Frequency Domain Link Adaptation for OFDM-based Cellular …21764/FULLTEXT01.pdf · 2006. 4....

Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

Frequency Domain Link Adaptation for

OFDM-based Cellular Packet Data

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Anders Ruberg

LiTH-ISY-EX−−06/3812−−SELinköping 2006

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

Frequency Domain Link Adaptation for

OFDM-based Cellular Packet Data

Examensarbete utfört i Kommunikationssystem

vid Tekniska högskolan i Linköpingav

Anders Ruberg

LiTH-ISY-EX−−06/3812−−SE

Handledare: Niclas Wiberg,Ericsson Research, Linköping

Examinator: Robert Forchheimer,ISY, Linköpings universitet

Linköping, 3 , 2006

Avdelning, Institution

Division, Department

Division of Control and CommunicationDepartment of Electrical EngineeringLinköpings universitetS-581 83 Linköping, Sweden

Datum

Date

2006-17-03

Språk

Language

� Svenska/Swedish

� Engelska/English

�

⊠

Rapporttyp

Report category

� Licentiatavhandling

� Examensarbete

� C-uppsats

� D-uppsats

� Övrig rapport

�

⊠

URL för elektronisk version

http://www.ep.liu.se/exjobb/isy/2006/3812

ISBN

—

ISRN

LiTH-ISY-EX−−06/3812−−SE

Serietitel och serienummer

Title of series, numberingISSN

—

Titel

TitleLänkadaptation i frekvensdomänen för paketdata i OFDM-baserade cellulära nät

Frequency Domain Link Adaptation for OFDM-based Cellular Packet Data

Författare

AuthorAnders Ruberg

Sammanfattning

Abstract

In order to be competitive with emerging mobile systems and to satisfy the evergrowing request for higher data rates, the 3G consortium, 3rd Generation Partner-ship Project (3GPP), is currently developing concepts for a long term evolution(LTE) of the 3G standard. The LTE-concept at Ericsson is based on Orthogo-nal Frequency Division Multiplexing (OFDM) as downlink air interface. OFDMenables the use of frequency domain link adaptation to select the most appropri-ate transmission parameters according to current channel conditions, in order tomaximize the throughput and maintain the delay at a desired level.

The purpose of this thesis work is to study, implement and evaluate differentlink adaptation algorithms. The main focus is on modulation adaptation, wherethe differences in performance between time domain and frequency domain adap-tation are investigated. The simulations made in this thesis are made with asimulator developed at Ericsson.

Simulations show in general that the cell throughput is enhanced by an averageof 3% when using frequency domain modulation adaptation. When using theimplemented frequency domain power allocation algorithm, a gain of 23-36% inaverage is seen in the users 5th percentile throughput. It should be noted thatthe simulations use a realistic web traffic model, which makes the channel qualityestimation (CQE) difficult. The CQE has great impact on the performance offrequency domain adaptation. Throughput improvements are expected when usingan improved CQE or interference avoidance schemes.

The gains with frequency domain adaptation shown in this thesis work may betoo small to motivate the extra signalling overhead required. The complexity ofthe implemented frequency domain power allocation algorithm is also very highcompared to the performance enhancement seen.

Nyckelord

Keywords OFDM, 3G LTE, link adaptation, power allocation

Abstract

In order to be competitive with emerging mobile systems and to satisfy the evergrowing request for higher data rates, the 3G consortium, 3rd Generation Partner-ship Project (3GPP), is currently developing concepts for a long term evolution(LTE) of the 3G standard. The LTE-concept at Ericsson is based on Orthogo-nal Frequency Division Multiplexing (OFDM) as downlink air interface. OFDMenables the use of frequency domain link adaptation to select the most appropri-ate transmission parameters according to current channel conditions, in order tomaximize the throughput and maintain the delay at a desired level.

The purpose of this thesis work is to study, implement and evaluate differentlink adaptation algorithms. The main focus is on modulation adaptation, wherethe differences in performance between time domain and frequency domain adap-tation are investigated. The simulations made in this thesis are made with asimulator developed at Ericsson.

Simulations show in general that the cell throughput is enhanced by an averageof 3% when using frequency domain modulation adaptation. When using theimplemented frequency domain power allocation algorithm, a gain of 23-36% inaverage is seen in the users 5th percentile throughput. It should be noted thatthe simulations use a realistic web traffic model, which makes the channel qualityestimation (CQE) difficult. The CQE has great impact on the performance offrequency domain adaptation. Throughput improvements are expected when usingan improved CQE or interference avoidance schemes.

The gains with frequency domain adaptation shown in this thesis work maybe too small to motivate the extra signalling overhead required. The complexityof the implemented frequency domain power allocation algorithm is also very highcompared to the performance enhancement seen.

v

Acknowledgements

I am very grateful that I was given the opportunity to do my master thesis work atEricsson Research in Linköping. I had a great time and I have met many inspiring,enthusiastic and competent people. The atmosphere at LinLab is incredible andall of the people working there are very friendly and helpful.

I especially want to thank my supervisor at Ericsson, Niclas Wiberg, whoalways took the time to discuss with me and guided me through the work. I amalso very thankful to Niclas for letting me participate in the simulator developmentat Ericsson, giving me very good experiences for the future. Special thanks to EvaEnglund for sharing ideas and for valuable lunch discussions. Thanks also to myexaminer Robert Forchheimer.

Finally, I would like to thank Jean-Christophe Laneri, a parallel master thesisstudent at Ericsson Research in Kista for the cooperation and discussions we had.Thanks also to Jessica Heyman for proof-reading the report and to my opponentErik Narby for interesting and helpful discussions.

Last but not least, I would like to thank my family for always supporting me,and give special thanks to Isabelle.

vii

Abbreviations

3G 3rd Generation mobile communication system3GPP 3rd Generation Partnership ProjectACK AcknowledgementADSL Asynchronous Digital Subscriber LineARQ Automatic Repeat RequestBLEP Block Error ProbabilityBLER Block Error RateCQ Channel QualityCQE Channel Quality EstimationCQI Channel Quality InformationDFT Discrete Fourier TransformEDGE Enhanced Data rates for Global EvolutionFFT Fast Fourier TransformGIR Gain to Interference RatioGPRS General Packet Radio ServiceGSM Global System for Mobile communicationsHARQ Hybrid Automatic Repeat RequestHSDPA High-Speed Downlink Packet AccessIR Incremental RedundancyISI Intersymbol InterferenceLTE Long Term EvolutionMCM Multi Carrier ModulationMCS Modulation and coding schemeNACK Negative AcknowledgeOFDM Orthogonal Frequency Division MultiplexingQAM Quadrature Amplitude ModulationQPSK Quadrature Phase Shift KeyingRBI Received Bit InformationRBIR Received Bit Information RateRNC Radio Network ControllerRR Round RobinRU Resource UnitSI Symbol Level Mutual InformationSIR Signal to Interference RatioSNR Signal to Noise RatioTCP/IP Transport Control Protocol/Internet protocolTD Time DomainTF Transport FormatTFD Time and Frequency DomainTR Transport ResourceTTI Transmission Time IntervalUMTS Universal Mobile Telecommunications ServicesUTRA Universal Terrestrial Radio AccessWCDMA Wideband Code Division Multiple AccessWLAN Wireless Local Area Network

ix

Contents

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Research Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theoretical Background 5

2.1 Orthogonal Frequency Division Multiplexing . . . . . . . . . . . . 52.1.1 OFDM History . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.2 System Description . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Radio Interface Protocols . . . . . . . . . . . . . . . . . . . . . . . 62.2.1 UTRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Mutual Information Quality Model . . . . . . . . . . . . . . . . . . 92.3.1 Symbol-Level Mutual Information . . . . . . . . . . . . . . 92.3.2 Block-Level Mutual Information . . . . . . . . . . . . . . . 12

3 3G Long Term Evolution 15

3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 Technical Description . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2.1 Resource Unit . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2.2 Other Parameters . . . . . . . . . . . . . . . . . . . . . . . 17

4 Link Adaptation 19

4.1 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.1.1 Transmission Parameters . . . . . . . . . . . . . . . . . . . 204.1.2 Optimization Criteria . . . . . . . . . . . . . . . . . . . . . 20

4.2 Optimal Link Adaptation . . . . . . . . . . . . . . . . . . . . . . . 214.2.1 Power Allocation using Water-Filling . . . . . . . . . . . . . 214.2.2 Near-Optimal Bit-Loading . . . . . . . . . . . . . . . . . . . 22

4.3 Simplified Link Adaptation . . . . . . . . . . . . . . . . . . . . . . 244.3.1 Constant Power Allocation . . . . . . . . . . . . . . . . . . 244.3.2 Simplified Bit-Loading . . . . . . . . . . . . . . . . . . . . . 244.3.3 Code Rate Adaptation . . . . . . . . . . . . . . . . . . . . . 26

xi

xii Contents

5 Implementation 31

5.1 Simulator System Description . . . . . . . . . . . . . . . . . . . . . 315.1.1 Basic Parameters . . . . . . . . . . . . . . . . . . . . . . . . 315.1.2 Channel Quality Information . . . . . . . . . . . . . . . . . 325.1.3 Transmission Resources . . . . . . . . . . . . . . . . . . . . 325.1.4 Transport Format . . . . . . . . . . . . . . . . . . . . . . . 32

