seen in the bit rate distribution. Theuser with lowest relative path gain in each cell is allowed to transmit with maximumtransmission power while the rest of the users can utilize what is left of the noiserise resource.

Example 3.4: Minimum bit rate

Assume the same scenario as the previous example but also introduce a minimumbit rate, Rmin = 1.0 [Mbps]. This yields the following result.

p =

19.1620.9817.9819.52

[dBm] γ =

0.760.490.490.89

R =

1.421.001.001.59

[Mbit/s]

The resulting average bit rate per user is thus 1.25 Mbps. This is obviously morefair but also a trade-off since system throughput decreases.

Example 3.5: Maximum bit rate

Yet again assume the same scenario as the previous example but instead of aminimum bit rate introduce a maximum bit rate, Rmax = 2.0 [Mbps]. This yieldsthe following result.

p =

20.2119.1516.7820.21

[dBm] γ =

1.230.280.331.23

R =

2.000.610.722.00

[Mbit/s]


The resulting average bit rate per user is thus 1.33 Mbps. Again this is more fairthan Example 3.3 and system throughput is again decreased due to the trade-offbetween fairness and throughput capacity.

Example 3.3 through Example 3.5 illustrate that the optimal result throughput-wise is achieved by an unfair bit rate distribution and a more fair system is attainedat the expense of system throughput.

Example 3.6: Two cell with one user in each cell

Let a system have two cells j, j ∈ {1, 2}, with one user i, i ∈ {1, 2}, in eachcell. Assume that user 1 in cell 1 can not be heard in cell 2 but user 2 in cell 2 isheard in both cells. Both users have an equal and relatively good path gain, i.e.around 110 dB, to their serving cells. This implies that they are able to utilize allof the available noise rise resource in their own cell. The effect user 2 has on user1, governed by the relative path gain, is studied. Assume the following path gainmatrix,

G =

(

g0 −∞g2,1 g0

)

[dB],

which results in the following system of equations,

Lmax ≥ β1 +g2,1

g0β2

Lmax ≥ β2

.

Since g2,1

g0< 1 the best resource distribution throughput-wise is to let user 2 use as

much as possible of the available resources and let user 1 use what is left. Figure 3.3shows the result when the g2,1 parameter is varied. Note that the x-axis is plottedin logarithmic scale and thus representing the ratio between g2,1 and g0 in [dB].

Example 3.6 illustrates that a user, located on the overlapping cell coveragearea, can utilize a significant amount of neighboring cells’ resources. The exampleshows that a user with a relative path gain greater than -7 dB has the potential tomore than halve the attainable CIR for a user in a neighboring cell. A relative pathgain of -7 dB will therefore be used as threshold to consider a user hazardous. Ina real system it would not go this far since the soft handover feature would comeinto force and decrease the induced interference. That would however result indecreased performance both for the user and for the system and thus still seen asan interference issue, justifying the -7 dB relative path gain threshold.


−25 −20 −15 −10 −5 00

1

2

3

4

5

Relative path gain [dB]

Bit

rate

[Mbp

s]

−25 −20 −15 −10 −5 0−6

−4

−2

0

2

4

6

8


CIR

[dB

]

User 1User 2

User 1User 2

Figure 3.3. Example how a user when close to the cell boundary affects another userin a neighboring cell.

Chapter 4

Simulation Models

Basic link budgets and theoretical equations may be used to dimension a networkbut due to the interactive and tight interference coupling of WCDMA networks amore complex approach based on simulations is required to optimize efficiency. Themodels used to simulate the WCDMA network will be presented in this chapter.

4.1 Radio Channel Model

The received energy of a transmitted electromagnetic wave will always be less thanthe energy with which it was transmitted. This is called signal attenuation and isdue to physical propagation mechanisms like reflection, diffraction, scattering andespecially the multidirectional radiation of any transmitting antenna. To attain arealistic model of the signal attenuation it will consist of four elements describedin this section. The total path gain can be expressed as a product, Equation (4.1),of these four elements.

g = gpgsgfga < 1 (4.1)

Here, the path gain is expressed in linear scale, but it is often referred to inlogarithmic scale and will then be the sum of the separate elements in dB.

4.1.1 Distance Attenuation

A widely used distance attenuation model for coverage calculation and link budgetsis the Okumura-Hata model [12]. Cell radiuses in the interval of 300 meters to1500 meters, where the Okumura-Hata model has good accuracy, is studied. TheOkumura-Hata model describes the distance attenuation as,

gp = 46.3 + 33.9 · log10(f) − 13.82 · log10(hB) − 3.2 · [log10(11.75 · hU )]2

− 4.97 + (44.9 − 6.55 · log10(hB)) · log10(d), (4.2)

where gp is the distance attenuation (dB), f is the frequency (MHz), hB and hU

are the heights (m) of the base station and user antenna respectively. Finally d

27

28 Simulation Models

(km) is the distance between the cell and the user. Note that gp strictly decreaseswith distance.

4.1.2 Shadow Fading

Shadow fading occurs when large obstacles like buildings and hills block the lineof sight. The shadow fading influence on the average signal level variations iscommonly modeled with a log-normal distribution.

gs = N (µ, σ) , (4.3)

where gs, µ and σ are given in dB. The parameters µ and σ are commonly set to 0dB and 8 dB respectively. When a user is located in such a way that it is subject toshadow fading it is likely that nearby locations also will be subject to shadowing,shadow fading is therefore spatially correlated. A decorrelation distance is usedto represent the distance where the correlation has decreased with a factor 1

e.

4.1.3 Multipath Fading

Multipath fading or fast fading is due to the multiple signal paths taken by thepropagating signal, hence the name multipath fading. The multiple componentsreach the receiver with different amplitude and phase resulting in constructive anddestructive interference. The multipath fading can shift vary fast since a slightchange in phase between two propagating components can have significant effecton the received signal, which is why it is also called fast fading.

