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Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

Coexistence of Real Time and Best Effort Servicesin Enhanced Uplink WCDMA

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Erik Axell

LITH-ISY-EX-3615-2005

Linköping 2005

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

Coexistence of Real Time and Best Effort Servicesin Enhanced Uplink WCDMA

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Erik Axell


Handledare: Erik Geĳer Lundinisy, Linköpigs universitet

Eva EnglundEricsson Research, Linköping

Examinator: Fredrik Gunnarssonisy, Linköpigs universitet

Linköping, 28 January, 2005

Avdelning, InstitutionDivision, Department

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

DatumDate

2005-01-28

SpråkLanguage

¤ Svenska/Swedish

¤ Engelska/English

¤

£

RapporttypReport category

¤ Licentiatavhandling

¤ Examensarbete

¤ C-uppsats

¤ D-uppsats

¤ Övrig rapport

¤

£

URL för elektronisk version

http://www.ep.liu.se/exjobb/isy/2005/3615

ISBN

—

ISRN


Serietitel och serienummerTitle of series, numbering

ISSN

—

TitelTitle

Samexistens av Realtidstjänster och Förbättrade Datatjänster i WCDMA UpplänkCoexistence of Real Time and Best Effort Services in Enhanced Uplink WCDMA

FörfattareAuthor

Erik Axell

SammanfattningAbstract

The increasing use of data services and the importance of IP based services inthird generation mobile communication system (3G), requires the transmissionfrom the cell phone to the base station, i.e. uplink, to manage high speed datarates. In the air interface for 3G in Europe, WCDMA, a concept for enhancingthe transmission from the cell phone to the base station, called Enhanced Uplink,is being standardized. The overall goal is to provide high speed data access forthe uplink. One of the requirements is that the enhanced uplink channels must beable to coexist with already existing WCDMA releases. For example, the enhanceduplink must not impact seriously on real time services, such as speech, carried oncurrent WCDMA channels.

The purpose of this work is to study how the quality, coverage and capacity ofreal time services carried on previous WCDMA releases is affected when introduc-ing the Enhanced Uplink in a WCDMA network. The main focus of the study isthus to demonstrate the trade-off between voice and best effort performances.

Theoretical assessments and simulations show that the Enhanced Uplink hasmany advantages over previous WCDMA releases. For example the EnhancedUplink yields a larger system throughput for all voice loads. The noise rise, i.e.the ratio of total received power to the background noise power is being consideredas the resource. It is shown that user traffic carried on the Enhanced Uplink isable to operate under a higher noise rise level as well as to get a higher throughputper noise rise. The resource is hence more efficiently utilized.

NyckelordKeywords 3G, UMTS, WCDMA, Enhanced Uplink, E-DCH

AbstractThe increasing use of data services and the importance of IP based services inthird generation mobile communication system (3G), requires the transmissionfrom the cell phone to the base station, i.e. uplink, to manage high speed datarates. In the air interface for 3G in Europe, WCDMA, a concept for enhancingthe transmission from the cell phone to the base station, called Enhanced Uplink,is being standardized. The overall goal is to provide high speed data access forthe uplink. One of the requirements is that the enhanced uplink channels must beable to coexist with already existing WCDMA releases. For example, the enhanceduplink must not impact seriously on real time services, such as speech, carried oncurrent WCDMA channels.

The purpose of this work is to study how the quality, coverage and capacityof real time services carried on previous WCDMA releases is affected when intro-ducing the Enhanced Uplink in a WCDMA network. The main focus of the studyis thus to demonstrate the trade-off between voice and best effort performances.

Theoretical assessments and simulations show that the Enhanced Uplink hasmany advantages over previous WCDMA releases. For example the EnhancedUplink yields a larger system throughput for all voice loads. The noise rise, i.e.the ratio of total received power to the background noise power is being consideredas the resource. It is shown that user traffic carried on the Enhanced Uplink isable to operate under a higher noise rise level as well as to get a higher throughputper noise rise. The resource is hence more efficiently utilized.

v

Acknowledgements

I have had the opportunity to perform my master thesis work at Ericsson Researchin Linköping. It has been a great time and I have met a lot of inspiring andcompetent people working at the front line of telecommunication technology. Iwould like to thank you all for great commitment and interest in my work and,most important, for making me feel welcome.

Special thanks to my supervisor Eva Englund for guiding me through the workand taking time to answering my questions. Thanks also to Ke Wang Helmerssonfor all the help with the simulations.

I would also like to thank my supervisor at the University, Erik Geĳer Lundinand my examiner Fredrik Gunnarsson for support, valuable comments on the workand for all the help with LATEX.

Finally, I would like to thank Andreas Bergström, parallel master thesis stu-dent and opponent, for good company and for helpful discussions and comments.

Erik AxellLinköping, January 2005

vii

Abbreviations and Acronyms

3G 3rd Generation mobile communication system3GPP 3rd Generation Partnership ProjectACK AcknowledgementBLER Block Error RateCDMA Code Division Multiple AccessDCH Dedicated Channel (transport channel)DS-CDMA Direct Sequence Code Division Multiple AccessE-DCH Enhanced Dedicated Channel (transport channel)EUL Enhanced UplinkFDD Frequency Division DuplexGSM Global System for Mobile communicationHARQ Hybrid Automatic Repeat RequestHSDPA High Speed Downlink Packet AccessIP Internet ProtocolISI Inter Symbol InterferenceITU International Telecommunication Unionkbps Kilobits per secondMbps Megabits per secondMMS Multimedia Messaging ServiceNACK Negative AcknowledgementNode B Base stationPC Power ControlQoS Quality of ServiceRAN Radio Access NetworkRLC Radio Link ControlRNC Radio Network ControllerRTT Round Trip TimeRx ReceiverSIR Signal to Interference RatioSNR Signal to Noise Ratio

ix

x

TCP Transmit Control ProtocolTDD Time Division DuplexTTI Transmission Time IntervalTx TransmitterUE User EquipmentUMTS Universal Mobile Telecommunication ServicesWCDMA Wideband Code Division Multiple Access

Contents

1 Introduction 11.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Third Generation Mobile Communication System 52.1 UMTS Network Architecture . . . . . . . . . . . . . . . . . . . . . 52.2 Introduction to WCDMA . . . . . . . . . . . . . . . . . . . . . . . 72.3 WCDMA Evolvement . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.1 Enhanced Uplink . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Theoretical Assessments 113.1 Pole Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Resource Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.3 Maximum Number of Users . . . . . . . . . . . . . . . . . . . . . . 173.4 Throughput per Noise Rise . . . . . . . . . . . . . . . . . . . . . . 203.5 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4 Simulation Model 254.1 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1.1 Shadow Fading . . . . . . . . . . . . . . . . . . . . . . . . . 254.1.2 Multipath Fading . . . . . . . . . . . . . . . . . . . . . . . . 26

4.2 Simulation Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2.2 Cell Deployment . . . . . . . . . . . . . . . . . . . . . . . . 264.2.3 User Placement . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3.1 Fast HARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3.2 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . 284.3.3 Admission Control . . . . . . . . . . . . . . . . . . . . . . . 284.3.4 Fast Rate Control . . . . . . . . . . . . . . . . . . . . . . . 29

4.4 Simulation Logging . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

xi

5 Simulations and Results 315.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . 315.2 Evaluation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . 325.3 Voice Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.4 Simultaneous Voice and Best Effort Data . . . . . . . . . . . . . . 35

5.4.1 At the Capacity Limit . . . . . . . . . . . . . . . . . . . . . 38

6 Conclusions 45

Bibliography 47

Chapter 1

Introduction

The introduction of the third generation mobile communication system (3G) openedup new doors in wireless communication. The possibilities of transmitting all kindsof data between cellular phones has increased enormously during the last decade.The air interface standard for 3G in Europe, called WCDMA, makes it possibleto use the cellular phone for web surfing, e-mailing, interactive gaming, videostreaming and receiving several other data services like multimedia, video-clipsand pictures.

However, new features create new demands on the communication system.In the WCDMA specifications a concept called high speed downlink packet access(HSDPA) has been evolved. This concept makes it possible to transmit high speeddata from the base station to the cell phone, i.e. downlink.

The increasing use of data services and the importance of IP based servicesalso requires the transmission from the cell phone to the base station, i.e. uplink,to manage high speed data rates. The standardization body for WCDMA is the3rd generation partnership (3GPP). Within 3GPP a concept for enhancing thetransmission from the cell phone to the base station, called Enhanced Uplink(EUL), is being standardized. The overall goal is to provide high speed dataaccess also for the uplink. One of the requirements that has been agreed uponwithin 3GPP is that the enhanced uplink channels must be able to coexist withalready existing WCDMA releases. For example, the enhanced uplink must notimpact seriously on real time services, such as speech, carried on current WCDMAchannels.

1.1 Problem StatementThe master thesis assignment is to study how the quality, coverage and capacityof real time services carried on previous WCDMA releases is affected when intro-ducing the Enhanced Uplink in a WCDMA network. Coverage is the geographicalarea within which a user can connect to the mobile network with acceptable qual-ity. Capacity is the number of users or the total amount of data bits per timeinstant that can be supported by the system. Specifically the impact on quality

1

2 Introduction

and capacity of speech carried on previous WCDMA releases when introducingthe Enhanced Uplink in the same network will be studied.

The aim is to demonstrate the trade-off between real time and best effortperformances and to investigate possible solutions to mitigate the impact on realtime services.

1.2 Related WorkAs far as we know, no previous work has been performed on evaluating simultane-ous voice and Enhanced Uplink users. However, a large amount of work has beendone on performance measures and evaluations of CDMA systems. The followingsection will give an outline of such previous related work.

