UplinkChannelDependentSchedulingforFuture CellularSystemsliu.diva-portal.org/smash/get/diva2:23046/FULLTEXT01.pdfKeywords channel dependent scheduling, uplink, SC-FDMA, channel quality

Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

Uplink Channel Dependent Scheduling for FutureCellular Systems

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Kristina Jersenius

LITH-ISY-EX--06/3905--SE

Linköping 2007

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

Uplink Channel Dependent Scheduling for FutureCellular Systems

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Kristina Jersenius

LITH-ISY-EX--06/3905--SE

Handledare: David Törnqvistisy, Linköpings universitet

Eva EnglundEricsson Research

Examinator: Fredrik Gunnarssonisy, Linköpings universitet

Linköping, 12 January, 2007

Avdelning, InstitutionDivision, Department

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

DatumDate

2007-01-12

SpråkLanguage

¤ Svenska/Swedish¤ Engelska/English

¤

£

RapporttypReport category

¤ Licentiatavhandling¤ Examensarbete¤ C-uppsats¤ D-uppsats¤ Övrig rapport¤

£

URL för elektronisk versionhttp://www.control.isy.liu.sehttp://www.ep.liu.se/2006/3905

ISBN—

ISRNLITH-ISY-EX--06/3905--SE

Serietitel och serienummerTitle of series, numbering

ISSN—

TitelTitle

Kanalberoende upplänksschemaläggning för framtida cellulära systemUplink Channel Dependent Scheduling for Future Cellular Systems

FörfattareAuthor

Kristina Jersenius

SammanfattningAbstract

One goal in the development of future cellular systems is to increase perfor-mance. Channel dependent scheduling can possibly contribute to a performanceenhancement. It requires channel quality information and uplink channel knowl-edge is often incomplete. This master thesis work compares channel dependentscheduling and channel independent scheduling for a Single Carrier FrequencyDivision Multiple Access-based uplink in time domain and time and frequencydomain assuming continuous channel quality information updates. It also evalu-ates different methods for providing channel quality information by investigatinghow the limited channel knowledge they supply affects the performance of channeldependent scheduling.

Single-cell simulations with perfect channel knowledge indicate small gains forchannel dependent scheduling. Large gains are seen when performing frequencyand time domain scheduling instead of only time domain scheduling. Limited chan-nel knowledge causes performance loss for channel dependent scheduling. The per-formance is only slightly decreased if a method with sufficiently frequent providingof channel quality information updates is applied.

More realistic multi-cell simulations show large gains for channel dependentscheduling. It is possible that these results are influenced by link adaptationand scheduling problems due to non predictable interference when performing dy-namic scheduling. In the comparison between channel dependent and channelindependent scheduling the channel dependent scheduling can benefit from thefact that the selected channel dependent scheduling algorithms result in a morestatic scheduling than the selected channel independent scheduling algorithms do.

NyckelordKeywords channel dependent scheduling, uplink, SC-FDMA, channel quality information,

3GPP Long Term Evolution

Abstract

One goal in the development of future cellular systems is to increase perfor-mance. Channel dependent scheduling can possibly contribute to a performanceenhancement. It requires channel quality information and uplink channel knowl-edge is often incomplete. This master thesis work compares channel dependentscheduling and channel independent scheduling for a Single Carrier FrequencyDivision Multiple Access-based uplink in time domain and time and frequencydomain assuming continuous channel quality information updates. It also evalu-ates different methods for providing channel quality information by investigatinghow the limited channel knowledge they supply affects the performance of channeldependent scheduling.

Single-cell simulations with perfect channel knowledge indicate small gains forchannel dependent scheduling. Large gains are seen when performing frequencyand time domain scheduling instead of only time domain scheduling. Limitedchannel knowledge causes performance loss for channel dependent scheduling. Theperformance is only slightly decreased if a method with sufficiently frequent pro-viding of channel quality information updates is applied.

More realistic multi-cell simulations show large gains for channel dependentscheduling. It is possible that these results are influenced by link adaptationand scheduling problems due to non predictable interference when performingdynamic scheduling. In the comparison between channel dependent and channelindependent scheduling the channel dependent scheduling can benefit from thefact that the selected channel dependent scheduling algorithms result in a morestatic scheduling than the selected channel independent scheduling algorithms do.

v

Acknowledgments

I would like to thank all people working at Ericsson Research in Linköping fora great master thesis work experience. I am looking forward to continue workingwith you.