4.1.4 Antenna Gain

The antenna gain models the gain offered by directional antennas. The antennacharacteristics is described in more detail in Section 4.2.

4.2 Antenna Model

An isotropic antenna radiates with equal intensity in all directions. Such an an-tenna can never be implemented in reality and even more importantly, it is notwanted. An isotropic antenna would be very inefficient since the objective for theantenna is to cover a surface area. Antennas are always directional to some extent,meaning that it radiates more energy in some directions than others, dependingon how it is designed. The antenna gain is described with reference to an idealisotropic antenna (dBi) or a dipolar antenna (dBd)1. The antenna model used isa three-sector antenna illustrated by the antenna diagram in Figure 4.1. A fulldescription of the antenna model can be found in [2].

Each cell covers 120 degrees in the horizontal plane resulting in full coveragewhen using three antennas. The vertical diagram in Figure 4.1(b) shows a 6-degreeelectrical tilt and to this a mechanical tilt is added or subtracted resulting in a

1See Appendix A for details concerning dBi and dBd.

4.3 Network Deployment 29

−10 dB

−3 dB

0 dB

30

210

60

240

90

270

120

300

150

330

180 0

(a) Horizontal antenna diagram

−10 dB

−3 dB

0 dB

30

210

60

240

90

270

120

300

150

330

180 0

(b) Vertical antenna diagram

Figure 4.1. Antenna diagram. The gain in the main direction of the antenna is 15.85dBd and the antenna diagrams show the gain (dB) in each direction with reference tothe gain in the main direction.

total downward tilt which is the antenna tilt angle referred to throughout thisthesis. Note that the electrical tilt is kept fix and only the mechanical tilt varied.

4.3 Network Deployment

The network deployment refers to site2 placement, number of cells per site, cellradius, antenna tilt etc. The network deployment used in the simulations consistsof seven sites using three sector antennas, resulting in 21 cells. Wrap-around isused to avoid border effects, i.e. the seven sites are repeated creating an infinitesimulation surface without borders. The network deployment is illustrated inFigure 4.2.

4.4 Static Simulation Models

As a first step of the simulations, static path gain maps are studied. The generatedpath gain maps are based on statistics from realistic scenarios. Each cell consistsof a number of bins where the bin size is set to a tenth of the cell radius. Notethat the number of bins will be fixed regardless of cell radius and thus result inthe bin size being proportional to the cell radius. It is reasonable since a small cellresults in a small bin size and thus good accuracy. When studying large cells, thebin size increases and the need for detailed accuracy decreases.

2Site refers to the physical location of a base station.


Figure 4.2. Simulated network deployment. The cell radius is defined as the radius ofthe circle circumscribing the cell, i.e. the distance from the center of the cell to one ofits corners, resulting in a site-to-site distance three times the cell radius.

4.4.1 Shadow Fading

When generating the path gain maps the shadow fading model is slightly differentfrom the model described in Section 4.1.2. Instead of being a single Gaussiandistribution it is a sum of two Gaussian distributions. One distribution modelsthe shadow fading over the cell while the other models the shadow fading in eachbin. The cell shadow fading model has µcell = 0 dB, σcell = 5 dB and decorrelationdistance equal to one fifth of the cell radius. The bin shadow fading model hasµbin = 0 dB and σbin = 3 dB resulting in a total shadow fading model with µtotal =0 dB and σtotal = 8 dB corresponding to the model described in Section 4.1.2.Decorrelation distance in the bin shadow fading model is meaningless since a userdoes not have a certain location within a bin. A parameter, 0 ≤ ρ ≤ 1, is usedin the bin shadow fading model to represent fading correlation to different cells,where zero represents uncorrelated and one completely correlated. A user in atopographical cavity for example will probably experience a low path gain to allcells. ρ is set to 0.5 when generating the path gain maps.

4.4.2 Multipath Fading

Multipath fading is not modeled when studying the static path gain maps since itholds no interest when studying a static situation. Instead a margin is added tocover the multipath fading variations.

4.5 Dynamic Simulation Models 31

4.5 Dynamic Simulation Models

As a second step of the simulations, a study of the dynamic aspects of the WCDMAnetwork is conducted. Simulations should reflect the world as realistically as possi-ble, limited of course by available computational capacity. Behavior over time andthe introduction of a traffic model will offer a more realistic aspect to the analysis.Dynamic simulations are more demanding than the path gain map generation andthus restricted to the most essential scenarios. Interference issues are most evidentwhen mobile users are not power limited, i.e. when cell radius is moderate. Thedynamic simulations are therefore restricted to scenarios where the cell radius isset to 500 meters.

4.5.1 Enhanced Uplink

Only the uplink will be simulated to reduce computational load. The new featuresin the enhanced uplink concept, some described in Section 2.3, are adopted in thedynamic simulations.

4.5.2 Traffic Model

The traffic model governs the users’ mobility, intensity, service requests etc. OnlyE-DCH traffic is modeled, i.e. all users in the system are using the new introducedE-DCH channel. Speech users using the DCH channel are not modeled since thefocus of the thesis is to study the effects of different network deployments ratherthan improvements in the EUL concept compared to previous WCDMA releases.

A file transfer protocol (FTP) model is used to model the requested dataservice. Each user is modeled to transfer a single data packet each. Packet sizeis set to 1 megabyte (MB), i.e. 8 Mb, and a maximum bit rate is set to 4 Mbps.The packet size is set relatively high3 to compensate for the maximum bit rate inorder to be able to study the effect of high bit rates.

The users are initially distributed uniformly over the simulation area and moveaccording to a Gaussian walk, with a constant average speed and average acceler-ation.

4.5.3 Soft and Softer Handover

The soft and softer handover feature is adopted in the dynamic simulations. Auser is allowed to maintain connection to up to three different cells at the sametime. Furthermore, a user is let in to soft handover when the relative path gain isgreater than -2 dB and let go of when the relative path gain falls below -4 dB.