Quality is closely connected to both capacity and coverage measures. Capacityis most often measured as a maximum throughput while still keeping the qualityrequirements fulfilled, and coverage as a geographical area within which the qualityrequirements fulfilled. Therefore no specific section for the quality measure isgiven.

1.2.1 CapacitySeveral publications deal with the problem of estimating the system capacity,e.g. [8] and [19] consider an integrated voice/data WCDMA system. Both definecapacity as the maximum amount of users for which the probability that they aresatisfied is greater than some certain percentage. In the latter a user is definedsatisfied when the probability that the bit error rate is below some threshold, isgreater than some value. Since the bit error rate is directly related to the receivedSIR, the satisfaction measure can also be formulated as the probability that thereceived SIR is greater than some threshold. The capacity region, in terms ofnumber of users, is obtained for an integrated speech and long constraint delaydata system.

This measure is also exemplified in [8], however a more general method isintroduced. Furthermore the capacity is evaluated and optimized. It is shownthat the total system capacity is maximized when the per-service capacities forall bearer services are equal. It is also shown that there is a linear relationshipbetween speech capacity and interactive data capacity.

Another common measure for data capacity is the system throughput, i.e. themaximum possible transmission data rate (often in kbps) as a function of receivedinterference power or noise rise. This measure is used in [5] to evaluate the uplinkcapacity gain in a WCDMA network due to faster scheduling. A capacity gain ofapproximately 10% is shown, simply by reducing the packet scheduling intervalfrom 500 ms to 100 ms.

The quality in terms of bit error rates and blocking probabilities for voicecalls in an integrated voice/data DS-CDMA network is discussed in [9]. The biterror rate is derived as a function of the number of voice and data calls, andthe relationship is used to determine the capacity of the network. The tradeoffbetween delay and capacity for data users is also illustrated.

1.2 Related Work 3

An optimization approach to support multimedia services in CDMA is pre-sented in [15]. The total transmitted power is minimized and the sum of ratesis maximized while all QoS constraints are still fulfilled. Each user specifies aQoS in form of maximum bit error rates that are mapped into equivalent SIRrequirements. The solutions are plotted as number of voice users against numberof data users, which yields a capacity region. The capacity region is defined bya straight line between approximately 32 speech users and 14 data users with bitrate 20 kbps.

Speech capacity is often measured in Erlangs, i.e. the traffic density computedas the ratio between call arrival rate and call departure rate. In [16], an integratedvoice/data CDMA system is considered, and the voice and data capacity in Erlangsis calculated both analytically and numerically. In [16] perfect power control aswell as imperfect power control is investigated with both Gaussian and Lognormalapproximation of the traffic load. A similar assessment on a DS-CDMA system isperformed in [11]. The two latter works only consider the single cell case.

In [13], the so called F-factor is introduced, defined as F = Ioc

Ioc+Iscwhere Ioc

is the interference power caused by users in the own cell (intra cell) and Isc is theinterference caused by users in the surrounding cells (inter cell). The difficultiesand possibilities of deriving the F-factor is discussed and the capacity in terms ofnumber of users is estimated.

Both [4] and [12] describes pole capacity, but with a slightly different approach.In [12] the results are also verified in simulations, while [4] gives a more generaltheoretical account. Pole capacity will be discussed in more detail in Section 3.1.

1.2.2 CoverageA method of calculating the inter and intra cell interference in a UMTS system isdiscussed in [18]. The interference is calculated by solving a system of fixed-pointequations, using an iteration algorithm. Furthermore, the result is used to reckonthe cell coverage area as the distance within which the outage probability is lessthan a certain percentage.

An analytical approach to determine coverage probabilities are given in [17].An algorithm is used where the interference at all cells, the transmission powersof all mobiles and the probability function of received powers at the base stationsare calculated. From this the coverage probability for a given mobile is reckoned.

Another analytical approach is described in [14]. The uplink coverage is in-vestigated in a UMTS system under non-homogenous and moving traffic load. Inparticular the coverage is investigated for different call assignment policies andfor a hot spot moving among the cells. Inter cell interference is also taken intoaccount. Some numerical results to verify the analysis are also given.

4 Introduction

1.3 Research ApproachTo achieve the thesis goals the study have been made in the following steps:

• Literature study on mixing real time and best effort services in uplink inCDMA-systems, particularly on various ways of evaluating the system qual-ity, coverage and capacity. An outline of this was presented in the previoussection. An introduction to the third generation mobile communication sys-tem and a description of WCDMA evolution to the Enhanced Uplink is givenin Chapter 2.

• Theoretical assessments, also including model description and methods ofevaluating and comparing the performance of speech versus data. The the-oretical assessments and results are described in more detail in Chapter 3.

• Simulation scenario design. The most important simulation models and as-sumptions as well as the simulation scenarios are described more in Chapter4.

• Evaluation methods and criterions selection. The performance measures andevaluation criteria used to evaluate the simulations are presented in sections5.1 and 5.2 respectively.

• Simulations and evaluation of simulation results. Comparison with the the-oretical assessments. The simulations and results are presented in sections5.3 and 5.4.

• Documentation and conclusions from the results. The conclusions of thestudy are presented in Chapter 6.

Chapter 2

Third Generation MobileCommunication System

The standard for third generation mobile communication system in Europe isreferred to as Universal Mobile Telecommunication Services (UMTS), adoptedby the International Telecommunication Union (ITU). The air interface used inUMTS is Wideband Code Division Multiple Access (WCDMA). The followingsections will give an overview of WCDMA for UMTS and it’s evolution to theEnhanced Uplink concept. More about UMTS can also be found in [6], [1] and[10] and the basic principles of wireless communications are described in [3].

2.1 UMTS Network Architecture

The UMTS network consists basically of a core network, Radio Network Con-trollers (RNC), base stations (Node B) and user equipments (UE). Figure 2.1shows a schematic picture of the UMTS network and it’s elements.

The core network is the connection to an external network such as the internetor the ordinary fixed telephony system. The UE can be a mobile phone or acomputer card. The RNC and Node B constitute the Radio Access Network(RAN), or the connection between the UE and the core network.

Each RNC controls a number of Node Bs. The actual radio signal is transmittedand received by a Node B. Each Node B supports one or a number of cells coveringa geographical area. If the antenna for example is of a three sector type, the NodeB consists of three cells. Each UE within a cell area is connected to a certain NodeB. The cells normally intersect near the cell borders, and UEs positioned in thisarea are connected to more than one Node B.

Handover is executed when a user moves between cells. Soft handover is whenthe UE is connected to several Node Bs, and softer handover is when the UE isconnected to several cells within the same Node B, see Figure 2.2. Soft and softerhandover enables the UE to maintain the continuity and quality of the connectionwhile moving from one cell to another.

5

6 Third Generation Mobile Communication System

RNCRNC RNC

Core network

Figure 2.1. UMTS architecture.

Node B Node B

RNC

Soft handover

Softer handover

Figure 2.2. Schematic description of soft and softer handover.

2.2 Introduction to WCDMA 7

2.2 Introduction to WCDMA

In previous generations of mobile communication systems, users are separatedby transmitting in different time slots and/or using different frequencies. InWCDMA users are separated through Code Division Multiple Access (CDMA).In this scheme each user is assigned on a unique code which makes it possible forseveral users to transmit on the same frequency at the same time.

WCDMA uses Direct Sequence CDMA. The original signal is spread by amultiplication with a spreading code, consisting of a sequence of 1 and -1 bits,also called chips. The spreading codes are chosen from the code tree in Figure 2.3.The different levels of the code tree corresponds to different code lengths. Once auser has been dedicated a code, no code in the subtree of that code can be used.This preserves orthogonality between codes, even for codes with different lengths.

c

(c, c)

(c, -c)

c1,1 = (1)

c2,1 = (1, 1)

c2,2 = (1, -1)

c4,1 = (1, 1, 1, 1)

c4,2 = (1, 1, -1, -1)

c4,3 = (1, -1, 1, -1)

c4,4 = (1, -1, -1, 1)

…

Figure 2.3. Channelization code tree.

Figure 2.4 shows the principal of spreading and despreading. In this example,every data symbol is multiplied by a spreading code sequence of 8 chips. We sayin this case that we have used a spreading factor of 8, i.e. the ratio of the chiprate to the data rate.

The multiplication with a spreading code with chip rate larger than the datarate results in an ostensibly random signal. Multiplying the spread signal withthe spreading code again, i.e. despreading, restores the signal to it’s original, seeFigure 2.4. A multiplication of the spread signal with the wrong spreading codewould result in a signal looking like noise.

The signal bandwidth is proportional to the bit rate. Since the signal is spreadby the spreading factor, the bandwidth will also widen with the spreading factor.WCDMA uses a chip rate of 3.84 Mcps, which yields a bandwidth of approximately5 MHz. Compared to other CDMA technologies, this bandwidth is wider, andhence the name Wideband CDMA.


Data

Spreading code

Spread signal= data x code

1-1

Spreading code

1-1

Spreading

Despreading

Symbol

Chip

Data= spread signal x code

1-1

1-1

1-1

Figure 2.4. Spreading and despreading in DS-CDMA.

Figure 2.5 shows the bandwidth widening as a consequence of spreading, andhow the receiver easily can find and decode the correct signal.

WCDMA supports two ways of separating downlink and uplink. The separa-tion can be done either by Frequency Division Duplex (FDD) or Time DivisionDuplex (TDD). The FDD mode is the one that operators are now deploying inWCDMA, and TDD mode will thus not be considered in the sequel.