Most of all I thank my Ericsson supervisor Eva Englund for enthusiasticallydiscussing my work with me, answering my questions and giving me good advice.I have enjoyed our discussions and learnt a lot from them. Special thanks alsogo to Pål Frenger, Per Magnusson, Ke Wang Helmersson and Niclas Wiberg fordiscussions, explanations and help with the simulator.

My examiner Fredrik Gunnarsson and my university supervisor David Törn-qvist are thanked for showing interest in my work and making useful comments onmy report. Thanks also go to my opponent Johan Kjelsson for observant proof-reading and good comments which helped me to improve the report.

Finally, I thank my family and friends for supporting me.

Linköping, January 2007Kristina Jersenius

vii

Abbreviations

3GPP The Third Generation Partnership ProjectARQ Automatic Repeat RequestBLER Block Error RateCDF Cumulative Distributive FunctionCDMA Code Division Multiple AccessCDT Channel Dependent Time Domain SchedulingCDFT Channel Dependent Frequency and Time Domain SchedulingCQE Channel Quality EstimationCQI Channel Quality IndicatorCSMA Carrier Sense Multiple AccessDA Dynamic AssignmentDFT Discrete Fourier TransformE-UTRA Enhanced Universal Terrestrial Radio AccessFA Fixed AssignmentFDD Frequency Division DuplexFDMA Frequency Division Multiple AccessFFT Fast Fourier TransformFT Fair ThroughputGIR Gain to Interference RatioGSM Global System for Mobile CommunicationsGPRS General Packet Radio ServiceHARQ Hybrid Automatic Repeat RequestIFFT Inverse Fast Fourier TransformI-FDMA Interleaved SC-FDMAIP Internet ProtocolL-FDMA Localized SC-FDMALTE Long Term EvolutionMAC Multiple Access ControlMax-SIR Maximum Signal to Interference RatioOFDM Orthogonal Frequency Division MultiplexingPAPR Peak to Average Power RatioPF Proportional FairQoS Quality of ServiceRLC Radio Link ControllerRR Round RobinRRFT Round Robin Frequency and Time Domain Scheduling

ix

x

Td Coherence TimeRTDMA Random Time Division Multiple AccessRU Resource UnitSC-FDMA Single Carrier Frequency Division Multiple AccessSIR Signal to Interference RatioSTATFT Static Frequency and Time Domain SchedulingSTATT Static Time Domain SchedulingTCP Transmission Control ProtocolTDD Time Division DuplexTDMA Time Division Multiple AccessTTI Time Transmission Time Interval

Contents

1 Introduction 11.1 Problem Statement and Definition . . . . . . . . . . . . . . . . . . 21.2 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theoretical Background 52.1 Resource Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Fixed and Dynamic Assignment . . . . . . . . . . . . . . . 52.1.2 Channel Dependent and Independent Scheduling . . . . . . 7

2.2 The Long Term Evolution . . . . . . . . . . . . . . . . . . . . . . . 72.3 The LTE Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3.1 Scheduling and Access . . . . . . . . . . . . . . . . . . . . . 92.3.2 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . 112.3.3 Link Adaptation . . . . . . . . . . . . . . . . . . . . . . . . 122.3.4 HARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4 E-UTRA (Enhanced Universal Terrestrial Radio Access) . . . . . . 13

3 Channel Dependent Scheduling 153.1 Downlink Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.1.1 CQI Reports . . . . . . . . . . . . . . . . . . . . . . . . . . 153.1.2 Scheduling Algorithms . . . . . . . . . . . . . . . . . . . . . 163.1.3 Quality of Service Scheduling . . . . . . . . . . . . . . . . . 17

3.2 Uplink Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2.1 Uplink CQI . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.2 Scheduling Algorithms . . . . . . . . . . . . . . . . . . . . . 213.2.3 HARQ and Retransmission Effects . . . . . . . . . . . . . . 223.2.4 Link Adaptation and Interference Estimate Effects . . . . . 233.2.5 Time Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4 Uplink Scheduling Algorithms 254.1 No Channel Knowledge . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1.1 Time Domain Scheduling . . . . . . . . . . . . . . . . . . . 254.1.2 Frequency and Time Domain Scheduling . . . . . . . . . . . 26