4.5.4 Logging

In order to attain reliable statistics, the simulation time is set to 200 seconds. Theinitial 20 seconds are discarded to let the system stabilize. User specific logging

3The packet size corresponds to a high resolution image for example.


like bit rate and path gain will be logged as an average for each user and onlyusers entering the system after the 20 second threshold will be included in thestatistics. System throughput is averaged over both time and cells while noise riseis logged in a histogram for each TTI. The simulations are performed three timesusing different seeds to attain sufficient statistics.

4.5.5 Hybrid ARQ with Soft Combining

Hybrid ARQ with soft combining will be adopted using 8 parallel HARQ queuesand a block error rate (BLER) set to 10 %. 8 parallel processes imply that 7 otherqueues will be handled until the first process has received an ACK or NACK andcan be retransmitted if needed. The number of parallel queues is set to correspondto the round trip time. The BLER is the probability of an erroneous block beingreceived. A BLER set to 10 % corresponds to 90 % of the transmitted blocksbeing received correctly on the first transmission attempt.

4.5.6 Scheduling

A maximum number of users will be admitted by the RNC and users not let in willwait until admitted. Active users will be given an initial transmission grant basedon available noise rise resources. The resources will be shared equally among theactive users by the scheduling algorithm. During each TTI the initial transmis-sion grant for each user is tuned to maximize efficiency according to the number oftransmitting users. Noise rise measurements needed to tune transmission grantswill be estimated with true noise rise to attain an optimistic assessment of perfor-mance.

A user in soft handover will receive transmission grants from all cells the activeset and the minimum grant will be applied.

4.5.7 G-RAKE Receiver Model

In order to reduce effects from the multipath fading a RAKE-receiver [15, 17] hasbeen used in previous WCDMA releases. A RAKE receiver has multiple antennascalled fingers receiving the different signal components reaching the receiver. Byusing the correlation between the different fingers the own signal interference canbe reduced. Figure 3.1 shows that an α ≈ 0.6, which is commonly used to modelthe own signal interference in a typical urban scenario4, corresponds to a maxi-mum bit rate of approximately 2 Mbps. A regular RAKE-receiver will obviouslynot be sufficient to reach the goal of up to 4 Mbps in the EUL concept. Instead thegeneralized RAKE-receiver (G-RAKE) will be employed. The G-RAKE receiverutilizes the correlation between the receiving fingers of the antenna more efficientlyresulting in less own signal interference. The G-RAKE receiver is modeled by set-ting the orthogonality factor α to zero resulting in perfect own signal interferencemanagement.

4The typical urban scenario is designed by 3GPP to be able to compare different simulationresults.

Chapter 5

Simulation Results

Results from simulations will be presented and analyzed in this chapter. The sim-ulations are conducted using 3G simulators in Matlab developed by Ericsson Re-search. Section 5.1 presents results from the statistically generated path gain maps,followed by Section 5.2 where user traffic is introduced in dynamic simulations. Fi-nally comparisons between the theoretical assessments and the simulation resultswill be made in Section 5.3.

5.1 Path Gain Map Generation

The network deployment, i.e. how site location, antenna etc. is chosen, willaffect the characteristics of the network and thereby the efficiency. In order toobserve effects of cell radius and antenna tilt settings, statistical path gain mapsrepresenting static snapshots in time are generated and studied.

5.1.1 Path Gain

A user will theoretically experience a path gain to all cells in the system but onlycells in a certain vicinity of the user will have any practical importance since pathgain drops rapidly with distance. The cell to which a user has best path gain willmost likely be the serving cell with the exception of users in handover which areconnected to multiple cells. Figure 5.1 illustrates the best path gain experiencedin each bin for different antenna tilts in a scenario where cell radius is set to 500meters.

The antenna tilt affects the experienced path gain throughout the network, bytilting the vertical antenna pattern1 the cell overlap can be controlled. As the tiltis increased the cell coverage overlap is decreased but so is the average path gainexperienced in the network as shown by the histograms in Figure 5.1. At a certainpoint, when the antenna tilt is too extreme, coverage is lost. This effect can be

1Described in Figure 4.1(b).

33

34 Simulation Results

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Figure 5.1. Path gain maps and histograms for different antenna tilts with a cell radiusset to 500 meters. The histograms show that the path gain mean decreases and the pathgain variance increases as the downward tilt of the antenna is increased. The figure canbe difficult to interpret if printed in black and white.

5.1 Path Gain Map Generation 35

seen in Figure 5.1(e) where low path gain, around -140 dB, is experienced on thecell boundary.

Complete coverage can never be achieved without cell overlap because of theantenna radiation pattern, instead there will always be a trade-off between cover-age and overlap requirements.

5.1.2 Path Gain Requirement

In order to ensure coverage in the network a requirement governing the experiencedpath gain is introduced. The 95th percentile of experienced path gain is required toexceed -140 dB, meaning that the path gain experienced in 95 percent of the binsmust be above -140 dB. Such a requirement is commonly used in cell planning andwill ensure coverage. Figure 5.2 shows the ratio of bins with a path gain exceeding-140 dB for different cell radiuses and antenna tilts.

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Figure 5.2. Ratio of bins with path gain greater than -140 dB.

Only network configurations where this requirement is fulfilled will be studiedfurther. Note that as the cell radius increase there is not much margin for tuningthe antenna tilt. Also note how the curves drop dramatically when the antennatilt is too extreme and coverage is lost.

The derivations of the theoretical assessments in Chapter 3 showed that therelative path gain is crucial in interference management rather than the absolutepath gain. The relative path gain is therefore studied further with the enforcedrequirement stated in this section.