2.3 WCDMA EvolvementThe WCDMA standard is continuously evolving. Figure 2.6 shows the WCDMAevolvement in principle. In the standardized air interface WCDMA release 5, aconcept for high bit rate downlink transmission was introduced, called High SpeedDownlink Packet Access (HSDPA).

Furthermore, the increasing use of data services and the importance of IPbased services also requires the uplink transmission to manage high speed datarates. Within the 3rd generation partnership (3GPP) a concept for enhancing thetransmission from the cell phone to the base station, called Enhanced Uplink, isbeing developed. The basic functions of Enhanced Uplink are described more inthe following section. The basics of HSDPA can be found in [7].

2.3.1 Enhanced UplinkMany solutions from HSDPA are also used in the Enhanced Uplink, but there arealso differences in downlink and uplink. The Enhanced Uplink concept is describedin more detail in [2]. The overall goal is to improve coverage and throughput aswell as to reduce the delay of the uplink. Among the requirements that havebeen agreed on within 3GPP is that the enhanced uplink channels must be able

2.3 WCDMA Evolvement 9

frequency

power

frequency

power

frequency

power

frequency

power

Spreading

Despreading

a b

c d

Figure 2.5. The spread signal in (b) occupies a larger bandwidth than the originalsignal in (a). Many user transmits at the same time in (c). After despreading in (d) thecorrect signal can easily be found.

Enhanced Uplink

Additional enhancements

Enhanced Downlink(HSDPA)

Rel 4 Rel 5 Rel 6

WCDMAWCDMA EvolvedEvolvedWCDMAWCDMA

R99

Enhanced Uplink

Additional enhancements

Enhanced Downlink(HSDPA)

Rel 4 Rel 5 Rel 6

WCDMAWCDMA EvolvedEvolvedWCDMAWCDMA

R99

Figure 2.6. Overview of the WCDMA evolvement


to coexist with already existing WCDMA releases. For example, the enhanceduplink must not impact seriously on real time services, such as speech, carried oncurrent WCDMA channels.

Basic Principles

The enhanced uplink introduces a new transport channel, the Enhanced DedicatedChannel (E-DCH). A dedicated channel (DCH) is assigned to only one UE at atime. A DCH is power controlled, meaning that the transmitter power is increasedif the channel is bad and, important in the uplink, the power is limited if too muchinterference is caused by the user. The E-DCH may transmit on a 2 ms basis,which is five times as often as transmission on the previous DCH. The shorterTransmission Time Interval (TTI) reduces uplink delays and makes it possibleto retransmit faster and to adapt faster to the system interference. E-DCH alsosupports 10 ms TTI in order to coexist with previous WCDMA releases. TheE-DCH also supports the following new features, which improves system capacity.

- Fast hybrid automatic repeat request (HARQ). The enhanced uplink sup-ports Node B controlled retransmissions, unlike previous WCDMA releaseswhere retransmissions are controlled from the RNC. By rapidly request re-transmission of erroneous data, the delays are reduced essentially and thecapacity is increased. The enhanced uplink also uses soft combining in theNode B, which means that data blocks that can not be correctly decodedare saved and combined with later retransmissions of the same blocks tofind the correct data. With soft combining the number of retransmissionsare reduced. Fast HARQ with soft combining leads to higher capacity androbustness against link adaption errors.

- Fast rate control. Since the uplink is interference limited a fast adaptionto the interference conditions is necessary. In the Node B the uplink datarate for each user is controlled. Moving the rate control to the Node Breduces delays which leads to a rapid adaption and a tight control of uplinkinterference. The fast rate control also allows admission control in the RNCto be more relaxed. A larger number of bursty high bit rate users can beallowed. This in all yields a higher uplink capacity.

The E-DCH also supports soft handover, just like previous WCDMA releases.During handover between cells the user is connected to both (in some cases eventhree) cells at the same time. This allows power control from multiple cells andis required to limit the inter cell interference, which is an important issue in theuplink.

In previous WCDMA releases both voice and best effort users are carried onthe DCH. However, in the sequel of this work DCH refers to previous WCDMArelease channels carrying best effort users, i.e. without the Node B controlledretransmissions and rate control, and with 10 ms TTI. Voice users are simplynamed voice or speech. In the same way E-DCH refers to best effort users carriedon the enhanced uplink supporting 2 ms TTI and the features mentioned above.

Chapter 3

Theoretical Assessments

As mentioned before there are several ways of measuring system performance. Weconsider pole capacity and resource efficiency and assess the maximum number ofusers by combining these two quantities. The last section of this chapter will takecoverage in consideration. Since the quality measure is incorporated in the othermeasures, no specific analysis of quality will be performed.

When talking about capacity one can consider hard capacity or soft capacity,where soft capacity depends on factors like where the users are positioned. It canbe shown that soft capacity is a more optimistic measure than hard capacity. Weonly consider hard capacity measures.

We aim on showing that the enhanced uplink has many advantages over pre-vious WCDMA releases in many aspects, mainly in a better resource utilizationand hence a larger amount of users and higher throughput.

3.1 Pole CapacityIn this section we assume perfect power control and unlimited UE transmitterpower. Perfect power control means that the UE power is controlled such that thereceived SIR is always equal to the target SIR. With these assumptions the noiserise level will for a certain traffic load approach infinity. The maximum numberof users where the noise rise level approaches infinity is called pole capacity. Ananalytical expression as well as a numerical approximation of the pole capacity isderived.

Assuming perfect power control yields that all voice users cause the same re-ceived power and, if we also assume all data users have the same bit rate, all datausers cause the same received power. The SIR targets, γv and γd, for voice anddata respectively can be expressed

γv =Pv

Itot − (1− ω)Pv, γd =

Pd

Itot − (1− ω)Pd, (3.1)

where ω is the own signal interference, Pv, Pd are the received powers from avoice and data user respectively and Itot is the total received interference. The

11

12 Theoretical Assessments

own signal interference is caused by time dispersion due to multipath propagation.Some parts of the signal are so delayed that they interfere with the next symbol.This phenomena is known as inter symbol interference (ISI), and can be surpressedwith more advanced receiver types. However with a standard RAKE receiver,the own signal interference can be significant. A RAKE receiver is basically acollection of multiple correlation receivers, called fingers, who combine all of thereceived signals. Here the own signal interference is assumed constant and equalfor all users. This is a simplification, in reality it varies a lot from user to user andin time.

The total interference,

Itot = N0 +N∑

n=1

Pi + Isc,

where N0 is the background noise power, Pi is the received power from user i,N is the total number of users in the cell and Isc is the interference power fromsurrounding cells. We denote the received powers from all voice users and all datausers in the own cell Iv and Id respectively, such that Iv + Id =

∑Nn=1 Pi. The

interference caused by users in the own cell is hence Ioc = Iv + Id. Approximat-ing the interference from surrounding cells, Isc, by a factor ξ times the own cellinterference yields the following expression for the total interference.

Itot = N0 + Ioc + Isc = N0 + (1 + ξ)Ioc = N0 + (1 + ξ)(Iv + Id)

Solving (3.1) for Pv and Pd yields

Pv =Itot

1γv

+ (1− ω), Pd =

Itot1γd

+ (1− ω). (3.2)

We define the system noise rise, which is considered as the limiting system resourcesince the uplink is mainly interference limited.

Definition 3.1.1 (Noise rise) Noise rise, η, is defined as the ratio of the totalreceived power to the background noise power, i.e.:

η =Itot

N0.

¤Definition 3.1.1 also yields

ηN0 = Itot = N0 + (1 + ξ)(Iv + Id), (3.3)

where ξ is the expansion factor. According to the definition above

ξ =Isc

Ioc.

The factor 1 + ξ is equal to the inverse of the F-factor [13], previously defined insection 1.2.1, i.e. 1 + ξ = Ioc+Isc

Ioc= 1

F .We define a service mix as a vector whose elements are the fractional contri-

butions of each bearer service to the expected total number of users [8].

3.2 Resource Efficiency 13

Definition 3.1.2 (Service mix)

α = (αv, αd) = (uv

utot,

ud

utot)

where uv, ud is the expected number of voice and data users respectively andutot = uv + ud is the expected total number of users.

¤

The assumption that all voice users cause the same received power, Pv, and alldata users cause the same received power, Pd, together with the service mix, α,transforms (3.3) to

ηN0 = N0 + (1 + ξ)(Pvuv + Pdud) = N0 + (1 + ξ)utot(Pvαv + Pdαd). (3.4)

Combining (3.2), (3.4) and Definition (3.1.1) and solving this for η yields

η =1

1− (1 + ξ)utot( αv1

γv+1−ω

+ αd1

γd+1−ω

)(3.5)

Since η > 0 per definition we conclude from the above equation that

(1 + ξ)utot(αv

1γv

+ 1− ω+

αd1γd

+ 1− ω) ≤ 1, (3.6)

for any stable system. Letting (3.6) approach equality implies η → ∞, and fromthis we get the pole capacity.

utot =1

(1 + ξ)( αv1

γv+1−ω

+ αd1

γd+1−ω

). (3.7)

This expression for pole capacity will be exemplified later in Example 3.2, Section3.3.

3.2 Resource EfficiencyAmong the expectations of the enhanced uplink is a better resource utilization.Thanks to fast rate control, we have a more efficient use of the available resource.In this section we give a description of the utilization of the resource and also givesome propositions and explanations to it’s behaviour.