5.2 Link Adaptation Implementation . . . . . . . . . . . . . . . . . . . 335.2.1 Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . 335.2.2 Channel Quality Estimation . . . . . . . . . . . . . . . . . . 355.2.3 Modulation Adaptation . . . . . . . . . . . . . . . . . . . . 375.2.4 Code Rate Adaptation . . . . . . . . . . . . . . . . . . . . . 415.2.5 SIR Back-off . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6 Simulation Model 45

6.1 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.1.1 Multi-Path Fading . . . . . . . . . . . . . . . . . . . . . . . 456.1.2 Interference Model . . . . . . . . . . . . . . . . . . . . . . . 45

6.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466.2.1 General System Parameters . . . . . . . . . . . . . . . . . . 466.2.2 Fast HARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . 466.2.3 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.3 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.3.1 Web Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.3.2 Full Buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.4 Cell Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.5 User Creation and Placement . . . . . . . . . . . . . . . . . . . . . 476.6 Simulation Logging . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.7 Simulation Seed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7 Simulation Results 49

7.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . 497.2 Block Error Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507.3 GIR Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.3.1 Different Cell Radii . . . . . . . . . . . . . . . . . . . . . . 527.3.2 Different Network Sizes . . . . . . . . . . . . . . . . . . . . 53

7.4 Modulation Adaptation . . . . . . . . . . . . . . . . . . . . . . . . 547.4.1 Load Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 567.4.2 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577.4.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.5 Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617.5.1 Single Link . . . . . . . . . . . . . . . . . . . . . . . . . . . 617.5.2 System Level . . . . . . . . . . . . . . . . . . . . . . . . . . 63

8 Conclusions 69

8.1 Modulation Adaptation . . . . . . . . . . . . . . . . . . . . . . . . 698.2 Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698.3 For Further Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Bibliography 73

A Orthogonal Frequency Division Multiplexing 75

A.1 Orthogonality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75A.2 Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76A.3 Realization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

xiv Contents

Chapter 1

Introduction

1.1 Background

The trend in cellular mobile systems is to achieve high-speed connections withsmall delays to allow broadband applications. GSM can offer around 9.6 kbit/swhich is not very much these days. With a GSM net that supports GPRS, userscan reach up to 160 kbit/s and with EDGE-technology (which is an evolved versionof GPRS), speeds up to 236 kbit/s can be achieved in the downlink. The problemis that to achieve these data rates, the conditions in the net have to be verygood. Even when the conditions are good, these previously mentioned rates arenot enough if the mobile users want to use streaming media with high quality ordownload large files.

3G is a big step towards offering real broadband connections, with supportof 2 Mbit/s in the downlink. With a new extension technology called high-speeddownlink packet access (HSDPA) peak rates up to 14.4 Mbit/s in the downlinkare possible.

Even though 3G with HSDPA can offer broadband connections, the devel-opment towards even higher data rates is in full progress. The 3G PartnershipProject (3GPP), a consortium of more than 200 wireless vendors and operators,has begun studies and the development of a long term evolution (LTE) of 3G thatsupports downlink speeds of up to 100 Mbit/s. Other main targets for the nextstandard are; support for 50 Mbit/s in the uplink, reduced delays, better coverageand improved spectrum efficiency.

The Ericsson concept for the 3G LTE is based on Orthogonal Frequency Divi-sion Multiplexing (OFDM) as air-interface, at least on the downlink. OFDM hasbecome a promising technique for future broadband cellular packet data systemsand is e.g. used in wireless LAN, as well as many fixed-wired systems (e.g. ADSL).

One of the interesting opportunities with OFDM is the possibility to use fre-quency domain adaptation to benefit from the time dispersion of the radio channel.With OFDM, resource allocations can be done individually for each sub-band, bychoosing a power level and a modulation and coding scheme (MCS). The use of fre-quency domain adaptation gives the possibility to exploit the channel to a greater

1

2 Introduction

extent than what is possible in earlier mobile systems.

1.2 Problem Statement

This thesis assignment is to study different downlink frequency domain link adap-tation algorithms, implement some of them in a cellular radio network simulatorand evaluate their performance. The evaluation will concentrate on throughput,error rate and robustness against imperfect channel estimation.

1.3 Previous Work

Link adaptation in the frequency domain is a rather new subject in the area ofcellular radio. However, it has been widely studied in fixed-wired systems withfocus on optimal resource allocations. Such optimal allocations to approach thetheoretical channel capacity is a well known subject in information theory.

The previous work can therefore be divided into two categories; link adap-tation with respect to mobile radio systems with time-varying channels and linkadaptation in fixed-wired systems with very slow varying channels.

Previous work regarding link adaptation for cellular systems at Ericsson havebeen important for this thesis work. Besides the work within Ericsson there arequite a few reports on link adaptation for wideband OFDM radio systems andWLAN available, accessible through e.g. IEEE. However, these reports talk inmore general terms and do not cover link adaptation in cellular systems and theproblems that arise in such systems.

Reports with fixed-wired systems in focus describe link adaptation with an op-timal allocation approach, that because of the complexity (among other problems)is not very attractive for cellular radio systems. These reports provide howevergood knowledge about link adaptation in general and the problems of finding op-timal adaptation solutions.

1.4 Research Approach

In order to achieve the thesis goals, the following research approach was taken.

• Study of reports on frequency domain adaptation both from Ericsson andexternal sources.

• Theoretical assessments and the construction of a detailed problem state-ment.

• Implementation in the simulator.

• Simulations

• Evaluation and conclusions.

1.5 Thesis Outline 3

1.5 Thesis Outline

The thesis is outlined as follows; in chapter 2 a theoretical background is covered.The concept of mutual information as a quality measure is described, which willbe used throughout the thesis. OFDM is briefly explained (OFDM is described inmore detail in appendix A) and the most basic about radio interface protocols inWCDMA UMTS is also covered. Chapter 3 explains the fundamentals of the 3GLong Term Evolution concept.

Chapter 4 gives the reader an introduction to the concept of link adaptation,and then describes link adaptation for OFDM in detail. This chapter gives the nec-essary information to continue to chapter 5 where the implementation of the linkadaptation algorithms is described as well as some basic concepts of the simulatorused.

Chapter 6 describes the simulation model used for the simulations made inthis thesis work. The simulation results are then presented in chapter 7. Chapter8 concludes the results and gives some suggestions what would be interesting toinvestigate in further studies about link adaptation.

4 Introduction

Chapter 2

Theoretical Background

2.1 Orthogonal Frequency Division Multiplexing

Orthogonal Frequency Division Multiplexing (OFDM) is a Multi-Carrier Modu-lation (MCM) technique, which is a method for transmitting data by dividing itinto several streams. These sub-streams have a much smaller data rate than theoriginal sequence and therefore also have a longer symbol time. With an increasedsymbol time, a MCM system is much less sensitive to delay spread caused bymulti-path fading, compared to a single-carrier system. By adding a guard time(so called cyclic prefix), the symbol time is extended even more and IntersymbolInterference (ISI) can practically be completely eliminated.

2.1.1 OFDM History

The OFDM technique was used in analog military systems in the 1960’s. Thesesystems were very complex since they consisted of many modulators and demod-ulators, one for each sub-stream. In 1971 Weinstein and Ebert proposed the useof Discrete Fourier Transform (DFT) for modulation and demodulation. Thismade OFDM much more attractive, but only recent progress in the area of VeryLarge Scale Integrated Circuit (VLSI) has made it possible to produce circuitsthat can do fast DFT (so called Fast Fourier Transformation, FFT) operationswithin microseconds and make OFDM systems commercially possible.[7]

In the 1980’s, OFDM was studied for different areas such as high-speed modemsand digital mobile communication. In the 1990’s, several OFDM systems were in-troduced, mainly fixed-wired systems such as Asymmetric Digital Subscriber Line(ADSL)1 and High bit-rate Digital Subscriber Line (HDSL), but also widebandradio systems, including Digital Audio Broadcasting (DAB), Digital Video Broad-casting (DVB) and High Definition TeleVision (HDTV) broadcasting.

Today, many wireless systems use OFDM, which has become one of the mostpromising techniques for future high-speed mobile communication systems. OFDMis for example used in Wireless Local Area Network (WLAN) systems, such as

1in DSL systems, OFDM is called Discrete Multi-Tone (DMT)

5

6 Theoretical Background

Figure 2.1. A basic OFDM transmitter with cyclic prefix to counterattack the affect ofchannel delay spread.

WiMAX, IEEE 802.11a/g and HiperLAN2. In Europe, FLASH-OFDM (an OFDMsystem that also specifies higher protocols) is proposed as a technique to use for3G extension in sparsely populated areas on the 450 MHz frequency band.

2.1.2 System Description

Figure 2.1 shows a typical flowchart of a simple OFDM transmitter. The OFDMsystem divides the data sequence s[n] into K sub-streams, with a serial to parallelconverter. The parallel flow is then modulated (i.e. symbols are assigned fromsome modulation constellation) which introduces the complex symbols ck. Thesecomplex symbols make up one OFDM symbol, which are converted to a time-domain signal with the IDFT. Finally the complex sequence s̃[n] is realized to acontinuous signal s(t). For more information of the mathematical description ofOFDM, see appendix A.