5.1.3 Relative Path Gain

A low relative path gain is favorable since it means that a better path gain isexperienced to the own cell while less interference is caused in neighboring cells.It was shown in Chapter 3 that a relative path gain greater than -7 dB might


consume more than half of a neighboring cell’s resources. A relative path gainthreshold will therefore be set to -7 dB and bins exceeding this threshold will beconsidered unsafe.

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Figure 5.3. Relative path gain in a network deployment with a cell radius of 500 metersand 6◦antenna tilt. The figure can be difficult to interpret if printed in black and white.

Note that the majority of the potentially dangerous bins are located on theborder between different sites as expected. What is more surprising is the lackof hazardous bins on the border between cells belonging to the same site. Theconclusion can be drawn that the sides of the horizontal antenna pattern2 attaincoverage without resulting in too severe overlap. This is fortunate since such anoverlap could imply high absolute path gain to both cells and thus significantinterference. The result is unambiguous regardless of antenna tilt. Instead theproblem lies at the front end of the cell where the antenna points towards anothersite. This is however the characteristic most coupled with the antenna tilt andthus the characteristic most easily influenced.

5.1.4 Ratio of Hazardous Bins

As a measurement of how problematic a scenario is, the ratio between the numberof unsafe bins and the total number of bins is studied. The ratio for different an-tenna tilts and cell radiuses is illustrated in Figure 5.4. Note that no considerationis taken to handover features when setting the threshold to -7 dB and above. Therelative path gain threshold when a user is admitted to and released from soft andsofter handover is usually set to approximately -2 dB and -4 dB respectively. Themeasurement will be regarded as pessimistic since a user in handover can be con-trolled but nonetheless considered an issue since the user experiences poor bit rateif restrictively controlled while causing significant interference if not controlled.

Figure 5.4 shows that the ratio of bins that are potentially dangerous are quitehigh, especially in scenarios with small cells and poor antenna tilt. On the otherhand by adjusting the tilt the problem can be improved. Another observation

2Described in Figure 4.1(a).


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made is that large cells have a high ratio of unsafe bins regardless of how the tilt ischosen. This might seem problematic since the situation can not be improved byadjusting the tilt. The following section shows that the problem is not as evidentas first perceived.

5.1.5 Neighbor Cell Resource Consumption

A user in a potentially dangerous location with approximately equal path gain totwo or more cells does not have to be a problem. If the user experiences very poorbut still equal path gain to two cells it will result in a high relative path gain, butbecause of the poor path gain the user will not be able to cause any considerabledamage.

In order to study the actual threat a user can constitute, rather than thepotential threat the relative path gain implies, a different approach will be taken.Let a potential user transmit with maximum capacity in each bin, i.e. eithertransmit with maximum transmission power or until the noise rise capacity inthe own cell is reached. The actual danger introduced by a user can thereby bestudied.

Figure 5.5 illustrates the relative noise rise caused in neighboring cells by a po-tential user in each bin. Note how, in a poorly tilted scenario as in Figure 5.5(a),hazardous locations are not focused to the cell border as expected. Instead sig-nificant interference can be caused from locations in the vicinity of a site as well.There are two reasons for this result. The antenna is pointing towards other sitesresulting in high relative path gain due to significant overlap. At the same timethe situation deteriorates even more since the vertical antenna diagram restrictsabsolute path gain near the site due to the tilt. As the tilt is adjusted both thesetwo effects decrease, improving the situation dramatically. Similar for all scenariosis that the location of hazardous bins are very uncorrelated.


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Figure 5.5. Introduced relative noise rise in neighboring cell by a potential user ineach bin transmitting with maximum capacity. The relative noise rise with respect tomaximum noise rise set to 7 dB. Cell radius is set to 500 meters. The figure can bedifficult to interpret if printed in black and white.


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Figure 5.6 shows the average relative noise rise based on the results presentedin Figure 5.5. The requirements enforced in Section 5.1.2 left little room for tuningthe antenna tilt for large cells and thereby the ability to handle overlap problems.The curve representing the 1500 meters cell radius in Figure 5.6 is however bothflat and relatively low, implying that a large cell does not pose any real problem.Users on the border of a large cell experience such a poor path gain, both to theown cell as well as to neighboring cells, that they can not pose any real threat inan interference point of view.

However, that is not the case when small cells are studied. As the cell ra-dius decreases the potential intercell interference problem increases. A cell radiusaround 500 meters with a poorly tilted antenna can result in a scenario where halfof the bins in the cell can consume more than half of the resources in neighboringcells. The reason is an antenna tilt resulting in antennas pointing toward eachother and thus creating significant overlap and with that significant intercell inter-ference problems. Due to the smaller cell radius more room is however availablefor tuning the antenna tilt. By adjusting the antenna tilt the induced interferencecan be decreased considerably.


5.2 Dynamic Traffic Simulations

The path gain map generation indicated that by tuning the antenna tilt the ef-ficiency of the network could be increased. However the static properties of thepath gain maps do not capture the dynamics of the network and thus the problemneeds to be studied further. This is done by introducing the traffic model andstudy simulations over time.

5.2.1 Performance

In order to evaluate results from the dynamic simulations performance measure-ments are needed. During the theoretical assessments system throughput was themaximized objective function. Average system throughput is defined as transmit-ted bits per second and cell.

Definition 5.1 System throughput

system throughput ,total number of transmitted bits

simulation time · total number of cells(5.1)

The dynamic simulations introduces interesting end-user performance measure-ments that are crucial to the QoS experienced by the users. The most centralbeing the user bit rate.

Definition 5.2 User bit rate

user bit rate ,packet size

user delay(5.2)

where user delay is the time taken to complete the packet transmission.

Definition 5.3 User delay

user delay , packet transmission time (5.3)

The traffic model described in Section 4.5.2 involves a packet size of 1 MB foreach user. This means that user delays for all users are comparable without packetsize normalization.