Some useful variables are defined to describe the model. At this stage weconsider the multiple service case, i.e. there are several different services simul-taneously in the system. Each service has some specific quality of service (QoS)requirements, such as maximum delay, maximum block error rate (BLER), mini-mum bit rate and so on. For a user to be satisfied, all of the requirements for thecurrent service must be fulfilled. This leads to the definition of our first quantity,maximum noise rise.


Definition 3.2.1 (Maximum noise rise) The maximum noise rise allowed inthe cell, i.e. the maximum noise rise at the receiving base station such that all ofthe QoS requirements are fulfilled, is denoted ηmax.

¤

Note that the noise rise, η, is a stochastic process that varies over time and fromone cell to another. Hence it is valuable to consider the expectation value of thenoise rise.

Definition 3.2.2 (Maximum expected noise rise) The maximum expected noiserise, η, is defined as

η = max{E[η] : Pr(η ≥ ηmax) ≤ P},

where η is the system noise rise.

¤

Furthermore, we define the resource efficiency, ρ, as the ratio of the maximum ex-pected resource utilization, i.e. the maximum expected noise rise, to the availableresource, i.e. the maximum allowed noise rise.

Definition 3.2.3 (Resource Efficiency)

ρ =η

ηmax

¤

This might seem as a bad measure of how good the resource is being utilized. Itshould be no problem to get a high noise rise without getting a good throughput.However it is a good measure of how much of the resource is being utilized. As-suming that the amount of utilized resource is correlated to a good throughputjustifies this measure. That this is in fact the case is shown in Section 3.4 andlater in simulations.

Now the single service case is considered. The single service case means thereis only one service type in the system. We consider single service systems withdifferent services n, for example only voice users or only best effort users. Byanalogy with Definition 3.2.2 the maximum expected noise rise for a single servicen is defined.

Definition 3.2.4 (Maximum expected noise rise per service) The maximumexpected noise rise for a single service n, is defined as:

ηn = max{E[ηn] : Pr(ηn ≥ ηmax) ≤ P}

¤

3.2 Resource Efficiency 15

For the single service case the maximum expected noise rise is of course the sameas the maximum expected noise rise for the single service.

The resource efficiency for a single service is defined analogous to the previousdefinition.

Definition 3.2.5 (Resource efficiency per service) The resource efficiency fora single service n is defined as:

ρn =ηn

ηmax.

¤

Again this of course looks exactly the same as Definition 3.2.3, since the singleservice case is only a special case of the multiple service case.

Now the interesting problem is to find out if and, if so, how the resourceefficiency depends on the service mix. To do some valuable evaluation of theresource efficiency we have to make some assumptions about the system behaviour.The sequel of this section will describe some possible assumptions and how thiswill affect the resource efficiency. We will only consider the two service case withsimultaneous voice and best effort data users.

Assumption 3.2.1 Resource efficiency, ρ, is independent of the service mix, α.

Assumption 3.2.2 Resource efficiency, ρ, depends linearly on the single serviceresource efficiencies according to:

ρ = βvρv + βdρd,

where βv and βd are parameters dependent on the service mix, α.

For the latter assumption, the question is how βv and βd depend on the servicemix. All reasonable parameters βv, βd must apply to the following relation:

βv = 0 ⇐⇒ βd = 1 and βv = 1 ⇐⇒ βd = 0. (3.8)

A reasonable assumption would be that βv and βd somehow relates to the inter-ference contribution per service for a given α, or rather the fraction of interferencecaused by each service , i.e.

βv =Iv

Iv + Id, βd =

Id

Iv + Id, (3.9)

where Iv, Id is the interference caused by voice users and data users, respectively.This definition yields βv + βd = 1, and hence (3.8) is fulfilled. We will now tryto find an expression for the coefficients βv and βd. To start with we consideronly the voice coefficient, βv. The interference caused by voice users can also beexpressed as:

Iv = Pvuv = Pvαvutot, (3.10)


where Pv is the received power from one voice user. Analogous we will get thesame expression for Id. Equations (3.9) and (3.10) yield the following expressionfor βv.

βv =Pvαv

Pvαv + Pdαd

Using expressions (3.2) for the received powers yields

βv =

Itot1

γv+(1−ω)

αv

Itot1

γv+(1−ω)

αv + Itot1

γd+(1−ω)

αd

=1

1 +1

γv+(1−ω)

1γd

+(1−ω)· αd

αv

In the same way for data users we get

βd =1

1 +1

γd+(1−ω)

1γv

+(1−ω)· αv

αd

Assume{

1− ω ¿ 1γv

1− ω ¿ 1γd

. This is reasonable for small SIR, but not for large

SIR, i.e. for high bit rates. In simulations we use a target bit rate of 320 kbps,which is not very high. Hence this is a reasonable simplification. Neglecting theterm (1− ω), yields

βv =1

1 + γdαd

γvαv

, βd =1

1 + γvαv

γdαd

. (3.11)

For small SNR the ratio of SIR targets is approximately the same as the ratio ofbit rates, i.e.

γv

γd≈ Rv

Rd,

and hence (3.11) becomes

βv =1

1 + Rdαd

Rvαv

, βd =1

1 + Rvαv

Rdαd

.

Inserting this in Assumption (3.2.2) yields the following expression for the resourceefficiency.

ρ =ρv

1 + Rdαd

Rvαv

+ρd

1 + Rvαv

Rdαd

(3.12)

Consequently, these assumptions gives us an expression for the resource efficiencydependent only on the service mix and the data rates. Also note that the depen-dency is on the ratio of bit rates and the ratio of service mix elements, rather thanabsolute values. This also leads to three interesting cases:

Rd > Rv. In this case the denominator of the first term in (3.12) will be largeand hence ρv will have a small impact on ρ.

Rd < Rv. By analogy with the previous case, ρv will now be dominating and ρd

will have a small impact on ρ.

3.3 Maximum Number of Users 17

Rd = Rv = R. If the bit rates are equal the resource efficiency will get a lineardependency of ρd and ρv according to:

ρ =ρv

1 + Rαd

Rαv

+ρd

1 + Rαv

Rαd

=ρvαv + ρdαd

αv + αd.

Since αd + αv = 1 and hence αd = (1− αv) this transforms into

ρ(αv) = ρvαv + ρd(1− αv),

which represents a straight line between ρd and ρv.

The conclusion is that the more alike the bit rates are, the more the resourceefficiency looks like a straight line and the more the bit rates differ, the more theresource efficiency for the service with larger bit rate dominates. This is shown inthe following example, with the intention to use as realistic values as possible.

Example 3.1: Resource Efficiency

This example shows what the resource efficiency from (3.12) looks like, i.e.the resource efficiency when using Assumption 3.2.2. We use as realistic parame-ters as possible, i.e. the same parameters as used in simulations. The voice bitrate is chosen to Rv = 12.2 kbps and the best effort data bit rate is chosen toRd = 64 kbps in Figure 3.1 and Rd = 320 kbps in Figure 3.2. We use ρv = 0.8and ρd = 0.5 and 0.3 to illustrate the difference between E-DCH and DCH. Thesevalues are chosen to be approximately the same as the simulation results shownin Table 5.3, section 5.4. In these examples the total resource efficiency is plottedversus the fraction of voice users.

It’s quite obvious that the total resource efficiency is dominated by the resourceefficiency for best effort data when using 320 kbps bit rate. This is the case alsowhen using 64 kbps bit rate, but not as significant. This is because the ratio Rd

Rv

is five times larger for 320 kbps than for 64 kbps.

3.3 Maximum Number of UsersWe now derive an expression for the maximum number of users in the system bycombining theory from the two previous sections. Using (3.5), but with η replacedby the maximum expected noise rise from Definition 3.2.4 yields

η =1

1− (1 + ξ)utot( αv1

γv+1−ω

+ αd1

γd+1−ω

). (3.13)


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Fraction of voice users, αv

Res

ourc

e E

ffici

ency

, ρ

ρd = 0.5

ρd = 0.3

Figure 3.1. Result from Example 3.1. Resource efficiency, ρ for ρv = 0.8, ρd = 0.5, 0.3,Rv = 12.2 kbps and Rd = 64 kbps.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Res

ourc

e E

ffici

ency

, ρ

ρd = 0.5

ρd = 0.3

Figure 3.2. Result from Example 3.1. Resource efficiency, ρ for ρv = 0.8, ρd = 0.5, 0.3,Rv = 12.2 kbps and Rd = 320 kbps.

3.3 Maximum Number of Users 19

From Definition 3.2.3 we get

η = ρ · ηmax. (3.14)

Inserting this in (3.13) and solving it for the expected total number of users, utot,yields

utot =1− 1

ρ·ηmax

(1 + ξ)( αv1

γv+1−ω

+ αd1

γd+1−ω

). (3.15)

Since we are calculating with stochastic processes, or rather expectation values,this is not strictly mathematical correct. We can really not just insert the maxi-mum expected noise rise to reckon the expected number of users. However, this isan initial approximation.

We also note that when letting ρ ·ηmax →∞, this is the exact same expressionas for the pole capacity, just as expected.

Inserting our assumed expression (3.12) for resource efficiency, ρ, in (3.15)yields

utot =1− Rvαv+Rdαd

(ρvRvαv+ρdRdαd)·ηmax

(1 + ξ)( αv1

γv+1−ω

+ αd1

γd+1−ω

). (3.16)

Example 3.2: Maximum number of users and pole capacityThe look of the capacity measure in (3.16) will now be shown in an example.Again we try to use as realistic parameters as possible for E-DCH, i.e. ρv = 0.85and ρd = 0.5. The voice bit rate is set to 12.2 kbps and the data bit rate is setto 320 kbps. For this example we also set ηmax = 7 dB, ξ = 0.7, γv = −21 dBand γd = −8 dB. The own signal interference, ω = 0.68, is reckoned from thesimulator for a 3GPP Typical Urban model. The result is shown in Figure 3.3,where the total number of users is plotted against the fraction of voice users, αv.As a comparison, the pole capacity from (3.7) is also plotted.