4.2 Perfect Channel Knowledge . . . . . . . . . . . . . . . . . . . . . . 27

xi

4.2.1 Channel Dependent Time Domain Scheduling . . . . . . . . 284.2.2 Channel Dependent Frequency and Time Domain Scheduling 28

4.3 Incomplete Channel Knowledge . . . . . . . . . . . . . . . . . . . . 294.3.1 Channel Sounding . . . . . . . . . . . . . . . . . . . . . . . 294.3.2 Distributed Reference Signals . . . . . . . . . . . . . . . . . 30

4.4 Link Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.5 Synchronous HARQ . . . . . . . . . . . . . . . . . . . . . . . . . . 304.6 Transmit Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.7 Receiver Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.8 Expected Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5 Simulation Model 335.1 Cellular Network Model . . . . . . . . . . . . . . . . . . . . . . . . 335.2 Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.3 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.4 User and Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . 365.5 Simulation Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6 Simulation Results 376.1 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . 376.2 Perfect Channel Knowledge . . . . . . . . . . . . . . . . . . . . . . 38

6.2.1 Simulation Scenario A . . . . . . . . . . . . . . . . . . . . . 386.2.2 Simulation Scenario B . . . . . . . . . . . . . . . . . . . . . 44

6.3 Incomplete Channel Knowledge . . . . . . . . . . . . . . . . . . . . 536.3.1 Channel Sounding . . . . . . . . . . . . . . . . . . . . . . . 536.3.2 Distributed Reference Signals . . . . . . . . . . . . . . . . . 57

7 Conclusions 59

8 Further Studies 61

Bibliography 63

A OFDM and DFT-Spread-OFDM 65A.1 OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65A.2 DFT-Spread-OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . 66

B TDFT and Sampling Theorem 68

Chapter 1

Introduction

Over the last decades the mobile phone has gone from being more or less theequivalent of a walkie talkie to being a complex component being designed notonly for speech but also for web browsing, messaging, video conversations, etc. Atthe same time the mobile telephone systems have been developed from the firstgeneration systems to the third generation systems to support more demandingservices.

The 3rd Generation Partnership Project (3GPP) is currently developing newconcepts in order for the 3GPP radio access technologies to remain competitivein a long-term perspective. The objective with the Long Term Evolution (LTE)is to provide enhanced performance in terms of higher data rates, reduced delays,improved coverage (the percentage of a network service area which upholds arequired communication quality) and capacity (the maximum amount of data thatcan be transmitted over a channel).

In the 3GPP LTE concept the physical layer is based on Orthogonal FrequencyDivision Multiplexing (OFDM) for the downlink, the communication link frombase station to user terminal, and Single Carrier Frequency Division MultipleAccess (SC-FDMA) for the uplink, the communication link from user terminalto base station. This physical layer allows users to transmit on different partsof the spectrum without interfering with each other, which means that channeldependent scheduling, a resource assignment based on the channel quality of thesespectrum parts, can be performed. A scheduling of this kind can contribute toincreased total throughput, the total amount of data per time unit that is deliveredto all user terminals or all base stations in a network, in comparison to channelindependent scheduling. In the downlink Channel Quality Indicator (CQI) reports,based on measurements on the downlink reference signals performed by the userterminals, are used for the scheduling. In the uplink the base station estimatesthe uplink CQI information from uplink reference signals.

1

2 Introduction

1.1 Problem Statement and DefinitionThe assignment is to propose and evaluate some different methods for providingand using CQI information for SC-FDMA uplink channel dependent scheduling.The extreme cases where the scheduler uses no CQI information, channel indepen-dent scheduling, and where the scheduler uses perfect CQI information, channeldependent scheduling with complete CQI information, are examined. Both casesare compared to investigate whether there are any gains with channel dependentscheduling. If a gain is indicated it is considered how CQI information can beprovided and how limited CQI knowledge affects the performance of the channeldependent scheduling.