In the system described here, the K data streams modulate K sub-carriers.The number of sub-carriers is an important parameter of an OFDM system, sinceit determines the size of the frequency band used and how much data an OFDMsymbol can carry. The number of sub-carriers is a property that will be usedfrequently in this thesis, but the text might refer to sub-channels or sub-bandsinstead. Sub-carriers are often clustered into sub-channels or sub-bands to reducegranularity and to simplify the description. Figure 2.2 shows that one sub-channelcontains several sub-carriers.

2.2 Radio Interface Protocols

This section gives an overview of the architecture of the UTRA structure of a 3Gnetwork to provide some insight of where in a cellular network link adaptation ismade.

An UMTS network is divided into three parts. User Equipment (UE), UMTSTerrestrial Radio Access Network (UTRA) and Core Network (CN). The radio

2.2 Radio Interface Protocols 7

Figure 2.2. The relationship between sub-channel and subcarrier.


Figure 2.3. UTRA radio interface protocol architecture.

interface protocols are located in the UTRA and that is the scope of this section.For further reading about the UMTS system architecture, see [5].

2.2.1 UTRA

The UTRA, which is a 3GPP-standard for the air interface of a 3G network,consists of three layers; the physical layer (layer 1), the data link layer (layer 2)and the network layer (layer 3). The radio interface protocol architecture in theUTRA is shown in figure 2.3.

The data link layer can be further divided into the Radio Link Controller(RLC) and the Medium Access Control (MAC). The RLC offers services to higherlayers. These services are called Radio Bearers (in the control plane they are calledSignalling Radio Bearers). The RLC’s task is to provide reliability by segmentationand retransmission services.

Between the RLC and the MAC are logical channels. There are different logicalchannels for different types of data. For example; the Broadcast Control Channel(BCCH) and the Dedicated Control Channel (DCCH) in the control plane and theCommon Traffic Channel (CTCH) and the Dedicated Traffic Channel (DTCH) inthe user plane.

The MAC entity provides services to the RLC. The MAC maps the differentlogical channels to transport channels, which then are provided to the physicallayer. The transport channels describes how the data is to be transferred through

2.3 Mutual Information Quality Model 9

the radio interface. Besides mapping logical channels to transport channels, theMAC is responsible for the selection of the best-suited transport format (TF) foreach transport channel, i.e. the link adaptation. The TF contains informationabout error protection, interleaving, bit rate and mapping onto physical channels.The MAC is also responsible for scheduling and priority handling.

2.3 Mutual Information Quality Model

A quality model is used to quantify the performance and the behavior of the phys-ical channel. The link adaptation algorithms that are implemented in this thesiswork are based on previous developed algorithms at Ericsson. These algorithmsuse a mutual information (MI) quality model. Since quality modelling is a funda-mental issue of link adaptation, and some terms in the MI model are frequentlyused in this thesis, this section will explain the basics of the MI quality model.

2.3.1 Symbol-Level Mutual Information

When modelling a digital communication system the transmitted data source isrepresented by a discrete stochastic variable X, which takes values from the mod-ulation constellation used. The received data is typically often represented as acontinuous stochastic variable Y , with values in the set of complex numbers. If thechannel is good-natured, X and Y are strongly correlated. In the worst-case theyare independent of each other. A measure of the information between transmittedand received symbol is the average mutual information, defined in 2.1.

Definition 2.1 Average mutual information

I(Y ;X) =N−1∑

i=0

∫

y∈C

P (xi, y) log2P (xi, y)

P (y)P (xi)dY

=

N−1∑

i=0

∫

y∈C

P (xi)P (y|xi) log2P (y|xi)

N−1∑i=0

P (xi)P (y|xi)dY

where X ∈ {x0, x1, ..., xN−1} and Y ∈ C. P (y|xi) is the transition probabilityfunction of Y conditioned on X and P (x) is the a-priori probability of X.

Example 2.1

For QPSK, X takes values from the set of complex coefficients {ck = aI + jaQ}where aI , aQ ∈ {−1,+1} and k ∈ {1, 2, 3, 4}. The symbol constellation of QPSKis shown in figure 2.4.


Figure 2.4. QPSK symbol constellation (Gray coding of the symbols is also shown).


For a time-varying channel, the transition probabilities changes with changingSignal to Noise Rate (SNR) and are written as P (y|xi, γ), where γ = EsN0 is theSNR. The expression for mutual information is then written as I(Y ;X, γ) or simplyI(γ).

Example 2.2

The average mutual information or the modulated symbol-level mutual informa-tion (SI) as it is called in the MI-quality model, can be expressed in terms of theentropy of the stochastic variables X and Y .

I(Y ;X) = I(X;Y ) = H(X) − H(X|Y )where H(X) is defined in 2.1.

H(X) = −N−1∑

i=0

P (xi) log2 P (xi) (2.1)

The SI is thus the entropy of the source X, minus the uncertainty that remainsabout the source after knowing the reconstructed symbol Y .

The well known Shannon channel capacity states the capacity for a time dis-crete memoryless channel, as defined in 2.2

Definition 2.2 Shannon capacity

C = maxP (X)

I(X;Y ) = maxP (X)

[H(X) − H(X|Y )] [bits/symbol]

For an Additive White Gaussian Noise (AWGN) channel, the maximum capac-ity is reached when X is a zero-mean Gaussian distributed stochastic variable. Inthis special case the capacity defined in 2.2 can be written as in 2.3.

Definition 2.3 AWGN channel capacity

C = log2(1 + γ) [bits/symbol]

Where γ = ( EsN0

) is the Signal to Noise Ratio (SNR).The channel capacity defined in 2.3 is a less good practical measure of capacity.

It is however possible to reach capacities quite close to the Shannon bound. Thisis seen in figure 2.5, where the capacities (SI) for different modulation methods,using Bit-Interleaved Coded Modulation (BICM), are plotted for different SNR:s.


2.3.2 Block-Level Mutual Information

Virtually all communication systems use some kind of coding before the transmis-sions. The data is coded and decoded in blocks of a certain length (see e.g. [3] forsome basic overview about coding.) It is therefore practical to define a capacitymeasure based on code blocks instead of single symbols as in the previous section.A capacity measure at code block level also expresses the behavior of the code,which is desirable.

Received Bit Information

Consider a code block of length N , with K information bits. If the N bits aremapped to symbols using a M-ordered modulation scheme, the block is transmittedusing J = Nlog

2M

symbols. The average mutual information for the received blockis defined in the MI quality model as the Received Bit Information (RBI).

Definition 2.4 Received Bit Information (RBI)

RBI(γj) =J∑

j=1

I(γj)

I(γj) is the average mutual information for symbol j at channel state γj definedin 2.1. It should be noted that I(γj) has to be numerically evaluated. In practice,a lookup table (eg. SIR2SI(γj , modulation)) is used based on the plotted curvesin figure 2.5.

Received Bit Information Rate

When the RBI is normalized with respect to the code block length, the channelcapacity per bit is expressed. This is defined as the Received Bit Information Rate(RBIR).

Definition 2.5 Received Bit Information Rate (RBIR)

RBIR(γj) =RBI(γj)

N

Shannon’s channel coding theorem states that for a code rate less than thechannel capacity there exists a coding system with the property that as the lengthof the code block increases, the probability of an error occurring in the decodedblock approaches zero, see theorem 2.1.

Theorem 2.1 Shannon coding theoremFor a rate R < C there exists a coding system with arbitrarily low block and biterror rates as the code length N → ∞.


−20 −15 −10 −5 0 5 10 15 20 25 300

1

2

3

4

5

6

7

8

9

10

SI [

bits

]

SIR [dB]

ShannonQPSK16QAM64QAM

Figure 2.5. Capacity in bits/symbol (SI) plotted for different Signal to InterferenceRatio (SIR)

According to the coding theorem, it is thus possible to transmit error free whenusing a code rate slightly less than the RBIR. However, this requires a code witha very long block length, which is not very practical. To compensate for this, theactual code rate has to be somewhat reduced. How much the code rate has to bereduced depends on the code being used. When using the 3GPP Turbo Code amapping between RBIR and actual code rate has been developed empirically inprevious work at Ericsson.

Chapter 3

3G Long Term Evolution

With HSDPA and Enhanced Uplink technology, 3GPP considers 3G to be a com-petitive system for quite a long period of time. But in order to stay competitivewith emerging mobile network technologies and to satisfy the ever growing requestfor higher speeds and better coverage from users and operators, 3GPP is planningfor a long term evolution of 3G, see figure 3.1.

Enhanced Uplink

Additional enhancements

Enhanced Downlink(HSDPA)

Rel 4 Rel 5 Rel 6

WCDMAWCDMA EvolvedEvolvedWCDMAWCDMA

R99

Figure 3.1. 3G Long Term Evolution (LTE).

3.1 Overview

The 3G LTE concept does not specify any particular technology yet; the funda-mental specification is planned to be released by 3GPP in June 2007.

Companies within 3GPP, such as NTT DoCoMo, Alcatel, Cingular Wireless,Ericsson, Lucent, Motorola, Nokia, Nortel, Qualcomm and Siemens are currentlyworking with different projects to contribute to the LTE.