A maximum noise rise is set to guarantee system stability and coverage, effi-cient use of available noise rise resources is crucial and thus a quantity needed tobe studied. The study has so far been restricted to static situations making theimmediate noise rise the natural measurement. Noise rise is however fluctuatingmaking the measurement over time ambiguous. The system noise rise measure-ment in this thesis is averaged over both time and cells resulting in a optimisticpresentation. Fluctuating noise rise is however seen indirectly through poor per-formance in other measurements.

Definition 5.4 Average noise rise

Λ , average noise rise over time and cells (5.4)

5.2 Dynamic Traffic Simulations 41

Like noise rise, the path gain experienced by a user will also vary with timeand with analogue reasoning, path gain is also studied as an average.

Definition 5.5 User path gain

g , average path gain over time (5.5)

This results in a optimistic measurement of user path gain. The fast fading modelaverages to one in linear scale making it invisible in the averaged path gain butagain it can be seen indirectly through other user performance measurements.

5.2.2 System Capacity

The performance of a system will be affected considerably by the load of the sys-tem. Load in this sense refers to the number of users served during the simulationtime. If a moderate number of users access a system at the same time the prob-ability of experiencing a high bit rate is higher than if a large number of userswould. System capacity is defined as the load where the 10th percentile of userbit rates exceeds 160 kbps, i.e. at least 90 percent of the users must experience abit rate higher than 160 kbps.

Definition 5.6 System capacity

The maximum number of users served during the simulation time with the re-quirement that the 10th percentile of user bit rates is greater than 160 kbps.

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Figure 5.7. CDF of user performance measurements. The different curves representresults with different levels of system load. Cell radius is set to 500 meters and antennatilt to 6◦.

Figure 5.7 shows user bit rate distribution and user delay distribution in theform of a CDF, see Appendix C for a brief explanation of the CDF. Note that even


though maximum bit rate is set to 4 Mbps the maximum user bit rate is around3 Mbps. There are three main reasons for this:

• BLER set to 10 %

• Channel setup time

• TCP ramping

The block error rate (BLER) is the probability of an erroneous block beingreceived. This means that a BLER of 10 percent results in a maximum bit rate of4 · 0.9 = 3.6 Mbps.

The channel setup time for a user is not negligible when bit rates are this higheven though the packet size is set fairly high.

The third factor is due to transport control protocol (TCP) ramping. TCPis the protocol governing the packet transmission and one of the effects from thisscheme is that it takes time for the bit rate to reach the aimed rate, this is calledramping. If these three factor were disregarded Figure 5.7(a) would instead looklike Figure 5.8. The effect from channel setup time and TCP ramping decreasesas packet size is increased or bit rate decreased.

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Figure 5.8. CDF of user bit rate disregarding from setup effects and BLER targeteffects.

Note that Figure 5.8 does not show the end-user experienced bit rate butinstead illustrates the immediate bit rate when a user is actually transmitting andbit rates around 4 Mbps is actually attained.

As was concluded in the theoretical assessments the possibility of high bit ratesare highest when the system is lightly loaded since a user with high bit rate canutilize most of a cell’s resources. Figure 5.7(a) shows that about 60 percent of theusers in a lightly loaded system experience a user bit rate of around 3 Mbps which ishigh. As expected, the experienced bit rates decreases as system load increases tofinally reach the system capacity limit. Appendix D contains extensive simulationresults from different system loads.


5.2.3 Antenna Tilt

The static simulations in Section 5.1 showed that relative path gain decreased asthe antenna tilt was increased. A user experiencing poor path gain to the own cell,i.e. not able to attain high bit rate or cause significant noise rise, and only slightlyworse path gain to another cell results in high relative path gain even though notposing any threat due to the poor absolute path gain. This was complicated toshow by studying the static path gain maps since no traffic was simulated. Whenstudying user bit rates and system throughput this is incorporated in a naturalway.

If the antenna is not tilted enough it will result in significant cell coverageoverlap causing unnecessary noise rise due to high relative path gain. If the antennatilt is too extreme coverage is lost resulting in low bit rates due to poor absolutepath gain.

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Figure 5.9. User performance measurements in a scenario with a system load of 75percent of system capacity. Cell radius is set to 500 meters.

When studying the bit rate distribution between users in Figure 5.9(a), itbecomes visible that the number of users experiencing a high bit rate is maximizedwhen a 6 degree antenna tilt is applied. The curve representing the maximumantenna tilt of 9 degrees has a high number of users with very low bit rate butthen crosses some of the other curves resulting in a higher number of users withhigh bit rate. The result is uniform for different system loads and explained by thefact that the 9 degree tilt is too extreme resulting in loss of coverage. Users locatedoutside the coverage area experience very low bit rate and users inside experiencebetter since the antenna is pointed more directly towards them. Basically a smallerarea is served better at the expense of coverage.

The user bit rates increase as the antenna tilt is tuned and so does the noiserise efficiency as seen in Figure 5.10. The curve representing the 9 degree antennatilt has the lowest noise rise but not because of better noise rise efficiency, insteadthis is again explained by decreased path gain due to lost coverage. The users aresimply not able to utilize available resources.


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Figure 5.11 shows CDFs over absolute and relative user path gain. The bestuser bit rate is attained at a 6 degree tilt even though the best path gain isexperienced around a 3 degree tilt. This is explained by better noise rise resourceutilization due to better relative path gain when comparing the 6 degree and the3 degree tilt.

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Figure 5.11. Path gain statistics for users in a scenario with a system load of 75 percentof system capacity. Cell radius is set to 500 meters.

5.2.4 System Performance

The more a system is loaded the lower the probability of attaining a high bit rateas seen in Figure 5.9(a). System throughput on the other hand increases with loaduntil a certain point where it levels out or even decreases. Experienced user bitrates will thus decrease as system throughput increases as seen in Figure 5.12(a).


System throughput reaches almost 2 Mbps on average which is good consideringscheduling and other realistic complications due to dynamic aspects. If the systemis overloaded the system throughput can exceed 2 Mbps but it also results in anunfair system since the number of users not attaining the required service increasesdramatically.