In this master thesis the scheduling strategies are focused on channel qualityknowledge. The implications of limited and delayed buffer knowledge are not in-cluded. Perfect knowledge of the user terminal transmit buffers is assumed. Otherscheduling aspects such as Quality of Service (QoS) are not considered. Intercellinterference management and link adaptation, which are linked to scheduling, arenot investigated in detail. The 3GPP LTE concept presents two versions of the SC-FDMA transmission scheme, one resulting in localized transmission and the otherin distributed transmission. This work only considers localized SC-FDMA. LTEshould support the duplex arrangements of Frequency Division Duplex (FDD),where uplink and downlink are separated in frequency, and Time Division Duplex(TDD), where uplink and downlink are separated in time. For FDD the availableCQI is limited to the one which can be derived from uplink reference signals. InTDD where the uplink and the downlink share the same frequency band it maybe possible to use the downlink CQI reports. This study examines only FDD.

1.2 Previous WorkChannel dependent scheduling for the OFDM-based downlink has been thoroughlyinvestigated and has shown performance gain. The SC-FDMA uplink channel de-pendent scheduling has not at all been examined to the same extent. There aremainly two factors which cause the uplink scheduling to be different from thedownlink one. Firstly, for the localized SC-FDMA-based uplink resource blocksthat are assigned to a user have to be contiguous in the frequency domain. Thisis not the case in the multi carrier downlink where a user can be assigned resourceblocks distributed over the entire bandwidth. In other words, downlink schedul-ing algorithms have to be modified. Secondly, there is the lack of uplink channelknowledge because users typically only transmit reference signals when they trans-mit data, due to power limitations, as opposed to the downlink where the basestation continuously transmits reference symbols. There are some 3GPP contri-butions that describe efforts that have been made to do uplink channel dependentscheduling and suggestions for how to obtain sufficient uplink CQI knowledge.

1.3 Method 3

1.3 MethodThe following steps are taken to solve the problem stated in chapter 1.1:

• Literature study on scheduling in general and SC-FDMA uplink channeldependent scheduling specifically

• Selecting and describing algorithms for uplink scheduling assuming perfectchannel knowledge and assuming no channel knowledge at all

• Dynamic simulations using a Java-based radio network simulator developedat Ericsson Research to estimate possible performance gains of channel de-pendent scheduling

• Selecting and describing methods for providing and utilizing incomplete CQIfor scheduling

• Dynamic simulations of scheduling with incomplete channel knowledge andanalysis of the results

1.4 Thesis OutlineChapter 2 contains a theoretical background describing 3GPP LTE, LTE uplinkdetails including SC-FDMA and resource assignment. The third chapter is focusedon downlink and uplink channel dependent scheduling.

In the fourth chapter the scheduling algorithms are explained in detail and theexpected outcomes of using the algorithms are discussed. The simulation modeland the selected simulation parameters are described in the fifth chapter. Chapter6 includes a presentation and an analysis of the simulation results.

The last two chapters contain conclusions and a discussion about further stud-ies.

4 Introduction

Chapter 2

Theoretical Background

This chapter introduces theory and concepts that are used in the remainder of thereport. It describes the LTE uplink in detail and explains the concepts of resourceassignment and channel dependent scheduling.

2.1 Resource AssignmentResource assignment or scheduling decides how resources, time slots and frequencychannels or subbands, are distributed among communicating stations, base sta-tions and user terminals, in a communication system.

2.1.1 Fixed and Dynamic AssignmentThere are two types of resource assignment: Fixed Assignment (FA) and DynamicAssignment (DA). In FA systems each communicating station or user is assigned itsown channel. A user is never allowed to use the channel of another user even if it isnot in use. For systems using FA there are high delays at low message arrival rateswhen the load of the system is low and there are many unused channels. On theother hand, there is a high maximum throughput at high arrival rates because ofefficient bandwidth utilization. In DA systems there are more users than channelsand channels are allocated to users who request to transmit. DA schemes show theopposite behavior of FA schemes. At low load the DA schemes result in low delayssince most of the bandwidth is utilized. At high load the maximum throughputis lower than the maximum throughput of FA schemes because there are channelsthat are used for resource assignment communication instead of data transmission.FA schemes are suitable for systems with continuous traffic while it is better toapply a DA scheme when the traffic is bursty.