The main targets for the 3G LTE concept at Ericsson are:

• Packet switching only. The circuit switched voice connections of today’ssystems will be replaced with voice over IP (VoIP).

• Higher data rates and better coverage. Speeds beyond 100 Mbit/s in thedownlink and 50 Mbit/s in the uplink will be possible in the entire cell area.

• Reduced delays. Less than 10 ms round-trip time (UE-RNC-UE)

15

16 3G Long Term Evolution

Figure 3.2. Suggestion for Resource Unit (RU) specifications in the 3G LTE concept.

• Improved capacity by better spectrum efficiency.

• Spectrum flexibility. Possibilities to operate in different spectrum allocationsand to use scalable bandwidths.

• Cost-effective transition from current 3G WCDMA release.To achieve these targets, the physical layer in the LTE concept at Ericsson is

based on OFDM, at least in the downlink. There are several advantages with usingOFDM. One of them is spectrum flexibility. With OFDM, it is easier to operate indifferent spectrum allocations of different sizes. Another advantage is that OFDMhandles fading in a much better way than single-carrier systems and the possibilityto use frequency domain adaptation to enhance system performance.

3.2 Technical Description

This section describes some technical details about the downlink specificationsin the LTE concept, that are of interest for link adaptation. The concept is stillsomewhat undefined, the parameters and numbers listed here are rather to be seenas thesis assumptions. More detailed parameter setting are described in SimulationModel, chapter 6.

3.2.1 Resource Unit

A Resource Unit (RU) is the smallest data bearing unit in the LTE concept. A RUhas both an extension in time and frequency, see figure 3.2. The time extension of0.5− 1 ms, which is also the transmission time interval (TTI), is sufficiently smallto satisfy the less than 10 ms RTT requirement and allows at the same time forefficient time-domain adaptation. The frequency extension is approximately 200kHz, and one RU contains around 10 OFDM-symbols.

In this thesis work it is assumed that there are no dedicated transport channels.The resources are shared by the users in time and frequency domain, see figure 3.3.A RU can be assigned to one and only one user, but users can be assigned morethan one RU. The assigned RUs do not have to be consecutive (in the downlink).

3.2 Technical Description 17

Figure 3.3. Scheduling over both time and frequency domain.

3.2.2 Other Parameters

Below are some other parameters for the concept listed:

• Retransmission scheme: Fast hybrid ARQ with incremental redundancy.

• Coding: 3GPP Turbo code.

• Modulation: At least QPSK and 16QAM. Most likely also 64 QAM for thedownlink. 256QAM is considered for downlink line of sight situations.

18 3G Long Term Evolution

Chapter 4

Link Adaptation

In a cellular network system, the received signal to interference ratio (SIR) atthe UE varies due to e.g. multipath effects and interference. If the transmissionparameters were set to deal with the worst case channel state, a lot of capacitywould be wasted when the UE experience good channel conditions. Instead shouldthe transmission parameters be continuously adjusted to the channel condition.

Adjusting transmission parameters according to the channel condition is gen-erally called link adaptation. Figure 4.1 shows a basic link adaptation scheme forsome arbitrary digital communication system.

Figure 4.1. Basic link adaptation.

In WCDMA UMTS Release 99, adaptation to the channel conditions is madewith power control. This ensures similar service quality to all communication linksin a cell, despite differences in channel conditions. However, power control is notthe most efficient way to allocate the available resources [1].

In HSDPA, the adaptation is done by rate control instead. The transmissionpower is constant over the TTI and the data rate is adjusted by using differentcoding rates. In HSDPA it is also possibly to increase the spectral efficiency byswitching modulation scheme from QPSK to 16QAM.

In the 3G LTE concept, which uses OFDM, the possibility to use link adapta-tion is not restricted to the time-domain only, as in WCDMA UMTS R99. The

19

20 Link Adaptation

frequency division property of OFDM makes it possibly to do adaptation in thefrequency domain as well. This offers opportunities to really exploit the channeland enhance the throughput. For example, assume that some of the sub-channelsexperiences a higher attenuation than others, then these sub-channels will domi-nate the error probability. With frequency domain adaption this can be avoidedby selecting different modulation and power levels for the different sub-channels,depending on how they are affected by the channel attenuation. The next sectionsin this chapter will describe link adaptation for OFDM in more detail.

4.1 Goals

The overall goal with link adaptation is to adjust the transmission parameters tothe channel condition, in order to optimize the transmission.

4.1.1 Transmission Parameters

Typical transmission parameters for an OFDM system with K sub-channels, thatare of interest for link adaptation, are the following:

The assigned amount of power for sub-channel k: Pk

The number of bits assigned to sub-channel k: bk = log2(M)∗ carriers fora M-ordered modulation, where carriers is the number of sub-carriers in thesub-channel.

Along with the transmission parameters there are usually also constraints forthe transmission. The most common constraints are:

The total available power: Ptot

The maximum block error rate: BLERtarget

The desired number of bits that should be transmitted Bmin

4.1.2 Optimization Criteria

Based on the transmission parameters and the constraints, different approachesfor transmission optimization can be made. The three most basic optimizationsare listed below.

To minimize the transmit power, while a certain bit rate is achieved and theerror rate is less than a target error rate.

To maximize the bit rate, while the error rate is less the a certain target andunder a power constraint.

To minimize the error probability, while a certain bit rate is achieved andunder a power constraint.

4.2 Optimal Link Adaptation 21

The system and its applications determine which of the above optimizationschemes that is appropriate. The link adaptation approach that is of most interestfor cellular network transmissions (especially in the downlink), and which will bein focus of this thesis is to maximize the bit rate.

The bit rate maximization problem can be expressed as,

max∑

k

bk (4.1)

with the constraints: ∑

k

pk ≤ Ptot

BLER ≤ BLERtargetRemember that the reasoning made here is in very general terms with focus

on the communication link between a transmitter and a receiver, i.e. as it is seenfrom a single-user perspective. However, the same ideas are applied in multi-usersystems. The links between an access point and the users are still optimized, butsince the users share resources the complexity of the optimizations is increased.

It should also be noted, that in a multi-user system it is the responsibility ofthe scheduler to ensure user satisfaction and the overall system optimization, withrespect to quality of service (QoS) and priority policies.

4.2 Optimal Link Adaptation

As described in the previous section, the goal with link adaptation is to find theoptimal transmission parameters in order to exploit the channel capacity as muchas possible. The transmission parameters that are subject to this optimization arethe power levels (Pk) and the number of bits (bk) for the different sub-channels.The process of finding these parameters is often called bit- and power loading (orallocation).

Finding the optimal transmission parameters often requires heavy computa-tions. It also requires perfect knowledge of the channel quality, in order to per-form well. This makes optimal link adaptation useful mostly for systems were thechannel quality changes relatively slowly, for example in fixed wired systems likeDSL.

4.2.1 Power Allocation using Water-Filling

Information theory states that the channel capacity for a time discrete, memorylesschannel with additive white Gaussian noise (AWGN) is

C =1

2log2(1 + γ) [bits/dimension] (4.2)

where γ = EsN0

, Es is the signal power and N0 is the variance of the normaldistributed channel noise (AWGN). A proof should be found in most books oninformation theory, for example [6].

22 Link Adaptation

Consider an OFDM system with K sub-channels, and lets say that these Ksub-channels are time discrete, memoryless AWGN channels with independentnoise variances. Then the channel capacity for the system is given by

C =

K∑

k=1

1

2log2(1 + γk) [bits/dimension]

If the total available power in the system is Ptot, how should the power bedistributed among the K sub-channels so that the maximum channel capacity forthe system is reached?

For the special case when the noise on the parallel channels is equal, the powershould also be equally distributed. For the more general case, when the channelssuffer from noise with different variances, the optimal power distribution is not soobvious. The optimal solution is given by the famous water-filling proposition.

The principle of water-filling can be described by pouring water into a tankwhere the shape of the bottom is determined by the noise of the different channels.Figure 4.2(a) illustrates this. The total available power is represented by theamount of water that is poured in the tank. Channels with noise that exceeds thewater level (L) in the tank, will not be given any power. However, if the availablepower in the system increases, more water will be poured in the tank. With anincreasing water level, power will also be given to noisier channels.

4.2.2 Near-Optimal Bit-Loading

The water-filling solution gives the optimum power distribution of the sub-channels.Sub-channels with high SNRs will be allocated more power and can therefore usea higher order modulation scheme and carry more bits, than lower SNR sub-channels. That is intuitive, but the details about what modulation- and codingscheme to use for each sub-channels is not revealed in the water-filling solution.

With the use of water-filling algorithms it is thus in theory possibly to achievethe maximum channel capacity. That requires however e.g. infinite granularity ofthe modulation constellation. Since this is not practically achievable, quantizationhas to be done and distortion is introduced.

With the use of a greedy algorithm based on the water-filling result it is pos-sible to achieve the optimal discrete bit- and power loading distribution [4]. Onesuch algorithm is Hughes-Hartog’s (HH) algorithm, another is Chow’s algorithm,which is used in ADSL systems. Both algorithms are near-optimal solutions to thebit-loading problem using water-filling, but Chow’s algorithm has some benefitsconcerning implementation issues [2].