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Figure 5.12. Resource utilization.

As the system load increases so does the noise rise utilization as seen in Fig-ure 5.12(b). The average noise rise slightly exceeds the maximum limit of 7 dBwhen the system is performing at the capacity limit. If the system was to beoverloaded the noise rise would still stay within the allowed restrictions at theexpense of a less fair system. This is because of the admission control only lettinga fixed number of users accessing the system at the same time. This is also whythe experienced bit rates drop dramatically when overloading the system.

5.2.5 General Performance

When combining results from simulations using different loads and antenna tiltsa number of interesting observations can be made. The benefits of a well tunedtilt in a system with low load is not obvious since system throughput is relativelyconstant regardless of tilt, see Figure 5.13(a). A system with poor antenna tilt andlow load instead utilizes resources less effectively resulting in higher noise rise. Aslong as the maximum noise rise is not reached this will not be transparent just bystudying the system throughput. As system load increases the advantages of a welltilted system becomes more obvious. A maximum throughput is achieved whenadopting a 6-7 degree antenna tilt when the cell radius is 500 meters. This resultwas also supported when user bit rate distributions were studied in Section 5.2.3.There is of course a coupling between cell radius and favorable antenna tilt thatneeds to be taken into consideration when studying a cell radius different from the500 meters as was used in this case.


When studying the noise rise for different antenna tilts in Figure 5.13(b), onecan see that noise rise decreases when increasing the tilt. The effect is moreapparent at higher system load even though visible at lower loads as well. Thesame result could be seen when studying the potentially caused interference inneighboring cells in the static simulations.

The ratio between throughput and noise rise captures both these aspects andreflects the efficiency of the system; how well does the system perform and to whatprice? When the system is loaded relatively low the efficiency seems to increase asthe antenna tilt is increased but experienced bit rates also begin to deteriorate at acertain point as shown in Figure 5.9(a). At higher system load the efficiency peaksat an 8 degree antenna tilt and then drops rapidly. The dramatic performance dropis due to an overtilted antenna resulting in coverage loss and has been consistentthroughout the different simulation measurement results. An undertilted systemis therefore preferable to an overtilted system in order avoid this effect.

0 1 2 3 4 5 6 7 8 90

0.5

1

1.5

2

2.5

Tilt [deg]

Sys

tem

thro

ughp

ut [M

bps]


(a) System throughput versus antenna tilt.

0 1 2 3 4 5 6 7 8 90

1

2

3

4

5

6

7

8

Tilt [deg]

Noi

se r

ise

[dB

]


(b) Noise rise versus antenna tilt.

0 1 2 3 4 5 6 7 8 90

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Tilt [deg]

Sys

tem

thro

ughp

ut o

ver

nois

e ris

e [M

bps/

dB]


(c) System throughput over noise rise versusantenna tilt.

Figure 5.13. CDF of system performance measurements.

5.3 Results Comparison 47

5.3 Results Comparison

5.3.1 Theoretical Assessments and Dynamic Simulations

In order to compare the resource optimization scheme derived in Chapter 3 withthe dynamic simulation results a number of conditions have to be set up.

• The average path gain logged for each user with respect to each cell in thedynamic simulations will be used as input to the theoretical optimization.

It would be unfair to introduce all the users served during the simulation to theoptimizing algorithm since the most favorable users during the entire simulationwould be chosen. This would not represent a snapshot of the simulation which isdesired for a comparison to be made.

• A number of users will therefore be randomly picked to represent a snapshot.This will be done repeatedly to attain more reliable statistics.

• The number of users picked each run corresponds to the average number ofusers accessing the system simultaneously when the system is loaded at thecapacity limit.

0 1 2 3 4 5 6 7 8 90

0.5

1

1.5

2

2.5

3

Tilt [deg]

Sys

tem

thro

ughp

ut [M

bps]

100 % of system capacityTheoretical assessment

Figure 5.14. Comparison between theoretical assessments and dynamic simulationresults. The simultaneous users are randomly picked from each of the 3 simulationseeds 100 times for each antenna tilt. The average result is presented as the theoreticalassessment curve.

The theoretical assessment curve is greatly affected by how the number of si-multaneous users is chosen. The general appearance of the curve is not influencedbut rather the absolute level. The number of simultaneous users should thereforebe chosen carefully to correspond to the dynamic simulation case to which it iscompared. The difference in absolute level is also explained by neglecting multi-path fading in the theoretical assessments. Since the theoretical optimization only


considers a snapshot in time and by using the average path gain for each user themultipath fading is not considered making the result an optimistic assessment.

It is interesting to observe how well the general appearance of the theoreticalcurve corresponds to the simulation result. The performance does however notdrop as rapidly when coverage is uncertain as in the simulation result. This canbe explained by unfairness in the optimization method. The system throughputoptimization compensates the path gain decrease by a more unfair resource distri-bution than in the dynamic simulation case. The scheduler used in the dynamicsimulations allocates the available resources between the users equally while thetheoretical optimization is allowed to distribute resources less fair.

5.3.2 Static and Dynamic Simulations

Figure 5.15 compares the average path gain over bins to the average path gain overusers in the static and dynamic simulations respectively. The analogue comparisonis made for relative path gain. It is interesting to see how well the static simulationresults correspond to the dynamic simulation results.

This implies that interference issues, not focusing on dynamic behavior, couldvery well be studied by performing static simulations which require less computa-tional power.

0 1 2 3 4 5 6 7 8 9−130

−125

−120

−115

−110

−105

−100

Tilt [deg]

Pat

h ga

in [d

B]

Static simulationsDynamic simulations

0 1 2 3 4 5 6 7 8 9−30

−25

−20

−15

−10

−5

0

Tilt [deg]

Rel

ativ

e pa

th g

ain

[dB

] Static simulationsDynamic simulations

Figure 5.15. Comparison between mean of absolute and relative path gain in static anddynamic simulations.