Multiple access is a word for how a common resource is shared among mul-tiple users. Jamalipour, Wada and Yamazato, [13], state that the first multipleaccess communication systems used Frequency Division Multiple Access (FDMA),the division of the frequency band into frequency channels which are distributedamong the users. FDMA was followed by Time Division Multiple Access (TDMA),

5

6 Theoretical Background

the division of time into time slots which are assigned to users. The basic ver-sions of TDMA and FDMA are examples of FA schemes and they have sufficientperformance for voice services. However, as other services, such as web browsing,have been introduced and the number of users has increased DA schemes havebecome more common. Examples of possible DA schemes are the multiple accessschemes of 2G (Global System for Mobile Communications, GSM) and 2.5G (Gen-eral Packet Radio Service, GPRS), TDMA combined with FDMA, and of LTE,OFDM for the downlink and SC-FDMA for the uplink.

There are contention-based, non-scheduled, and scheduled DA schemes. For acontention-based scheme there is no central scheduler that tells a user when it isallowed to use a certain resource. It is up to the user to decide when and overwhat channel it transmits. For a scheduled DA scheme there is a central scheduler,typically situated in the base station, that distributes resources to users. If thescheduler is situated in the base station it is straightforward to use scheduled DAschemes in the downlink since the base station has full knowledge of how much datait wants to send. In the uplink it is more complicated since the buffer knowledgeis in the user terminals and has to be transmitted to the base station.

Contention-based Schemes

Simple contention-based DA schemes such as Random Time Division MultipleAccess (RTDMA) schemes have one channel. It means that every station has accessto the entire bandwidth when transmitting. It is up to every station to decidewhen it wants to transmit and this leads to a risk of conflict. A collision channelwith feedback is therefore introduced. The transmission attempt of a station isanswered by a feedback message telling the station if the attempt was successfulor not. The first RTDMA scheme, the ALOHA-Algorithm, was developed in the1960s according to Ahlin and Zander, [1]. In the ALOHA-Algorithm a stationtransmits its message as soon as it has one. If the transmission is successful itremoves the message from its queue of messages and transmits the next one ifthere is one. If there is a collision the station waits for a random time intervaland then tries to transmit the message again. The reason that the time intervalis selected randomly is that if the stations wait for an equal amount of time thereis a collision again.

Another contention-based DA scheme is Carrier Sense Multiple Access (CSMA)in which the stations measure signal level to detect transmission. If a station isabout to transmit a message and it senses that another station is transmittingit postpones its transmission for some time. For a non-persistent CSMA schemethis time interval is selected randomly and is followed by a new sensing for othertransmissions. In a persistent CSMA scheme the station waits until the end of theongoing transmission of the other station and then transmits its own message.

Scheduled Schemes

More complex methods to resolve conflicts, Conflict Resolution Algorithms, thanthe primitive ALOHA and CSMA methods have been developed. There are alsoscheduled schemes that avoid collisions by using reservation packets. During a

2.2 The Long Term Evolution 7

reservation phase reservation packets containing station/user ID, pending messagesand message size are sent using a simple contention-based assignment scheme suchas ALOHA. Then a central controller, a scheduler, receives the reservation packetsand decides which users should be allowed to send using what resources. Thedecision of the scheduler is sent forward to the users and a collision-free data phasefollows. These reservation schemes are the basis of current resource allocation orscheduling methods. An alternative to using a contention-based DA scheme duringthe reservation phase in the systems of today is using an FA scheme.

2.1.2 Channel Dependent and Independent SchedulingThe main task of a scheduler is to distribute resources to the users. For an OFDMor SC-FDMA based scheduler the resources are time-frequency intervals consistingof a certain frequency and time amount. If the scheduling is performed in thetime domain all resources are given to one user each scheduling time interval. Ascheduling in frequency and time domain means that every scheduling time intervalseveral users are allocated resources.

A channel independent scheduler does not consider channel quality. A typicalexample is the Round Robin (RR) scheduler which every scheduling time intervalassigns all resources to the user which has waited the largest amount of time totransmit.