Greedy algorithms for bit loading requires that many iterations over the sub-carriers are made, which leads to heavy computations. Heavy computations areespecially unwanted when the adaptation is to be made for a fast time-varyingwireless channel, because the risk is great that the calculated solution is madewith outdated channel quality knowledge.

4.2 Optimal Link Adaptation 23

(a) Power allocation using water-filling. (b) Power allocation using fix equally powerdistribution.

(c) Power allocation using fix equally power distribution, with threshold

Figure 4.2. Different power allocation strategies.

24 Link Adaptation

4.3 Simplified Link Adaptation

Link adaptation algorithms like Chow’s or HH’s require perfect channel qualityknowledge (also called Channel Quality Information, CQI) in order to achieve nearmaximum channel capacity. In wireless cellular systems the channel quality (CQ)for the users can change relatively fast, which greatly decreases the gains of usingsuch algorithms.

With fast varying CQ, it’s more advantageous to use simplified link adaptationalgorithms. The following sections describe different simplifications for bit- andpower loading.

4.3.1 Constant Power Allocation

Power loading can be simplified by the use of a constant power allocation schemewhich distribute the power uniformly, se figure 4.2(b). [8] shows that the optimalwater-filling solution does not give any great gains compared to distributing thepower evenly over the sub-channels.

With this approximate water-filling algorithm, the critical issue is to leaveout the sub-channels that are left out in the water-filling solution. This is becauselog(1+SNR) is more sensitive to SNR when SNR is low, which makes it importantto ensure that low SNR sub-channels are allocated the correct amount of power[8].

It is thus important to sort out the bad sub-channels (i.e. the ones with lowSNR) and instead distribute the power equally over the good ones. The selectionbetween good and bad sub-channels has be made carefully. If a threshold betweengood and bad sub-channels was to be set, it should aim at the water-level thatwas the result of using water-filling. Figure 4.2(c) shows two different selections ofthreshold. A good chosen threshold (in this case T2) will save power from beingwasted on bad sub-channels (compare to figure 4.2(a)).

It is however very difficult to achieve good results with a fixed threshold whenhaving a time-varying channel, such as in a cellular network. Instead, the thresholdshould be chosen in an adaptive way according to the channel quality. This ofcourse increases the complexity of the power allocation. One such approach iscalled on-off power control, where an exhausting search algorithm is used to findwhich sub-channels that should be used (on) or not used (off).

4.3.2 Simplified Bit-Loading

The near-optimal bit loading previously described (section 4.2.2) has mainly threeproblems that make that approach unattractive to use in a cellular network system.

• A large signalling overhead is required, since each sub-carrier may use adifferent modulation scheme.

• With many sub-carriers, time consuming iterations over the sub-carriers arerequired in order to choose modulation method.

4.3 Simplified Link Adaptation 25

• A quality information measurement is required for each sub-carrier, whichamong other drawbacks, requires a large feedback channel.

To avoid these drawbacks, a single sub-carrier is not subject to adaptationwhen doing simplified bit-loading. The sub-carriers are instead clustered intolarger groups.

Sub-Carrier Clustering

In [4], sub-carriers are clustered into blocks and one modulation scheme is selectedfor each block based on the mean SNR of the sub-carriers in the block. It isshown that this approach has comparable performance and less computationalcomplexity than Chow’s algorithm. This algorithm, together with a constantpower allocation scheme that sorts out weak sub-channels, is proposed by [4] tobe a good candidate for possible extensions of the current high-speed WLANstandard. [4] also concludes that the algorithm is robust to a certain amount ofimperfect CQI updating, i.e. delay on the CQI feedback channel.

Clustering of sub-carriers is also a fundamental property of the LTE concept,(see section 3.2), where one RU contains 12-14 sub-carriers. Modulation adap-tation of the RUs can be done in the time domain (TD) or in both time- andfrequency domain (TFD).

TFD modulation adaptation uses different modulation schemes for differentRUs, whereas TD adaptation selects a common scheme for all the RUs. The lattergives of course less signalling overhead, but the adaptation is on the other handonly made in the time-dimension, losing some of the advantages with OFDM.

The selection of modulation scheme should be based on the channel qualitymodel used. Using the MI-quality model described in section 2.3, the receivedbit information (RBI) for a RU (TFD adaptation) or a collection of RUs (TDadaptation) is used to determine what modulation to use.

The RBI was introduced in the MI-quality model as a measure of the mutualinformation carried by a number of received symbols that has been transmittedthrough an AWGN channel.

Example 4.1

Consider some system where modulation methods are to be selected for 10 RUsin order to maximize the throughput. The SIR estimates of the RUs are shownin figure 4.3(a). Three modulation schemes are supported; QPSK, 16QAM and64QAM. Which modulations should be selected, in order to maximize the through-put?

For each RU and for each modulation scheme, a RBI value is calculated. Atypical TFD modulation adaptation would select the modulation scheme that max-imizes the RU RBI, as shown in figure 4.3(b). With TD adaptation that is notpossible, since only one modulation scheme can be selected. The selected schemeis the one that maximizes the sum of RBI for all the RUs.

When using the SIR to SI mapping shown in figure 2.5, the results in table4.1 are achieved. This assumes that there are 128 symbols in each RU, which is

26 Link Adaptation

a number that depends on how the system parameters are defined (i.e. the timeand frequency extension).

Modulation Adaptation Resulting RBI [bits]TFD 3421.89TD, using 64QAM 3194.96TD using 16QAM 3398.07TD using QPSK 2365.40

Table 4.1. RBI values for different modulation adaptation algorithms and modulationschemes.

A very small gain (0.7%) is seen in this example when using TFD modulationadaptation, compared to the best result with TD adaptation.

Example 4.2

This example is based on the same TD and TFD modulation adaptation as in theexample above, but shows a greater gain for TFD adaptation, se figure 4.4(a) and4.4(b).

Modulation Adaptation Resulting RBI [bits]TFD 919.53TD, using 64QAM 791.40TD using 16QAM 811.71TD using QPSK 678.17

Table 4.2. RBI values for different modulation adaptation algorithms and modulationschemes.

As seen in table 4.2 the gain for TFD adaptation is greater than in the previousexample. The gain for this constructed example is 11.7%.

4.3.3 Code Rate Adaptation

Some words should also be said about code rate adaptation. With sub-carriersclustered into blocks, the code rate adaptation could be made block-wise. However,the code blocks cannot be made too small, and for simplicity of retransmissionschemes and protocols, often a single code rate is used for the transmitted data.

The following three different approaches to code rate adaptation are for exam-ple possible in the LTE concept.


• RU-wise. Each RU is a code block and the rate is selected based on theRBIR of the RU.

• Block-wise. A collection of RUs make up one code block and the rate is thusadapted based on the RBIR for the block.

• Single rate. One code block includes all of the RUs and the code rate isadapted based on the RBIR of all of them.

These code rate adaptation schemes are based on RBIR (see section 2.3).RBIR, which is the normalized RBI, is the code rate that theoretically can beused to transmit error free with the capacity given by the RBI, using an idealcode.

28 Link Adaptation

1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

16

18

20

Sig

nal t

o In

terf

eren

ce R

atio

[dB

]

Sub−band

(a) SIR estimates for different RUs (sub-bands).

1 2 3 4 5 6 7 8 9 100

50

100

150

200

250

300

350

400

450

500

Rec

eive

d B

it In

form

atio

n [b

its]

Sub−band

64QAM16QAMQPSK

(b) Typical selection of modulation scheme by a TFD modulation adaptationalgorithm.

Figure 4.3. Modulation adaptation example.


1 2 3 4 5 6 7 8 9 100

2

4

6

8

10

12

14

16

18

20

Sub−band

Sig

nal t

o In

terf

eren

ce R

atio

[dB

]

(a) SIR estimates for different RUs (sub-bands).

1 2 3 4 5 6 7 8 9 100

50

100

150

200

250

300

350

400

450

50064QAM16QAMQPSK

Rec

eive

d B

it In

form

atio

n [b

its]

Sub−band

(b) Typical selection of modulation scheme by a TFD modulation adaptationalgorithm.

Figure 4.4. Modulation adaptation example.

30 Link Adaptation

Chapter 5

Implementation

This chapter describes the implementation of the link adaptation algorithms, inthe simulator. First, some fundamental concepts of the simulator are explained,such as Channel Quality Information (CQI) and Transport Format (TF). Then,the different implemented link adaptation functions are described in detail.

5.1 Simulator System Description

The simulator used in this thesis is a cellular network simulator with supportfor different traffic models. Since the simulator is very complex there are manyparameters and concepts that are important but all can not be explained here. Thissection contains only the definitions and system parameters that are importantfrom a link adaptation perspective.

5.1.1 Basic Parameters

The following list contains basic simulator system parameters that will be fre-quently used when describing the implemented algorithms later in this chapter.

Number of sub-bands: K

Number of users: N

Supported modulation methods: M = {M1,M2, ...,ML} == {QPSK, 16QAM, 64QAM}

Downlink transmit power per cell: Ptot

The frequency band is divided into sub-bands, and for the study in this thesisthere is a one-to-one correspondence between sub-bands and Resource Units (RUs).However, the number of sub-bands could be increased (so that there is more thanone sub-band in a RU) to refine the granularly of the simulation. On the otherhand, an increasing number of sub-bands would yield slower simulations.