Chapter 6

Conclusions

Interference management in the WCDMA uplink is crucial to attain an efficientsystem since noise rise is considered the primary resource. Contradicting require-ments like extensive coverage and high bit rate availability make the system designcomplicated. The interactive and tight interference coupling in a WCDMA net-work renders the design even more difficult.

The network deployment carries great responsibility in the final system per-formance and end-user experience. Large cells will barely make coverage require-ments while interference issues will be primary in smaller cells. By exploiting theadvantages of directional antennas the interference management can be made moreefficient.

Theoretical assessments showed that users located on the cell coverage overlappose a threat since they utilize significant amount of resources in multiple cells.How to handle such a user is ambiguous since restricted control will result in de-creased bit rate availability while loose control will deprive other users of qualitydue to inefficiency. Not only absolute path gain but also relative path gain ex-perienced by a user showed to be crucial when allocating resources. High systemperformance in the form of system throughput was attained at the expense offairness in the system.

Static path gain map analysis showed that the cell coverage overlap could becontrolled by tuning of the receiving antenna. The ideal is favorable uniform pathgain over the cell dropping to zero at the cell boundary. This is not possiblewith real antennas but the objective is the same when tuning the antenna. Cellcoverage overlap problems showed to be concentrated to the boundaries of cellsbelonging to different sites while the boundaries of cells belonging to the same sitewere barely affected.

Coverage conditions left small margins for antenna tuning in large cells but itwas not required since interference issues showed to be mild. In smaller cells onthe other hand interference issues showed to be evident but resolved by tuning theantenna tilt.

Through dynamic simulations central performance measurements like systemthroughput, noise rise efficiency and user performance could be studied. The

49

50 Conclusions

results were unanimous showing that by optimizing the antenna tilt performanceincreased through

• increased user bit rate,

• increased system throughput and

• more efficient utilization of noise rise resources.

Important to point out is that the different performance improvements werenot trade-offs but attained simultaneous. Best performance was not necessarilyattained when path gain was maximized but rather when cell coverage overlap wasminimized without losing coverage. As system load increased so did the systemthroughput while user experienced bit rate decreased as expected due to usercompetition.

6.1 Future Work

As the evolvement of WCDMA is an ongoing process led by 3GPP so is the needfor further studies in general.

An important feature of the EUL concept is the coexistence with previousreleases of WCDMA. That aspect is not covered in this thesis. In [5] the coexistenceof DCH users and E-DCH users are studied for relatively low bit rates, i.e. 384kbps, compared to the 4 Mbps goal in the EUL concept. As bit rates change sodo the conditions for coexistence and they should therefore be studied.

This thesis focuses on a more general network deployment with uniform cellstructure. More realistic scenarios with embedded microcells and nonuniform cellsoffer additional challenges interesting to study.

Bibliography

[1] Basic concepts of WCDMA radio access networks. Ericsson, white paper.www.ericsson.com/products/white_papers_pdf/e207_whitepaper_ny_k1.pdf(Acc. 2005-01-10).

[2] UMTS antenna model. Kathrein-werke kg, technical report.www.kathrein.de/de/mca/produkte/download/9362051i.pdf(Acc. 2005-03-10).

[3] WCDMA evolved. Ericsson, white paper.www.ericsson.com/products/white_papers_pdf/wcdma_evolved.pdf(Acc. 2005-01-10).

[4] Lars Ahlin and Jens Zander. Principles of Wireless Communications. Stu-dentlitteratur, Second edition, 1998. ISBN 91-44-00762-0.

[5] Erik Axell. Coexistence of Real Time and Best Effort Services in EnhancedUplink WCDMA. Master thesis, Department of Electrical Engineering,Linköping Universitet, Linköping, Sweden, 2005. LITH-ISY-EX-3615-2005.

[6] Stephen Boyd and Lieven Vandenberghe. Convex Optimization. CambridgeUniversity Press, First edition, 2004. ISBN 0521833787.

[7] Shirin Dehghan, Dave Lister, Ray Owen, and Phil Jones. W-CDMA capacityand planning issues. In Electronics & Communication Engineering Journal,volume 12, 2000.

[8] Erik Geĳer Lundin. Uplink Load in CDMA Cellular Systems. Licentiate the-sis, Department of Electrical Engineering, Linköping Universitet, Linköping,Sweden, 2003. ISBN 91-7373-747-X.

[9] Erik Geĳer Lundin, Fredrik Gunnarsson, and Gustafsson Fredrik. Uplink loadestimation in WCDMA. In Proceedings of the IEEE Wireless Communica-tions and Networking Conference, volume 3, 2003.

[10] Harri Holma and Antti Toskala, editors. WCDMA for UMTS. John Wiley &Sons, Ltd, First edition, 2000. ISBN 0-471-72051-8.

51

52 Bibliography

[11] Joonhwan Kim, Young-June Choi, and Saewoong Bahk. Interference-basedcapacity analysis in CDMA cellular systems. In Proceedings of the IEEEWireless Communications and Networking Conference, volume 2, 2003.

[12] Jaana Laiho, Achim Wacker, and Tomás Novosad, editors. Radio NetworkPlanning and Optimisation for UMTS. John Wiley & Sons, Ltd, First edition,2002. ISBN 0-471-48653-1.

[13] Zhuyu Lei, Goodmanm David J., and Narayan B. Mandayam. Location-dependent other-cell interference and its effect on the uplink capacity of acellular CDMA system. In Proceedings of the IEEE Vehicular TechnologyConference, volume 3, 1999.

[14] Seong-Jun Oh and Anthony C.K. Soong. QoS-constrained information-theoretic sum capacity of reverse link CDMA systems. In Proceedings ofthe IEEE Global Telecommunications Conference, volume 1, 2003.