When a scheduler bases its resource assignment on channel conditions it iscalled channel dependent scheduling. Kwok, Yu-Kwong and V.K.N, [14], mentionthat the reason to do channel dependent scheduling is that a bad channel state givesa low throughput. There is channel dependent time domain scheduling (CDT),where one user with good overall channel conditions is given the entire frequencyband every scheduling time interval, and channel dependent frequency and timedomain scheduling (CDFT), where several users are allocated frequency domainsubbands with good quality every scheduling time interval. For mobile users orusers in a mobile environment the channel quality varies with frequency and time.A multiuser diversity gain can be obtained if resources are allocated to the userterminals with the best channel quality.

2.2 The Long Term EvolutionLTE is as mentioned in chapter 1 the 3GPP evolution of 3G. The 3GPP re-quirements for LTE described by Ekstrom, Furuskar, Karlsson, Meyer, Parkvall,Torsner and Wahlqvist, [6], are among others:

• Instantaneous peak-data rates of 100 Mbit/s in downlink and 50 Mbit/s inuplink

• Improved user average throughput and improved throughput for cell-edgeusers

• Improved spectrum efficiency


• Spectrum flexibility in both uplink and downlink

• Handover to and from 3G and 2G systems

Spectrum flexibility includes the possibility to use the LTE-technology in spec-trum allocations of different sizes, from less than 5MHz up to 20 MHz, and thesupport of operation in both paired and unpaired spectrum. There are three pos-sible duplex arrangements: FDD, TDD and combined FDD/TDD, see Figure 2.1.

Figure 2.1. Duplex arrangements.

Most of the 3G concepts are based on Code Division Multiple Access (CDMA).In CDMA narrowband user information is spread into a wider spectrum. Eachuser is assigned its own code and all users can therefore use the entire frequencydomain. OFDM for the downlink and SC-FDMA for the uplink are the multipleaccess schemes chosen for the 3G LTE partly because of their greater spectrumflexibility with possibility of wideband transmission bandwidths and the possibilityof performing frequency domain link adaptation and scheduling.

It is important to note that the LTE uplink details in this report are 3GPPworking assumptions and not specifications.

2.3 The LTE UplinkThe uplink is based on single-carrier TDMA combined with FDMA, SC-FDMA.SC-FDMA offers a low peak to average power ratio (PAPR) as opposed to OFDMwhich is used in the downlink. The low PAPR of the SC-FDMA uplink gives a

2.3 The LTE Uplink 9

power-efficient user to base station transmission and reduced mobile power con-sumption. The basis of the transmission scheme is DFT-Spread-OFDM, whichcan be seen as pre-coded OFDM where DFT plus possible spectrum shaping hasbeen added to the original OFDM-scheme. 3GPP suggests two different types ofSC-FDMA, L-FDMA (Localized SC-FDMA) resulting in localized transmissionand I-FDMA (Interleaved SC-FDMA) resulting in distributed transmission. InL-FDMA a user is assigned consecutive subcarriers and in I-FDMA distributedequidistant subcarriers. I-FDMA has a lower PAPR than L-FDMA according toLim, Hyung, Kyungjin and Goodman, [16]. See appendix A for more informationabout OFDM, DFT-Spread-OFDM and subcarriers.

A resource unit (RU) is the smallest frequency-time resource block of the up-link. It is 1 ms, a transmission time interval (TTI), in time and 180 kHz, 1 subbandor 12 subcarriers times a subcarrier space of 15 kHz, in frequency. In each TTIthere are two subframes. Each 0.5 ms subframe consists of six long blocks used fordata and two short blocks used for reference symbols. An illustration of an RU canbe seen in Figure 2.2. A cyclic prefix is inserted between the blocks. The purposeof the cyclic prefix is to enable efficient frequency-domain equalization, to reducethe impact of intersymbol interference, at the receiver side. Figure 2.3 shows theplacement of an RU in the time and frequency domain with a total bandwidth of100 subbands.

The two short blocks can be used for different purposes, demodulation/detectionor uplink channel quality estimation (CQE). According to the 3GPP TechnicalSpecification Report, [17], the uplink reference signal structure should allow forlocalized reference signals and distributed reference signals. Transmission of refer-ence signals may be achieved by using frequency division multiplexing or code divi-sion multiplexing of the reference signals of different users. Uplink CQE referencesignals may occupy at least partly different spectrum than the data transmission.There are several possible alternative uplink reference structures for L-FDMA: onewhere all reference signals are localized, one where there are localized referencesignals in one short block and distributed reference signals in the other and onewhere the transmission of localized reference signals every now and then is inter-rupted by a period where all users transmit distributed reference signals. The lastalternative is called channel sounding.