31

32 Implementation

5.1.2 Channel Quality Information

With a certain period of time, downlink Channel Quality Information (CQI) isestimated for each user and for all sub-bands on the frequency band. CQI for auser is expressed as a vector of Gain to Interference Ratios (GIR), one for eachsub-band.

Definition 5.1 Channel quality information (CQI) for user n

CQIn = [GIRn1 , GIRn2 , ..., GIR

nK ]

A GIR value describes the ratio between the signal gain and the interference,as experienced by the receiver on a certain sub-band. The signal gain is the resultof path-loss and multi-path fading. The interference includes noise and interferingtransmissions from other base stations in the network. A higher GIR indicates abetter sub-channel.

Definition 5.2 Gain to Interference Ratio (GIR) on sub-band k for user n

GIRnk =signal path gain

interference

5.1.3 Transmission Resources

In each cell c, the base station has certain transmission resources (TR). (A TRis a collection of transmission resources.) The TR is shared among the users inthe cell, and it is the responsibility of the scheduler in the cell to handle theseresources and distribute them to the users.

The TR in the simulator basically contains the following:

• Downlink transmit power, P ctot.• Available RUs, K ′. This is a set with RUs that are currently available for

transmission. For example, when using a frequency domain scheduler, thesize set is becoming smaller as users in the cell are scheduled (and reserveRUs). |K ′| = K is true when using a time domain scheduler, because onlyone user is scheduled in each TTI.

5.1.4 Transport Format

When a user is scheduled, the scheduler requests a transport format (TF) fromthe link adaptation. The TF, which describes certain transmission parameters, isthen sent to the physical layer.

The TF in the simulator basically contains the following information:

• The code block length, B.• The code rate, Cr.• A list with modulation methods that should be used, [m1,m2, ...,mK ].• A list of power levels, [P1, P2, ..., PK ].

5.2 Link Adaptation Implementation 33

5.2 Link Adaptation Implementation

Figure 5.1 shows an overview over the implemented link adaptation. The differentblocks are further explained in this chapter.

Figure 5.1. Flowchart of link adaption, as implemented in the simulator.

5.2.1 Power Allocation

This section describes the different power allocation algorithms that are imple-mented in the simulator. The input for these algorithms is a TR, as shown infigure 5.1.

Fixed Power Allocation

The fixed power allocation algorithm distributes the available power equally overall of the RUs assigned for the transmission, hence it does not take the channelquality in consideration at all.

Input

• A TR.

Algorithm

The power per RU is defined as follows.

Definition 5.3 The power for RU k

Pk =

{P ctot|K′| , k ∈ K ′0, else

34 Implementation

where |K ′| is the number of available RUs in the TR.Fixed power allocation is done in constant time, the algorithm complexity is

thus O(1).

Output

• A list of power levels, [P1, P2, ..., PK ].

On-off Power Allocation using Fixed Threshold

As described in section 4.3.1, power can be "saved" if there is a selection betweengood RUs and bad RUs using some threshold. The power is still equally dis-tributed, but only among the good RUs. If the threshold is selected carefully, thispower allocation will perform better than the fixed power allocation algorithm.

Input

• A TR.

• A threshold value, T .

Algorithm

First the selection between good and bad RUs is made, based on the giventhreshold.

Definition 5.4 Good and bad RUs

Kgood = {k : GIRnk ≥ T}

Definition 5.5 The power for RU k.

Pk =

{P ctot

|Kgood|, k ∈ Kgood

0, else

The algorithm complexity is O(|K ′|), since it takes as many operations as thereare available RUs to find the set {Kgood}.

Output


On-off Power Allocation using Adaptive Threshold

Since it is difficult to chose a good fixed threshold, the previous algorithm canbe extended to search for the threshold that maximizes the mutual information(RBI).

Input


• A TR.

Algorithm

1. Sort the vector CQIn in descending order to get the list CQInsorted.

2. Let K ′i represent a set of i consecutive RUs from the list, starting with thefirst element (i.e. the RU with highest GIR). Iterate i from 1 to K ′ to getthe sets K ′1,K

′2, ...,K

′K :

K ′1 = CQInsorted[1]

K ′2 = CQInsorted[1, 2]

...K ′K = CQI

nsorted[1, 2, ...,K] = CQI

nsorted

3. For each set K ′i, calculate the RBI to get a throughput mapping.

RBIi = RBI(K′i)

4. The set Ki for which i maximizes RBIi is defined as the set K ′good.

K ′good = maxi

RBIi

Definition 5.6 The power per RU for user n in cell c.

Pk =

{P ctot

|Kgood|, k ∈ Kgood

0, else

The sorting of CQIn requires O(|K ′|) operations, the creation of the sets K ′irequire O(|K ′|). The throughput mapping requires O(L|K ′|) operations, where Lis the number of modulation schemes. Thus, the complexity is O(L|K ′|).

Output


5.2.2 Channel Quality Estimation

The CQI contains information of the downlink channel quality (CQ) that was ex-perienced by the user in the latest measurement. The Channel Quality Estimation(CQE) is an estimate of the CQ at the next transmission.

The CQI in a previous TTI gives of course an indication of the CQ in the nextTTI, but there are also factors that could have changed in the time elapsed betweentwo TTI’s, which also change the CQ. One such factor is e.g. the interference. Asdescribed in this section, the implemented CQE in this thesis assumes that theinterference does not change between the latest CQI and the next transmission.

36 Implementation

Input

• A list of power levels for user n, Pn = [Pn1 , Pn2 , ..., PnK ].

• Channel Quality Information for user n, CQIn.

Algorithm

Based on the CQI, a user’s CQE is calculated. The CQE is expressed as avector of SIR values, one for each sub-band.

Definition 5.7 Channel quality estimation (CQE) for user n

CQEn = [SIRn1 , SIRn2 , ..., SIR

nK ]

The SIR values are calculated as follows

Definition 5.8 Signal to interference ratio (SIR) for user n on sub-band k

SIRnk = Pnk GIR

nk

where Pnk is the power assigned to sub-band k.

Note that in order for this CQE to work well, it is required that the CQI isreliable to a certain degree. That means that the changes from one CQI report toanother has to be relatively small. This implies that the power allocation used inthe network should be fixed and equally distributed over the sub-bands, a time-domain scheduler should be used, the amount of traffic should be almost constantand the users should move relatively slow.

The reason why the accuracy of the CQE decreases when using a web trafficmodel is because the amount of traffic in the network is more likely to changefrom one TTI to another. A CQI report is made based on the present interference(which relates to the amount of traffic in the network) at a given time. If the trafficchanges to the next TTI, the interference will change as well, which decreases theCQE accuracy if the change of interference was not foreseen.

The accuracy of the CQE will also decrease when the power is not equallydistributed among the sub-bands. That is because it is difficult to estimate howthe base stations will set the power levels, i.e. the different Pnk , because it dependson the channel conditions of the users. The allocation made by one base stationwill, in addition, effect the allocation of the others, creating a cyclic dependencebetween the power allocations being made.

Output

• Channel Quality Estimation for user n, CQEn.


5.2.3 Modulation Adaptation

The implemented modulation adaptation corresponds to the previously Ericsson-developed algorithms called LA3 and LA4. LA4 does modulation adaptation in thetime-domain, whereas LA3 does adaptation in both time-and frequency domain,as described in section 4.3.

Input


Algorithm

The modulation adaptation uses two quantities for describing the informationcarried in a RU; received bit information (RBI) and the number of raw bits (B).

RBI was explained in 2.3 but some clarification about how RBI is used heremight be necessary. RU RBI is used when considering the RBI for one RU, and isdenoted RBIk, see definition 5.9. A principle flowchart of the function SIR2RBI,which is used to calculate the RU RBI, is shown in figure 5.2. RBI for a wholecode block is called accumulated RBI.

Definition 5.9 Received Bit Information (RBI) for RU k

RBImik =J∑

j=1

SIR2SI(SIRk,mi),mi ∈ M

where J is the number of symbols in RU k and mi is a modulation method.The number of raw bits (see definition 5.10) describes the number of data bits

carried in one RU. This number is determined by the number of symbols in theRU and the modulation method used.

Definition 5.10 Number of raw bits (B) for RU k

Bmik =

J∑

j=1

log2 Mmi ,mi ∈ M

where Mmi is the order for modulation mi, eg. MQPSK = 4 and M16QAM = 16.

• LA4 - Time Domain Modulation Adaptation

A flowchart for the LA4 algorithm is shown i figure 5.3. The algorithm loopsthrough the RUs with assigned power (greater than zero), in the list CQEn. Thefollowing steps are then taken for each RU:

38 Implementation

Figure 5.2. Flowchart describing how the RU RBI is calculated using a SIR to symbolinformation (SI) mapping.


1. Calculate the RBI for the selected RU k with the available modulationmethods. The result is a set of values RBIm1k , RBI

m2k , ..., RBI

mLk , where

mi is some modulation method from the set of supported modulations.With the modulation methods supported in the simulator used the resultis: RBIQPSKk , RBI

16QAMk , RBI

64QAMk . These values are called RU RBIs

and indicate how many bits it will be possible to send in that RU, using idealcoding. The RU RBI is calculated using the function SIR2RBI, for which aflowchart is shown in figure 5.2.