With these assumptions we can also reckon the maximum number of users inthe single service cases. For the case with only voice users we see that uv ≈ 56.8,and with only best effort users ud,EUL ≈ 2.34. The same quantities for the polecapacity are uv,pole ≈ 74.2 and ud,pole ≈ 3.90.

In the same way we reckon the maximum number of users for DCH with thesame parameters, but ρd = 0.3. This yields ud,Rel5 ≈ 1.30. Hence, in the singleservice best effort data case we get a capacity for E-DCH approximately ud,EUL

ud,Rel5=

2.341.30 = 1.8 times the capacity for DCH. An even larger capacity gain is shown insimulations in Chapter 5.


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

10

20

30

40

50

60

70

80

Fraction of voice users

Tot

al n

umbe

r of

use

rs

Maximum Number of UsersPole Capacity

Figure 3.3. Total number of users, utot, calculated from (3.16) with parameters set toρv = 0.85, ρd = 0.5, Rv = 12.2 kbps, Rd = 320 kbps, ηmax = 7 dB, ξ = 0.7, ω = 0.68,γv = −21 dB and γd = −8 dB.

3.4 Throughput per Noise Rise

This section will estimate the throughput per noise rise, i.e. the ratio of throughputto the noise rise. This is also a way of validating the resource efficiency measure.If we can show that the E-DCH gets a higher or equivalent throughput per noiserise as the DCH, this means that the resource efficiency is a legitimate measure tocompare the system performances.

Thanks to the fast HARQ processes it is possible to get a higher bit rate for agiven SIR. This leads to a lower noise rise caused by a user with a given bit rate,or equivalent a higher throughput for a given noise rise.

The throughput is calculated as the sum of bit rates for all active users. Sincethe resource efficiency is only valid at the capacity limit, we only consider thethroughput per noise rise for a maximum number of users. Assuming that allusers of service n have the same bit rate Rn yields the throughput

∑Nn=1 λnRnun,

where λn is the activity factor for service n and un is the maximum expectednumber of service n users. Since we consider the capacity limit, the maximumnoise rise, ηmax, from Definition 3.2.1 is used as noise rise measure. This yieldsthe following expression for throughput per noise rise

ε =∑N

n=1 λnRnun

ηmax

3.5 Coverage 21

Which in the two service case transforms into

ε =utot(λvRvαv + λdRdαd)

ηmax. (3.17)

Using the expression (3.15) for the maximum expected total number of users,utot yields

ε =1− 1

ρ·ηmax

(1 + ξ)( αv1

γv+1−ω

+ αd1

γd+1−ω

)· (λvRvαv + λdRdαd)

ηmax. (3.18)

By a short glance at this expression it is quite obvious that a higher resourceefficiency, ρ, also yields a higher throughput per noise rise. Hence with theseassumptions, a better resource efficiency also yields a better throughput, and theresource efficiency is for sure a valid measurement.

3.5 CoverageOne of the goals of the Enhanced Uplink is to improve the coverage for highbit rates. The purpose of this section is to theoretically assess the maximumpossible bit rate for a given cell radius. The cell radius is given by the maximumallowed noise rise, ηmax, in each cell. Again using (3.5) but with utotαv = uv andutotαd = ud and η replaced by the maximum noise rise ηmax yields

ηmax =1

1− (1 + ξ)( uv1

γv+1−ω

+ ud1

γd+1−ω

).

At first we assume there are only voice users in the system and calculate themaximum possible SIR target for one new best effort user entering the system, i.e.we assume ud = 1 and solve the equation for γd.

(1 + ξ)(uv

1γv

+ 1− ω+

11γd

+ 1− ω) = 1− 1

ηmax⇒

11γd

+ 1− ω=

1− 1ηmax

1 + ξ− uv

1γv

+ 1− ω︸︷︷︸

K

⇒

1γd

+ 1− ω =1K⇒

γd =1

1K − 1 + ω

, K =1− 1

ηmax

1 + ξ− uv

1γv

+ 1− ω. (3.19)

We now have an expression for the maximum SIR target for a new best effort userentering the system. For this SIR, we can calculate a bit rate. Thanks to the


fast HARQ processes in the enhanced uplink, we can admit multiple transmittingattempts and hence get a capacity gain.

Example 3.3: SIR target for a given coverageThis example will calculate the SIR target reckoned from (3.19). We use the sameparameter values as for Example 3.2, i.e. ηmax = 7 dB, ξ = 0.7 and γv = −21 dB.The own signal interference is again ω = 0.68 for 3GPP Typical Urban, andω = 0.17 for the ITU standardized traffic model Pedestrian A, also reckoned fromthe simulator. We also know from Example 3.2 that the maximum number ofvoice users is uv ≈ 56.8. If uv exceeds this value we would get γd < 0, which isnot possible. Figure 3.4 shows the SIR target plotted against the number of voiceusers.

0 10 20 30 40 50 60−25

−20

−15

−10

−5

0

5

Number of voice users

SIR

targ

et fo

r a

data

use

r

Pedestrian A3GPP Typical Urban

Figure 3.4. SIR target, γd, calculated from (3.19) with parameters set to ηmax = 7 dB,ξ = 0.7, γv = −21 dB, ω = 0.68 for 3GPP Typical Urban and ω = 0.17 for PedestrianA.

To calculate the bit rates from (3.19), we need a relationship between bit rateand SIR target. Figure 3.5 shows a plot of the SIR target versus bit rate for E-DCHusing a 3GPP Typical Urban traffic model with perfect own signal interferencecancellation. This plot is performed by tuning the SIR targets in the simulator fordifferent bit rates. To derive a analytical expression for the relationship betweenbit rate and SIR, we use the Shannon capacity formula, i.e.

3.5 Coverage 23

R = C log(1 + γ), (3.20)

where C is a constant. The constant, C, is adjusted such that the simulatedand the analytical curves approaches each other. The constant is chosen to beC = 2.5 · 103. This curve is also shown in Figure 3.5.

0 500 1000 1500 2000 2500 3000 3500 4000 4500−12

−10

−8

−6

−4

−2

0

2

4

6

Bit rate [kbps]

SIR

[dB

]

SimulatedAnalytical

Figure 3.5. SIR target versus bit rate, reckoned from simulator for 3GPP Typical Urbanand analytical using Shannon capacity formula.

Example 3.4: Bit rate for a given coverageThis example estimates the maximum possible bit rate for a new best effort userentering the system, with a given coverage. Equation (3.20) is used to calculatethe bit rate for a given SIR target. The SIR target is reckoned from (3.19) with thesame parameters as for Example 3.3, using the 3GPP Typical Urban traffic model.

Figure 3.6 shows the maximum bit rate versus number of voice users. We seethat the offered bit rate seems to be linearly dependent on the number of voiceusers in the valid region.

One is often interested in the maximum bit rate a best effort user can be of-fered. For the 3GPP Typical Urban model the maximum SIR is γd ≈ −2.56 dB,which yields approximately 1.1 Mbps maximum bit rate using (3.20).

Interesting is also the absolute maximum bit rate a best effort user can be of-fered. Again this is when uv = 0. Also assuming that there is no power limitation,i.e. η → ∞, and considering only a single cell, i.e. the other to own interference


ξ = 0. These assumptions turns (3.19) into

γd =1ω

.

For the 3GPP Typical Urban model the mean value ω = 0.68 ⇒ γd = 10.68 =

1.68 dB. The bit rate for this SIR, reckoned from (3.20), is approximately 2.26 Mbps.Again this is not strictly mathematical correct since we are dealing with mean val-ues. What is known in this case is that E[γd] = E[ 1

ω ] ≥ 1E[ω] . This inequality

means that the simplification results in an underestimation of the possible bit rate.As discussed earlier the own signal interference, ω, in reality varies a lot. Herethe mean value for the 3GPP Typical Urban traffic model is considered. In realitythis of course means also that this maximum bit rate varies a lot.

0 10 20 30 40 50 600

200

400

600

800

1000

1200

Number of voice users

Max

imum

bit

rate

for

a da

ta u

ser

[kbp

s]

Figure 3.6. Maximum bit rate for one new best effort user entering the system versusnumber of voice users. Result from Example 3.4.

Chapter 4

Simulation Model

To evaluate the system performance and validate the theoretical assessments, sim-ulations are performed. This chapter describes the simulation models and assump-tions in more detail.

4.1 Propagation Model

The propagation model characterizes the channel qualities by the attenuation oftransmitted signals. The attenuation is the inverse of the path gain. The pathgain, G, is thus the ratio of the received power to the transmitted power and itconsists of four different parts,

G = GaGdGsGm < 1.

Here, Ga is the antenna gain, Gd is the distance attenuation, Gs is the shadowfading and Gm is the multipath fading. The antenna gains and distance atten-uations are given as lookup tables. The lookup table for distance attenuation iscalculated using to the Okumura-Hata model. The fading models are describedmore in the following sections.

4.1.1 Shadow Fading

A mobile moving through an environment will be shadowed by major obstaclessuch as hills or buildings, which will cause fluctuations in the received signal. Thisphenomenon is called shadow fading. Shadow fading is mostly quite slow, andhence also called slow fading.

The shadow fading in logarithmic scale is modelled in the simulator by a normaldistributed variable, with mean µ = 0 and standard deviation σ = 8. The shadowfading is also assumed to be correlated with decorrelation distance 100 meters.