[15] Tero Ojanperä and Ramjee Prasad, editors. Wideband CDMA for ThirdGeneration Mobile Communications. Artech House Publishers, First edition,1998. ISBN 0-89006-735-X.

[16] Ray Owen, Phil Jones, Shirin Dehghan, and Dave Lister. Uplink WCDMAcapacity and range as a function of inter-to-intra cell interference: theory andpractice. In Proceedings of the 51st IEEE Vehicular Technology Conference,volume 1, 2000.

[17] Ramjee Prasad, Werner Mohr, and Walter Konhäuser, editors. Third Genera-tion Mobile Communication Systems. Artech House Publishers, First edition,2000. ISBN 1-58053-082-6.

[18] John G. Proakis. Digital Communications. McGraw-Hill, Third edition, 1995.ISBN 0-07-051726-6.

[19] Danlu Zhang, Seong-Jun Oh, and Nagabhushana T. Sindhushayana. Optimalresource allocation for data service in CDMA reverse link. In Proceedings ofthe IEEE Wireless Communications and Networking Conference, volume 3,2004.

Appendix A

Decibel

A.1 dB

Decibel (dB) is a logarithmic unit used to describe a ratio between a measuredvalue and a reference value, the ratio is therefore unit less. The logarithmic func-tion is defined as follows.

x with respect to y in dB = 10 log10

(

x

y

)

dB

Path gain is often referred to in dB and the ratio studied is the signal attenuationbetween two points.

A.2 dBW and dBm

The dBW notation implies a ratio where 1 W is used as reference and similarlydBm corresponds to 1 mW being used as reference.

10 dBW = 10 log10

(

10

1

)

= 10 log10

(

10000

0.001

)

= 40 dBm

A.3 dBi

dBi is the gain offered by an antenna relative an isotropic antenna. An isotropicantenna has a uniform gain in all direction thus 0 dBi in all directions.

A.4 dBd

dBd is the gain offered by an antenna relative a dipole antenna. Experimentmeasurements have shown that a dipole antenna performs 2.14 dB over an isotropicantenna thus 0 dBd corresponds to 2.14 dBi.

53

Appendix B

Taylor Expansion of the

Objective Function

The elements in the sum in the objective function in (3.23) can be written as,

R = C ln

(

1 +β

1 − (1 − α)β

)

= C ln

(

1 + αβ

1 − (1 − α)β

)

= C (ln (1 + αβ) − ln (1 − (1 − α) β)) .

(B.1)

The natural logarithmic function can be written using its Taylor expansion.

ln(1 + x) = x −x2

2+ · · · + (−1)n−1 xn

n+ · · · when − 1 < x ≤ 1 (B.2)

The variables in (B.1) fulfil the Taylor expansion requirement.

0 ≤ α ≤ 10 ≤ β < 1

⇒−1 < αβ ≤ 1−1 < − (1 − α)β ≤ 1

Equation (B.1) can therefore be approximated with a quadratic function.

R = C (ln (1 + αβ) − ln (1 − (1 − α) β))

≈ C

(

αβ −α2β2

2+ (1 − α)β +

(1 − α)2 β2

2

)

= C

(

αβ −α2β2

2+ β − αβ +

β2

2+

α2β2

2−

2αβ2

2

)

= C

(

β +β2

2−

2αβ2

2

)

= C

(

β +1 − 2α

2β2

)

(B.3)

54

Appendix C

Cumulative Distribution

Function

−300 −250 −200 −150 −100 −50 0 50 100 150 200 250 3000

100

200

300

400

500

600

700

800

900

1000

(a) Normally distributed samples.

−300 −250 −200 −150 −100 −50 0 50 100 150 200 250 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(b) Cumulative histogram of Figure C.1(a).

−300 −200 −100 0 100 200 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(c) Resulting CDF.

Figure C.1. Illustration of the cumulative distribution function (CDF).

55

Appendix D

Dynamic Simulation Results

Simulation results using different system loads1 are presented in this appendix.The parameter settings used to obtain these results are presented in Table D.1.

Table D.1. Parameter settings used in the dynamic simulations.

General settings Only uplink simulated YesOnly E-DCH users simulated YesIdeal noise rise estimation YesMaximum noise rise 7 dBSimulation time 200 sNumber of seeds 3

Network deployment Cell radius 500 mSites 7Cells per site 3Vertical antenna model Yes

Propagation model Multipath fading model 3GPP Typical UrbanOrthogonality factor, α 0

E-DCH Maximum bit rate 4 MbpsPacket size 1 MBTTI 2 msHARQ with soft combining YesParallel HARQ processes 8Outer loop BLER target 0.1Transmission attempts 1RBS scheduling YesMaximum user transmission power 21 dBm

SHO Maximum number of connections 3Acceptance threshold -2 dBRelease threshold -4 dB

1See Section 5.2.1 for definition of system capacity.

56

57

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User delay [s]



−140 −130 −120 −110 −100 −90 −800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User path gain [dB]


(c) CDF of user path gain.

−60 −50 −40 −30 −20 −10 00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1



(d) CDF of relative user path gain.

0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Noise rise [dB]


(e) CDF of noise rise.

Figure D.1. 25 percent of system capacity.

58 Dynamic Simulation Results

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User delay [s]



−140 −130 −120 −110 −100 −90 −800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User path gain [dB]



−60 −50 −40 −30 −20 −10 00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Noise rise [dB]




59

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User delay [s]



−140 −130 −120 −110 −100 −90 −800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User path gain [dB]



−60 −50 −40 −30 −20 −10 00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Noise rise [dB]




60 Dynamic Simulation Results

0 0.5 1 1.5 2 2.5 3 3.5 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User delay [s]



−140 −130 −120 −110 −100 −90 −800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User path gain [dB]



−60 −50 −40 −30 −20 −10 00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1




0 1 2 3 4 5 6 7 8 9 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Noise rise [dB]




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