2. Calculate the number of raw bits with the available modulation methods toget the set Bm1k , B

m2k , ..., B

mLk . These are the actual bits that are carried by

the RU.

3. Add the calculated RU RBI for each modulation method:∑K′

1 RBIQPSKk ,

∑K′1 RBI

16QAMk and

∑K′1 RBI

64QAMk .

where K ′ is the number of RUs that have been assigned to the user so far.

4. Add the number of raw bits for each modulation method:∑K′

1 BQPSKk ,∑K′

1 B16QAMk and

∑K′1 B

64QAMk .

Select the modulation ms that gives the greatest sum∑K′

1 RBImsk . ms will

then be the common modulation method for the assigned RUs K ′.∑K′

1 RBImsk is

the accumulated RBI of the code block, which indicates how many bits that willbe correctly received by the UE. The accumulated number of raw bits,

∑K′1 B

msk ,

tells how many bits that will be transmitted and thus the code block length.

• LA3

A flowchart for the LA3 algorithm is shown i figure 5.4. The algorithm loopsthrough the RUs with assigned power (greater than zero), in the list CQEn. Thefollowing steps are then taken for each RU:

1. Calculate the RBI for the selected RU k with the available modulation meth-ods. The result is a set of values RBIm1k , RBI

m2k , ..., RBI

mLk , where mi is

some modulation method from the set M . In the simulator used, the resultis RBIQPSKk , RBI

16QAMk and RBI

64QAMk .

2. Select the modulation ms ∈ M that gives the greatest RBI, ie. the maximumelement in the set {RBIm1k , RBIm2k , ..., RBImLk }The selected modulation method ms will be used for RU k, and added tothe list of modulations Ms = [m1,m2, ...,m′K ], where K

′ is the number ofRUs used so far by the algorithm.

3. Add RBImsk to the accumulated RBI∑K′

k=1 RBImkk where mk = Ms[k] and

K ′ is the number of RUs assigned so far.

4. Add the number of raw bits for RU k using modulation ms to the accumu-lated number of raw bits,

∑K′k=1 B

mkk .

40 Implementation

Figure 5.3. LA4 as implemented in the simulator.


The list Ms = [m1,m2, ...,m′K ] tells what modulation to use on what RU. Theseare the modulation methods that maximizes the throughput for user n. Theaccumulated number of raw bits;

∑K′k=1 B

mkk , (mk = Ms[k]) is the code block

length and the accumulated RBI;∑K′

k=1 RBImkk , (mk = Ms[k]) is the RBI of the

code block, which gives an indication of the achievable capacity assuming an idealchannel coding.

Output

• A list of selected modulations, Ms = [m1,m2, ...,m′K ]. Note that for LA4 allof the elements in the list will contain the selected common modulation ms.

• Accumulated RBI, ∑K′

k=1 RBImkk where mk = Ms[k] for LA3 or mk = ms

for LA4.

• Code block length, N = ∑K′

k=1 Bmkk where mk = Ms[k] for LA3 or mk = ms

for LA4.

5.2.4 Code Rate Adaptation

As mentioned in section 2.3, the RBIR is the the code rate possible to use with anideal code, to achieve the channel capacity indicated by the RBI. The adaption ofcode rate according to the modulation adaptation is done in the following function.Remember that when using the 3GPP Turbo Code a lower code rate than thetheoretically one has to be chosen according to a correction model.

Input

• Accumulated RBI, i.e. the mutual information of the code block.

• Code block length N , i.e. the number of data bits in the code block.

• The target Block Error Rate (BLERtarget).

Algorithm

The code rate, Cr, is selected with the help of a mapping of RBIR, code blocklength and some code rate to a block error probability (BLEP)

BLEP = RBIR2BLEP(RBIR,B,Cr)

where RBIR is calculated as defined in 2.3. The function RBIR2BLEP is basedon empirical results from simulations made with the MI-quality model.

The BLEP gives an indication of what the BLER will be, and therefore thecode rate that gives a BLEP mapping that corresponds best to the target BLER,should be selected. This code rate can be found by doing interval halving overcode rates, using the RBIR2BLEP function.

In practice, it would be to slow to do interval halving every time the code rateadaptation is made. Instead, a lookup table is generated in advance. The table

42 Implementation

Figure 5.4. LA3 as implemented in the simulator.


is created for a certain target BLER, i.e. one table for a 10 percent BLER. Thetable maps a RBIR value and a code block length to a code rate that can supportthe target BLER.

Output

• A code rate, Cr.

5.2.5 SIR Back-off

The code rate adaptation should ensure that the BLER is kept close to the BLERtarget. This has however shown not to be the case since the code rate adaptationis based on interpolation, the 3GPP Turbo code itself has some properties thatmakes the code rate selection somewhat inaccurate and the fact that the CQEmay not be perfect. The BLER can however be controlled by a back-off in SIR,as shown in this section.

Input


• The target BLER, BLERtarget.

Algorithm

As defined in section 5.2.2, the CQE is a vector of SIR-values,

CQEn = [SIRn1 , SIRn2 , ..., SIR

nK ]

To make the BLER converge to the target BLER, a SIR back-off factor isapplied on the CQEn vector.

Definition 5.11 Back-off factor F

F = B ∗ N ∗ BLER − A ∗ N(1 − BLER)

N is the total number of transmitted blocks. N(1 − BLER) is the number ofsuccessfully transmitted blocks and N ∗ BLER is the number of unsuccessfullytransmitted. A is a compensation factor that will decrease the back-off F when ablock is correctly decoded, while B increases the back-off in case of unsuccessfuldecoding. Since F is supposed to back-off from an estimated SIR, it cannot beset to negative values by the algorithm (i.e. F < 0 ⇒ F = 0).

Whenever a decoding feedback is received the back-off F is applied to each SIRvalue as

compensated (SIRnk )dB = SIRnk − F. (5.1)

The result is a new set CQEs, CQEncompensated, which will make the actual BLERconverge towards BLERtarget, see theorem 5.1.

44 Implementation

Theorem 5.1 Convergence of BLERAssume that F converges to some convergence value L,

|F | < |L| as t → +∞

⇔ |B ∗ BLER − A ∗ (1 − BLER)| < | LN

|

As the time → +∞, the total number of transmitted blocks N → +∞. Then thefollowing is true;

B ∗ BLER − A ∗ (1 − BLER) → 0, N → +∞⇔ BLER(B + A) → A

⇔ BLER → AB + A

The result means that when having access to feedback from the decoder the BLERcan be controlled by the ratio A

B+A = BLERtarget. The size of A and B will de-termine the speed of the convergence, but also the sensitivity of the compensationfactor.

Output

• Compensated SIR estimates for user n, CQEncompensated.

Chapter 6

Simulation Model

This section describes the simulation model that has been used in the simulations.More detailed settings are described accordingly with the different simulations inthe next chapter.

6.1 Propagation Model

The propagation model describes the signal attenuation that the transmitted signalexperiences on the channel. The power of the received signal will be decreaseddue to physical propagation mechanisms like distance attenuation, reflection andscattering. To model this, four different elements are taken into account; distanceattenuation Gd, antenna gain Ga, shadow fading Gs and multi-path fading Gm.The product of these elements is called the path gain G.

G = GdGaGsGm < 1 (6.1)

6.1.1 Multi-Path Fading

A radio signal will most likely not take a single route from the transmitter to thereceiver, but it will reflect and scatter due to different objects in the environment.This phenomena is called multi-path and gives rise to the problem of fading anddispersion when receiving a signal.

Fading is due to the interference of multiple signals with random relative phase,which is caused by reflections. Differences in path delays of the received signalswill cause dispersion in time.

In this thesis’, has the 3GPP Typical Urban model been used for multi-pathmodelling.

6.1.2 Interference Model

The interference an user experiences is the combination of noise and interferingtransmissions on the same frequency. The Signal to Interference Ratio (SIR) foruser i is expressed as

45

46 Simulation Model

SIRi =PiGi∑J

j=0,j 6=i PjGj + N, (6.2)

where P is the signal power, G is the path gain as defined in 6.1, N is the noiseand J is the number of interfering users.

6.2 System Model

6.2.1 General System Parameters

The general system parameters used in the simulator are shown in table 6.1.

Downlink bandwidth 20 MHzDownlink maximum transmission power 80 WFrequency reuse factor 1TTI 6.667 msNumber of resource units 81Number of symbols per resource unit 128Supported modulation methods QPSK, 16QAM, 64QAM

Table 6.1. General system parameters

6.2.2 Fast HARQ

A fast Hybrid Automatic Repeat Request (HARQ) scheme is used for retrans-missions. The HARQ is implemented as a stop-and-wait protocol with 8 parallelprocesses. A retransmission for a process is thus only attempted after receiving aNegative Acknowledge (NACK) on that process from the UE.

Incremental redundancy (IR) is used at the UE for retransmissions. This ismodelled with ideal IR where the soft information (i.e. the decoder soft output,which means the reliability of the decoding result) is added for each retransmission.

6.2.3 Scheduling

The downlink scheduler algorithm used is a Round Robin (RR) scheduler that willschedule the user with the longest waiting time, i.e. the time since the user lastwas scheduled. This is under the prerequisite that there is something to transmitto the user. A user with a pending retransmission will always be prioritized.