25

26 Simulation Model

4.1.2 Multipath Fading

Radio waves travel different paths from transmitter to receiver, and some havebeen reflected several times during their way. Each reflection causes a phase shift,and the difference in length of path way also leads to a phase difference at thereceiver. This is known as multipath fading. It is also called fast fading, due torapid variations of the received signal power.

The multipath fading is modelled in the simulator with a standardized modelcalled 3GPP Typical Urban.

4.2 Simulation Scenarios

The simulation scenarios should be chosen such that the simulations reflect arealistic system. On the other hand, a perfect realistic model is impossible toimplement. Also, simplifications has to be done not to render too complicatedcalculations. The simulation scenarios are chosen to be a compromise betweenthese demands.

4.2.1 Traffic Model

The simulated traffic consists of both speech and best effort data. Both user typesare based on Poisson processes for arrival. The speech model includes a voiceactivity process, where the user is active 60% of the time. The speech calls aremodelled with an exponential talk time with an average of 90 seconds.

The best effort traffic consists of a combined MMS and e-mail traffic model,where MMS occurs with 60% probability and e-mail with 40% probability. Thepackets are of random size with mean 12.7 kB for MMS and 60 kB for e-mail. Thesimulator also models the TCP flow control for packet based data.

4.2.2 Cell Deployment

The simulator models a two-dimensional environment and maintains positions forbase stations and users. The simulation environment consists of seven sites, eachwith a three sector antenna. This yields 21 cells, forming a uniform hexagonalpattern as shown in Figure 4.1. To avoid border effects the plan is repeatedthrough a wrap-around technique, so that the environment forms in fact an infinitehexagonal grid. The cell radius is set to 500 meters.

4.2.3 User Placement

Users are initially placed randomly throughout the simulated area according to auniform distribution. They move with a Rayleigh distributed absolute velocity.

4.3 System Model 27

Figure 4.1. Cell deployment.

4.3 System ModelThis section briefly describes the most significant assumptions and operations usedto model the system.

4.3.1 Fast HARQ

Retransmissions are modelled with a stop-and-wait protocol. This means that noretransmission attempt is done in the same process before a negative acknowl-edgement is received. For E-DCH with 2 ms TTI, the simulator uses five parallelqueues. The number of parallel queues are set such that the time to go through allthe queues are approximately the same as the HARQ round trip time, so when thefirst queue is handled again, the Acknowledgement (ACK) or Negative Acknowl-edgement (NACK) has been received by the UE. Also, the time to go through thequeues and the round trip time being as close together as possible. The principaloperation of the HARQ processes is shown in Figure 4.2.

The outer loop power control model allows four transmission attempts, mean-ing that the probability that the transmission is still incorrect after the fourthtransmission is 1%. If the number of retransmissions exceeds five, a Radio LinkControl (RLC) retransmission is triggered, which will perform a retransmissionrequest from the RNC.

When errors occur, all blocks transmitted in the current TTI will be retrans-mitted. The successive attempts are soft combined, using a model for chase com-bining, where each retransmission is an exact copy of the original transmission.Chase combining is modelled by adding the SIR for each transmission attempt.The transport format and resource combination is not changed if retransmissionis required.

28 Simulation Model

processingprocessing



Node Bprocessing


NACK ACK NACK ACK

processing

ACK ACK

UE 1 2 3 54 1 2 3 54

Known (fixed) timing relation

2 ms TTI

1 2 3 4 5

1 2 3 4 5

Figure 4.2. HARQ processes.

4.3.2 Power Control

Power control consists of an inner and an outer loop. The inner loop comparesthe received SIR with a target SIR. The transmission power is increased by 1 dBif the received SIR is below the target, and decreased by 1 dB if the received SIRis above the target.

The outer loop controls the target SIR. The target SIR is increased by afixed step for each erroneously received block, and decreased by a smaller amountfor every correctly received block. The ratio between the increase and decreaseamounts is calculated based on the block error rate (BLER) target, such that theactual BLER, in a stationary situation, will converge to the BLER target.

4.3.3 Admission Control

Admission control makes sure that the system is not overloaded, by not admittingusers that will cause the overload. When a user requests a channel, the noise risethat will be caused by the user is estimated. The estimate is added to the currentsystem noise rise. If this noise rise estimation exceeds the maximum allowed noiserise of 7 dB, the user will be blocked and not admitted to the system. For voiceusers this means that they will not get a channel. For best effort users it meansthat they will actually be admitted to the system, but not allowed to start thetransmission. Hence a best effort user will actually experience a very low bit ratebecause of the waiting time rather than being blocked.

The blocking of voice users will only be used in the voice only simulationswith no best effort data users in the system. To model the prioritization of voiceusers, they are always admitted when letting simultaneous best effort users intothe system. If done correctly, the admission control should look only at the nonbest effort load when admitting voice users. This might seem a bit simplified,however it will be shown that the capacity is in fact limited by the best effortusers. But perhaps capacity could be increased further by rejecting voice calls.

4.4 Simulation Logging 29

4.3.4 Fast Rate ControlNode B rate control with a busy indicator is modelled using initial working as-sumptions for rate control not conformant with current agreement in 3GPP in alldetails. The node B controls the maximum bit rate, Rmax, i.e. the maximumnumber of blocks that a EDCH user is allowed to transmit in each TTI. The busyindicator is set when the noise rise exceeds a certain value and unset when it fallsbelow the same value.

If the busy flag is set, a new user with data in the transmit buffer sends arate request to the Node B. If the busy flag is set the user will not be admitted,but users that already have a radio link will continue the transmission with bitrate ≤ Rmax. However, if the busy flag is set when the rate request is receivedby the Node B, the maximum bit rate will be decreased and hence the users thatare already transmitting are forced to lower their bit rates. Now the new usercan be admitted and allowed to transmit with bit rate lower than the new Rmax.Example 4.1 and Figure 4.3 shows how the rate control with busy indicator works.

Example 4.1: Rate control with busy indicatorThis example describes how the rate control with busy indicator works. Thedescription refers to Figure 4.3.

1. UE2 starts transmitting and the busy indicator is set.

2. UE2 stops transmitting and the busy indicator is unset. UE1 starts trans-mitting and the busy indicator is set again.

3. UE2 wants to start transmitting but can not be admitted immediately sincethe busy indicator is set. UE2 sends a rate request.

4. Rmax is lowered and hence also UE1 bit rate is lowered. The busy indicatoris temporarily unset and UE2 is admitted and starts transmitting.

5. UE1 stops transmitting and the busy indicator is again unset. Rmax isincreased and hence also UE2 bit rate is increased.

4.4 Simulation LoggingThe simulated time is set to 200 seconds, and the logging starts after 20 secondswhen the traffic is assumed stable. Information on the total system is being loggedevery 2 ms. However, information about each user, such as number of transmittedblocks, is logged only for the total simulated time and not each time instant, dueto memory limitations. Each scenario is simulated three times with different seedsto get more accurate values.

30 Simulation Model

Rmax

Busy Indicator

UE1

REQ

UE2

SETUNSET

1 2 3 4 5

Figure 4.3. Example of fast rate control with busy indicator.

Chapter 5

Simulations and Results

The following chapter presents the simulations and results. The first section definessome performance measures, the second section describes the evaluation methodsand the last sections presents the simulation results.

5.1 Performance Measures

Since the purpose of the master thesis is to demonstrate the trade-off between realtime and best effort performances, a great issue is to decide what to evaluate andhow to evaluate the performances. This section presents the measures that arecalculated and studied from simulations. The next section describes how to usethese measures. Some of these measures are already known from the theoreticalassessments.

Throughput

Throughput is the data rate in the whole cell. It is calculated by dividing the totalnumber of transmitted bits by the elapsed time and number of cells. As receivedbits we only consider blocks that have been delivered.

throughput =transmitted bits

elapsed time · number of cells

Normalized User Bit Rate

Normalized user bit rate is the data rate experienced by the user. It is calculatedby dividing the size (bits) of the message the user has transmitted by the timeelapsed from the message was generated until it was completely received.

normalized user bit rate =message size

transmission time

31

32 Simulations and Results

Normalized User DelayNormalized user delay is the time it takes for the user to get a certain number ofbits. It is calculated by dividing the time elapsed from the message was generateduntil it was completely received by the size (bits) of the message the user hastransmitted. Normalized user delay is the inverse of normalized user bit rate.When calculating normalized user bit rate and normalized user delay, only userswho have started and finished the transmission within the simulated time areconsidered.

normalized user delay =transmission time

message size=

1normalized user bit rate

Block Error RateBlock error rate (BLER) describes the fraction of data block errors transmittedby the user. BLER is calculated by dividing the number of blocks that are nottransmitted correctly by the total number of transmitted blocks. Block error ratesfor voice users are calculated from users who have been given a channel and startedthe transmission within the simulated time.

BLER =Number of failed blocks

Total number of blocks

Resource EfficiencyResource efficiency describes the utilization of the available resource. This is thesame quantity as treated theoretically in Section 3.2. It is measured as the ratioof the noise rise mean value to the maximum noise rise limit at the capacity limit.

ρ =η

ηmax

Throughput per Noise RiseThroughput per noise rise is the ratio of throughput to the noise rise mean value.This, again is the same quantity as treated theoretically in Section 3.4 earlier. Thisis a way of measuring the efficiency of the caused noise rise as well as a validationof the resource efficiency measure.

throughput per noise rise =throughput

η

5.2 Evaluation CriteriaTo get the interesting information out of the simulations and the above measures,one needs some criteria to evaluate the system performance. The criteria that areapplied in the sequel to evaluate the simulation results are presented below.