The RR scheduler is thus time domain based. It will only schedule one user perTTI, which means that all the base station transmission resources will be availablefor the transmission.

6.3 Traffic Model 47

6.3 Traffic Model

6.3.1 Web Traffic

In order to make more realistic simulations, a web browsing traffic model basedon the TCP/IP protocol can be used. The traffic model determines the intensityand the size of the traffic. The model gives possibilities to set a range of differentparameters, for example the size of the objects that are downloaded and the user’sreading time.

Two different settings for the web traffic have been used with the simulationsin this thesis. The different settings are shown in table 6.2. Typical simulationtimes when using web traffic have been 500 seconds with model 1 and 100 secondswith model 2.

Setting Model 1 Model 2Reading time 5 s 1 sWeb page size distribution Log-normal FixedMaximum web page size 10 MB 1 MBMean web page size 5 MB 1 MBMinimum web page size 1 B 1 MB

Table 6.2. Web traffic model settings.

It should be noted that when a web traffic model is used, the measured through-put is the number of information bits received without protocol headers.

6.3.2 Full Buffers

In full buffer mode are the users’ send buffers, i.e. the outgoing traffic on thedownlink, set to it’s maximum value. When using full buffer mode there is alwaysdata to send to each user, which makes the traffic constant in the net.

6.4 Cell Deployment

Two different cell network sizes are used. The sites (base stations) are three-cellsites, giving a 9-cell or a 21-cell network with three or seven sites respectively. Thecells are hexagonally shaped and are repeated using a wrap-around technique, toform an infinite grid. This is to avoid border effects.

6.5 User Creation and Placement

Simulations can either be run with a fixed number of users that are created atinitiation or with an user generator which creates users dynamically.

The user generator in the simulator creates users according to a Possion processwith a certain intensity. The mean life time of the users in this thesis simulations

48 Simulation Model

is 90 seconds. The arrival intensity is set to 1 user per second, resulting in anaverage of 90 users.

The users are randomly placed over the simulation area, according to a uniformdistribution. The users move typically with a speed of 3 km/h in some randomdirection, if nothing else is stated.

6.6 Simulation Logging

The logging starts when the simulator starts, i.e. there is no delay in order forthe traffic model to stabilize. This could possibly effect the results when usingthe web traffic model. However, simulations with the web traffic model are runsignificantly longer than simulations with full buffers, in order to compensate forthis.

6.7 Simulation Seed

The user placement and e.g. the sizes of the web pages in the web traffic model arerandomly created based on the simulator seed. To statistically verify simulationresults, the same simulations setting should be run with different seeds.

Chapter 7

Simulation Results

This chapter presents the results from simulations using the simulation modeldescribed in chapter 6. The simulations are both made on link- and system level.Link level simulations study only the performance of a single radio connectionwith no interference from other transmitting stations. At system level, a cellularnetwork is modelled with a number of transmitting stations and moving users.

7.1 Performance Measures

The following performance measures are used in this chapter.

Cell Throughput

Cell throughput is the total downlink throughput per cell during a certain elapsedtime (usually one second).

Cell throughput =total transmitted information bits

number of cells ∗ elapsed time

User Throughput

User throughput is the user downlink throughput during a certain elapsed time(usually one second).

User throughput =transmitted information bits

elapsed time

User 5th Percentile Throughput

The user 5th percentile throughput is a measure of the throughput for the usersnear the cell border. 5% of the users in the network have a lower throughput thanthe 5th percentile throughput, while 95% have a higher throughput.

49

50 Simulation Results

User Packet Bit Rate

The packet bit rate is calculated by dividing the message size in bits by the timeelapsed from the message generation until it was complete received by the user.

packet bit rate =message size

transmission time

Block Error Rate

Block Error Rate (BLER) is the ratio between incorrectly received blocks andtotal number of transmitted blocks, for a user.

BLER =incorrectly received blocks

transmitted blocks

7.2 Block Error Rate

The code rate adaptation should maintain the block error rate (BLER) at a givenBLERtarget, to ensure that the constraint BLER ≤ BLERtarget is fulfilled. Thecode rate adaptation itself cannot do this, due to reasons mentioned in 5.2.5.This is shown in figure 7.1(a), where the BLER is around 30 % instead of theBLERtarget = 10%. With the use of a SIR back-off factor, the BLER will convergeto BLERtarget as shown in figure 7.1(b).

It is important to be able to control the BLER in order to keep the delay fromincreasing to levels that could have a negative impact on certain applications, likeVoice over IP (VoIP) or other real-time applications.

A BLERtarget = 10% with the SIR back-off algorithm is used in all the re-maining simulations in this thesis.

7.3 GIR Analysis

Intuitively, the advantage with time- and frequency domain modulation adapta-tion, i.e. LA3, is when the channel experiences deep frequency selective fading.Frequency selective fading in this thesis study (with frequency reuse factor one)will typically be due to multi-path effects both on the link between transmitterand receiver but also from interfering transmitters. The multi-path effects dependon the environment in which the network operates. An urban environment givesgreater multi-path effects than an open space area.

Frequency selective fading will decrease the GIRs on the affected sub-bandswhich leads to an increase in the variation of GIR. A measure of the effects offrequency selective fading is thus the variation of GIR over the frequency band fora user. The variation is expressed as the standard deviation, see 7.1.

Std(CQIn) =√

V ar(CQIn) = E{GIRnk − GIRnmean} (7.1)Two factors that affect the variance or standard deviation of a users’ GIR,

are the environment and the amount of interference. In this thesis work, the

7.3 GIR Analysis 51

0 20 40 60 80 100 120 140 160 180 2000.25

0.3

0.35

0.4

Time [s]

Blo

ck E

rror

Rat

e (B

LER

)

(a) BLER without SIR compensation.

0 20 40 60 80 100 120 140 160 180 2000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Time [s]

Blo

ck E

rror

Rat

e (B

LER

) [%

]

(b) BLER with SIR compensation.

Figure 7.1. BLER analysis.


changes in GIR variation is studied when the amount of interference is changed.The environment (multi-path model) will thus be held constant throughout thesimulations.

The interference can be changed for example by changing the number of trans-mitting stations in the network and by changing the cell radii. Both these ap-proaches are used here to investigate the variations in GIR.

7.3.1 Different Cell Radii

How changes in cell radii effect the GIR standard deviation, are studied in thissection. Specific parameters used for these simulations are listed in table 7.1.

Network size 9 cellsCell radii 1000,2000,3000 mNumber of users 10User speed 3 km/hTraffic model Web browsing (model 1) and full bufferPower allocation FixedBLER target 10%CQI perfect

Table 7.1. Simulation parameters for GIR analysis with different cell radii.

Full Buffer Traffic

Figure 7.2 shows a CDF of the distribution of the GIR standard deviation for theusers in the network. It is seen that the GIR standard deviation tend to decreasewith an increasing cell radius. Thus, with an increasing radius the noise will makeup for an increasing part of the interference. Since the noise is constant on thefrequency band, the variation in GIR will then only depend on the multi-patheffects from the transmitting base station.

With a cell radius of 3000 meters, the multi-path effects from interfering trans-mitters have obviously decreased. However, the difference in deviation for differentradii is not very large. The conclusions that can be drawn from this is that differ-ent cell radii (in the range of 1000 - 3000 m) have little impact on the standarddeviation of GIR, and therefore should no great performance differences betweenTD and TFD adaptation be expected when changing the cell radii.

Web Browsing Traffic

The previous section showed the GIR variance for full buffer traffic. Since manyof the simulations in this thesis work use a web traffic model, which is a morerealistic model, it is important to also study what impact the traffic model has onthe GIR standard deviation.

7.3 GIR Analysis 53

2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1000 m2000 m3000 m

GIR standard deviation [dB]

Figure 7.2. CDF of the average experienced GIR standard deviation on the downlinkfrequency band with full buffer traffic.

Figure 7.3 shows that the GIR standard deviation is slightly larger than forfull buffer traffic. With full buffer traffic a trend could be seen that the standarddeviation decreases with increasing radius, due to the fact that noise will dominatethe interference at larger cell radii. For web traffic the result is more unpredictable.No obvious trend can been seen, making it difficult to draw any conclusions howthe GIR deviation is affected when having web browsing traffic.

7.3.2 Different Network Sizes

The change of the GIR standard deviation has been studied for different cell radii,but how much does the standard deviation change due to an increasing numberof transmitting base stations in the network? And are there any differences to beexpected for different amount of loads? The simulation settings used to investigatethis are listed in table 7.2.

Figure 7.4(a) and figure 7.4(b) shows the deviation in GIR for a 9-cell networkand a 21-cell network respectively, for simulations with different load (i.e. eachcurve shows the distribution of the GIR standard deviation over the users, atdifferent loads). No great differences are seen for the two network sizes. Differentamount of load does not effect the deviation to any particular extent either.

Figure 7.5 shows an example of the difference in GIR standard deviation dis-tribution for the users in a 9-cell and 21-cell network with 630 users. These curvesare taken from the figure 7.4(a) and 7.4(b) to show that there are more users witha slightly higher GIR deviation in the 21-cell network than in the 9-cell network.


2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

GIR standard deviation [dB]

1000 m2000 m3000 m

Figure 7.3. CDF of the average experienced GIR standard deviation on the downli

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