5.3 Voice Only 33

For the capacity measures we need some helpful definitions. To start with, wedefine an unsatisfied user.

Definition 5.2.1 (Unsatisfied voice user) A voice user is unsatisfied iff

- The user is blocked

or

- BLER ≥ 1%

¤

As mentioned in the previous chapter, the first criterium that the user is blockedwill only be used in the simulations with only voice users in the system. For themixed service simulations, we only consider BLER. Of course we must keep aneye on the noise rise also in these simulations to make sure it does not exceed thenoise rise for the only voice case, when users are being blocked.

Definition 5.2.2 (Unsatisfied best effort user) A best effort user is unsatis-fied iff

Normalized user bit rate ≤ 10 kbps ⇔ Normalized user delay ≥ 0.1 s/kbits

¤

Capacity is measured in number of users for voice and throughput for best effortdata. Using the above definitions, we define the capacity measure applied toevaluate the simulation results.

Definition 5.2.3 (Capacity) Capacity is defined as the maximum number ofusers or the maximum throughput such that the fraction of unsatisfied voice users< 5% and the fraction of unsatisfied best effort users < 5%.

¤

5.3 Voice OnlyTable 5.1 shows the most significant parameters for this simulation, that have notalready been discussed in Chapter 4.

Power Control (PC) Outer loop PC BLER target 0.007Admission control Maximum noise rise 7 dB

Noise rise for busy flag 6 dB

Table 5.1. Simulation parameters for voice simulation.

The sequel of this section evaluates the results from the voice only simulations.


Capacity

Figure 5.1 shows the fraction of unsatisfied users versus the number of voice usersper cell. The scale is normalized such that the voice capacity = 1. This normalizedscale will be used throughout the whole chapter. With the capacity measuredefined earlier in this chapter, i.e. at most 5% unsatisfied users, the maximumnumber of voice users per cell can be derived.

0.85 0.9 0.95 1 1.05 1.10

5

10

15

Voice traffic load

Fra

ctio

n of

uns

atis

fied

user

s [%

]

Figure 5.1. Fraction of unsatisfied users versus voice load for voice only simulations.The scale is normalized such that the voice capacity = 1

The voice capacity is mainly limited by blocking. The optimal capacity isreceived if users experience bad values for blocking and BLER at the same noiserise level. However these simulations are run with a quite small cell radius, and topreserve coverage one must block users. For a large cell radius the capacity wouldinstead be limited by the users experiencing bad BLER rather than blocking. Sothe system performance is really a trade-off between coverage and capacity.

Resource Efficiency

Figure 5.2 shows the mean noise rise for the simulations with only voice users.The mean noise rise, η ≈ 6.04 dB at the capacity limit. This yields the resourceefficiency ρ = η

ηmax≈ 6.04 dB

7 dB ≈ 4.025.01 ≈ 0.80. Since this is the single service case,

the single service resource efficiency for voice ρv is equal to the total resourceefficiency ρ, i.e. ρv = ρ ≈ 0.80.

5.4 Simultaneous Voice and Best Effort Data 35

4.5 5 5.5 6 6.50

5

10

15

Fra

ctio

n of

uns

atis

fied

user

s [%

]

Noise rise [dB]

Figure 5.2. Mean noise rise for voice only simulations.

5.4 Simultaneous Voice and Best Effort Data

The main part of the work has been focused on the simultaneous voice and besteffort data simulations. This section presents these simulations and the mostimportant results and conclusions.

The simulation parameters for the simultaneous voice and best effort datasimulations will be the same for voice as for the previous simulation together withthe parameters for best effort data shown in Table 5.2.

To start with we consider the best effort data only case. Figure 5.3 shows the95th percentile normalized user delay versus best effort load for both E-DCH andDCH. With the evaluation criteria defined earlier, the capacity is derived for bothE-DCH and DCH. The scale is normalized such that the DCH system throughputat the single service capacity limit = 1. The normalized scale will be used in thesequel of this chapter. The capacity for the E-DCH is, in the single service case,approximately 2.66 times the capacity for DCH.

Table 5.3 shows the mean noise rise and resource efficiencies at the single servicecapacity limits. We see that best effort data users carried on DCH experience badquality, i.e. low bit rate, for a lower noise rise than the E-DCH. Voice users manageeven more noise rise before experiencing bad quality. The same behaviour is seenin the two service case, with coexistent voice and best effort data users. This leadsto that the capacity is in fact limited by the best effort data users.

This behaviour depends mainly on the admission control. Since voice usersare prioritized, the admission will let as many users as possible into the system,while maintaining acceptable quality. Best effort load is more bursty and the


Power Control (PC) Outer loop PC BLER target 0.01Admission control Maximum noise rise 7 dB

Noise rise for busy flag 6 dBE-DCH TTI [ms] 2

Slots per TTI 3HARQ with soft combining YesTx attempts 4Target rate [kbps] 320

DCH TTI [ms] 10Slots per TTI 15HARQ with soft combining NoTx attempts 1Target rate [kbps] 320

Table 5.2. Simulation parameters for best effort data simulations.

Service Mean noise rise Resource efficiencyVoice 6.04 dB 0.80E-DCH 3.72 dB 0.47DCH 1.55 dB 0.29

Table 5.3. Mean noise rise and resource efficiency at the single service capacity limits.


0 0.5 1 1.5 2 2.5 30

0.05

0.1

0.15

0.2

0.25

Best−effort throughput

95th

per

cent

ile n

orm

alis

ed d

elay

[s/k

bits

]

E−DCHDCH

Figure 5.3. Results from best effort data only simulation for both E-DCH and DCH.The scale is normalized such that the DCH capacity = 1.

admission control must keep a back off margin to the maximum noise rise in orderto not exceed the threshold. Best effort users that are not admitted, have to waitand hence experience low bit rate. The E-DCH adapts faster to the interferenceconditions than DCH channels thanks to the shorter TTI. Node B rate controlalso allows the admission control to be more relaxed. A high bit rate user can betreated in the admission control as a user with lower bit rate, since the Node B ratecontrol is able to lower the bit rate very fast. This in all means that voice usersendure higher noise rise before experiencing bad quality than best effort users, andbest effort users carried on the E-DCH endure more noise rise than those carriedon DCH channels.

Now we consider coexistence of voice and best effort data. Defining the capacityas before, i.e. at most 5% of the best effort users should have a normalized packetdelay greater than 0.1 s/kbit and at most 5% of the voice users should have BLERgreater than 1%. An example of the capacity limitation in the two service caseis shown in Figure 5.4, where the fraction of unsatisfied users for both voice andE-DCH is plotted versus best effort throughput for an appropriate voice load. Itis quite obvious that the capacity is limited by the best effort users. The principallook of Figure 5.4 is seen also for DCH and for all voice loads.

Deriving the capacities for all simulated voice loads in the same way yields thecapacity regions for the E-DCH and DCH, shown in Figure 5.5.


0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.80

5

10

15

20

25

Best effort throughput

Fra

ctio

n of

uns

atis

fied

user

s [%

]

VoiceE−DCH

Figure 5.4. Fraction of unsatisfied users versus best effort throughput in the mixedservice case.

5.4.1 At the Capacity Limit

This section discusses and evaluates the most interesting simulation results, i.e.the performance at the capacity limit.

Figure 5.5 shows the maximum best effort data throughput versus voice loadfor E-DCH and DCH. The areas below the curves define the capacity regions for E-DCH and DCH. We see for example that with the capacity criteria defined earlier,the best effort data carried on DCH will get no throughput at all approximatelyabove 0.50 of the voice capacity. The same limit for E-DCH is around 0.85 ofthe voice capacity. This difference is dependent on the resistance to noise rise,discussed earlier. We simply need more of the available resource, or noise rise, toadmit one best effort user carried on DCH than on the E-DCH. Users that are notadmitted, experience large packet delay. If no restrictions are set to the packetdelay, both capacity curves would approach the voice load = 1, but the best effortusers would also experience an extremely large packet delay. In fact, we could geta slightly higher throughput, but with the drawback of a non acceptable packetdelay. The capacity regions are thus of course dependent on what packet delay, orpacket bit rate, is considered ’acceptable’.

Figure 5.6 shows the gain in capacity with enhanced uplink, i.e. the gain intotal system throughput. The E-DCH yields a capacity of approximately 2.6 timesthe DCH capacity, for all voice loads for which the DCH system gets any best effortdata throughput at all.

Figure 5.7 shows the cumulative distribution functions (CDF) of noise rise.The noise rise CDF for voice is plotted at the capacity limit. The noise rise CDFsfor best effort data are plotted at the simulated load just above the capacity limit,


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

0.5

1

1.5

2

2.5

3

Offered voice load

Max

imum

bes

t effo

rt th

roug

hput

E−DCHDCH

Figure 5.5. Maximum best effort throughput versus voice load for both E-DCH andDCH. The areas below the curves define the capacity regions for E-DCH and DCHrespectively.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.450

0.5

1

1.5

2

2.5

3

Offered voice load

Rat

io o

f E−

DC

H c

apac

ity to

DC

H c

apac

ity

Figure 5.6. Capacity gain with E-DCH compared to DCH.


so that the noise rise at the capacity limit is actually lower. The figure shows thatvoice users are the most noise rise resistant, best effort data carried on DCH aremost noise rise sensitive and E-DCH is in between. Hence it also shows that thelimitation of not blocking voice users when having coexistent best effort users doesnot affect the result, since the best effort users are limiting the capacity.

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]

CDF for noise rise

VoiceEnhanced UplinkRelease 5

Figure 5.7. Noise rise CDF at the capacity limits for voice, E-DCH and DCH.

In Figure 5.8 the simulated resource efficiency is plotted, together with thetheoretical expression (3.12), i.e. the same curves as shown in Figure 3.2. We seethat the curves not quite correspond to each other. The theoretical curve is almostconstant for small fractions of voice, but the simulated one rises a bit. Probablya small amount of voice users is enough to rise the noise rise level.

The theoretical curve rises very quickly for large αv and approaches the singleservice resource efficiency for voice. The simulated curve does not reach thesefractions of voice users, mainly because of the packet delay criterium discussedearlier. If we let go of this criterium, perhaps the simulated curve would also risebut the simulations can not show this, because of the gap between single servicevoice and the mixed system.

The theoretical expression (3.12) for resource efficiency is probably too simpli-fied. For example, no dependence of the quantification of the service mix is takeninto account. In reality the fraction of users is only assigned certain values. Thecapacity limitation by best effort users is also probably a big issue. The noiserise at the capacity limit is thus of course also strongly limited by the best effortusers. This might affect the resource efficiency more than the bit rates, assumedin (3.12).

The intention was however to show that the enhanced uplink shows a better


resource efficiency than DCH. Figure 5.8 shows that the resource efficiency for theE-DCH is considerably higher than for DCH for all voice loads.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Res

ourc

e E

ffici

ency

, ρ

E−DCH − SimulatedDCH − SimulatedE−DCH − TheoreticalDCH − Theoretical

Figure 5.8. Resource efficiency for E-DCH and DCH at the capacity limit versus fractionof voice users, αv. The theoretical curves are the same as shown in Figure 3.2

Figure 5.9 shows the theoretical mean noise rise and the simulated mean, max-imum and minimum noise rise at the capacity limits versus service mix, or ratherfraction of voice users, αv. The theoretical mean noise rise is calculated fromη = ρ · ηmax, with the resource efficiency, ρ, for E-DCH from Figure 5.8. The fig-ure shows that the expression (3.12) gives a quite good approximation of the noiserise level despite the simplifications. The theoretical curve is definitely within thedeviation margins.

We note that the mean noise rise level increases with the increasing fractionof voice users, just as expected. However the maximum noise rise decreases withincreasing αv. This can be explained by the bursty behaviour of best effort users.In fact we see that both the minimum and the maximum noise rise approachesthe mean noise rise for increasing fractions of voice users, αv. It means that theuncertainty of the mean noise rise, and hence the resource efficiency, is larger forlarger fractions of best effort users, i.e. small fractions of voice users.

Figure 5.10 shows the ratio of the total system throughput to the mean noiserise at the capacity limit. The scale is normalized such that the throughput pernoise rise for DCH in the single service case = 1. Again the E-DCH shows betterperformance than DCH. The throughput per noise rise is larger for the E-DCHthan for DCH for all voice loads. Hence the caused noise rise is more efficientlyutilized with E-DCH than with DCH.

The better resource efficiency together with the larger throughput per noiserise, proves the better resource utilization by the enhanced uplink compared to


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

2

4

6

8

10

12


Noi

se r

ise

[dB

]

E−DCH meanE−DCH maxE−DCH minE−DCH mean − theoretical

Figure 5.9. Noise rise at the capacity limits for the enhanced uplink versus fraction ofvoice users, αv.

previous WCDMA releases. Users carried on the enhanced uplink are able to usea larger amount of the noise rise, as well as to use it more efficiently.

Figure 5.11 shows the 95th percentile normalized packet bit rate at the lowestbest effort load, for all simulated voice loads. Note that this is not at the capacitylimits, but at the lowest simulated best effort loads for each voice load. The bitrate scale is normalized such that the 95th percentile bit rate for DCH in the singleservice case = 1. We see that the gain with E-DCH is approximately 1.15 timesthe bit rate for a user carried on DCH for all voice loads.

The main focus of the work has been on the simulations. The results showedsignificant improvements from many aspects when introducing the enhanced uplinkin a WCDMA network. Simulations showed a better noise rise resistance and amore efficient utilization of the caused noise rise. These two factors together yieldsan appreciably higher system throughput, and hence a larger capacity region.

Simulations showed also that E-DCH users experience bad quality for a lowernoise rise level than voice users. Hence, with these quality requirements, voiceusers are not really affected by introducing the enhanced uplink.


0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Offered voice load

Thr

ough

put p

er n

oise

ris

e

E−DCHDCH

Figure 5.10. Throughput per noise rise [ kbps/celldB

] for E-DCH and DCH at the capacitylimit.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.8

0.85

0.9

0.95

1

1.05

1.1

1.15

Offered voice load

95 p

erce

ntile

bit

rate

at t

he lo

wes

t loa

d

E−DCHDCH

Figure 5.11. 95th percentile bit rate at the lowest loads for E-DCH and DCH.


Chapter 6

Conclusions

The master thesis assignment was to study the consequences of introducing theenhanced uplink in a WCDMA network, in particular on the quality, coverage andcapacity of real time services carried on previous WCDMA releases. The aim wasto demonstrate the trade-off between real time and best effort performances. Themain focus of the work has thus been on coexistent voice and best effort data.Both theoretical assessments and simulations has shown that the enhanced uplinkhas many advantages over previous WCDMA releases.

To start with, the simulations showed that the capacity is in fact limited bythe best effort users experiencing bad quality, i.e. large packet delay. Howeverbest effort users carried on the enhanced uplink manage a lot more interferencethan those carried on previous WCDMA channels before experiencing bad quality.This lead to that the capacity region where both voice and best effort users aresatisfied is appreciably larger for the enhanced uplink. Nevertheless the fact thatthe capacity is limited by the best effort users means that there is a large gapbetween the single service voice capacity and the voice load level where we areable to admit a best effort user with decent packet bit rate. Since voice users areprioritized and the noise rise really does not reach a high level in the mixed servicecase, this means that best effort users does not really affect the quality for voiceusers.

The improved resistance to interference on the enhanced uplink, compared toprevious WCDMA channels, shows a larger utilization of the available resource,or the noise rise. Together with the result that the enhanced uplink also yields alarger throughput per noise rise than previous WCDMA releases, this proves thatthe resource is definitely more efficiently utilized. Users carried on the enhanceduplink are thus able to use a larger amount of the noise rise, as well as to use itmore efficiently.

This is also reflected in the total system throughput. The enhanced uplinkyields a capacity approximately 2.6 times the capacity on previous WCDMA chan-nels for all voice loads.

45

46 Conclusions

Bibliography

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[2] Feasibility study for enhanced uplink for UTRA FDD. Technical Report25.896, 3GPP, 2004.

[3] Lars Ahlin and Jens Zander. Principles of Wireless Communications. Stu-dentlitteratur, 2nd edition, 2000. ISBN 91-44-00762-0.

[4] Pete Boyer, Milica Stojanovic, and John Proakis. A simple generalization ofthe CDMA reverse link pole capacity formula. IEEE Transactions on Com-munications, 49(10):1719–1722, October 2001.

[5] Konstantinos Dimou, Claudio Rosa, Troels B. Srensen, Jeroen Wigard, andPreben E. Mogensen. Performance of uplink packet services in WCDMA. InProceedings of the 57th Semiannual IEEE Vehicular Technology Conference,volume 3, pages 2071–2075, April 2003.

[6] Basic concepts of WCDMA radio access network. Ericsson, White Paper,2001.www.ericsson.com/products/white−papers−pdf/e207−whitepaper−ny−k1.pdf(Acc. 2004-09-02).

[7] WCDMA evolved, the first step - HSDPA. Ericsson, White Paper, May 2004.www.ericsson.com/products/white−papers−pdf/wcdma−evolved.pdf(Acc. 2004-09-06).

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[9] Ning Guo and Salvatore D. Morgera. The grade of service for integratedvoice/data wireless DS-CDMA networks. In Proceedings of IEEE Interna-tional Conference on Communications, volume 2, pages 1104–1110, May 1994.

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

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[11] Narayan B. Mandayam, Jack M. Holtzman, and Sergio Barberis. Erlangcapacity for an integrated voice/data DS-CDMA wireless system with variablebit rate sources. In Proceedings of IEEE Personal, Indoor Mobile RadioConference, volume 3, pages 1078–1082, September 1995.

[12] Ray Owen, Phil Jones, Shirin Dehgan, and Dave Lister. UMTS capacity andplanning issues. In Proceedings of the First International Conference on 3GMobile Communication Technologies, pages 218–223, March 2000.

[13] Ray Owen, Phil Jones, Shirin Dehgan, 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, pages 298–302, May 2000.

[14] Dorin Saban, Hans van den Berg, Richar J. Boucherie, and Irwan Endrayanto.CDMA coverage under mobile heterogeneous network load. In Proceedings ofthe 56th IEEE Vehicular Technology Conference, volume 1, pages 326–330,September 2002.

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[16] Ashwin Sampath, Narayan B. Mandayam, and Jack M. Holtzman. Erlangcapacity of a power controlled integrated voice and data CDMA system. InProceedings of IEEE Vehicular Technology Conference, volume 3, pages 1557–1561, May 1997.

[17] Bernd Schröder, Bernhard Liesenfeld, Albert Keller, Kenji Leibnitz, DirkStaehle, and Phuoc Tran-Gia. An analytical approach for determining cover-age probabilities in large UMTS networks. In Proceedings of the 54th IEEEVehicular Technology Conference, volume 3, pages 1750–1754, October 2001.

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