2.3.1 Scheduling and AccessAmobile cannot start transmitting whenever it wants to. It has to ask for resourcesand a permission to transmit. This request is sent contention-based or using FA tothe base station. The initial scheduling request includes the user terminal identityand possibly other scheduling information such as buffer status, priority, etc. Inthe base station the scheduler takes care of the mobile request and responds witha resource assignment. The resource assignment includes which RUs the user canuse for its transmission. Orthogonal user signals are provided since an RU cannotbe assigned to more than one user. The scheduling is typically performed per cell,a cellular communication network consists of cells, each TTI. See chapter 5.1 fora more detailed explanation of cell and cellular network.


Figure 2.2. A resource unit.

There are two types of random access procedure for uplink users: a non-synchronized random access procedure for a user terminal which is not uplinksynchronized and a simplified procedure, synchronized random access procedure,for a user terminal which is time-aligned. The non-synchronized procedure includesthe transmission of a preamble, which is sent to obtain uplink synchronization. Anon-synchronized user terminal which wishes to transmit synchronizes to downlinktransmissions and reads the Broadcast Control Channel, a downlink channel forbroadcasting control information, to know when and at which frequency band itcan send its preamble. The base station processes the preamble and answers theuser terminal with a time alignment and scheduled resources or a time alignmentand a resource allocation on which to send a scheduling request. A contention-based synchronized random access can be done on a regular basis, e.g. everysecond subframe.

Uplink scheduling may be based on such factors as having different priorities

2.3 The LTE Uplink 11

Figure 2.3. The placement of an RU in the time and frequency domain.

for user data of different QoS classes, user terminal buffers sizes, retransmissionswaiting to be done and uplink CQI. Uplink scheduling is complicated since thescheduler, which is situated in the base station, does not automatically know whatthe users want to send, how much they want to send and what their channel qualityis. There is a possibility of semi static scheduling, which means that the schedulerdetermines a scheduling that is valid for certain sequences of TTIs instead of onlyone TTI or that the scheduler sends a scheduling pattern that is valid for severalTTIs.

2.3.2 Power ControlPower control is applied to compensate for differences in path gain, the receivedtransmission power over the transmitted transmission power, between differentusers. Its task is to control the transmit power of simultaneously transmittingusers to prevent that the base station experiences a large difference in receivedpower between different users. Under perfect conditions this should not have to bedone since the orthogonality of the user signals eliminates intra-cell interference. Inthe reality there is some interference because of imperfect transmitter and receiverimplementations. Two alternative power control schemes are that the base station


measures the received power level and sends power control commands to the userterminals or that the user terminals measure downlink signal strength and controltheir own power.

2.3.3 Link AdaptationThe link adaptation consists of that the base station measures the uplink channelquality in order to decide modulation schemes, code block lengths and codingrates.

2.3.4 HARQThe uplink Hybrid Automatic Repeat Request (HARQ) is based on an N-channelStop-and-Wait protocol. In an Automatic Repeat Request (ARQ) system trans-mission errors are detected and retransmissions are requested. In a HARQ systemthe ARQ is combined with error correction which means that some errors are notonly detected but also corrected using error correcting codes. There are differenttypes of HARQ systems. Type I HARQ tries to correct transmission errors andasks for a retransmission if the correction attempt fails. Type II HARQ does notonly attempt to correct erroneous received blocks, but also combines retransmittedreceived blocks with received blocks from earlier transmission attempts to enhancethe probability of a successful retransmission reception. A Stop-and-Wait proto-col means that one block at a time is sent over the channel. This transmissionis followed by an ACK leading to a new transmission or a NACK leading to aretransmission. New transmissions are blocked while waiting for an ACK/NACK.The N-channel Stop-and-Wait protocol makes transmissions more efficient by al-lowing N simultaneous HARQ processes so that new data can be transmitted whilewaiting for ACK/NACK.

HARQ processes can be either synchronous or asynchronous. If the HARQis synchronous the retransmissions of the HARQ processes must occur at knowntime instants. This affects the behavior of the scheduler since the scheduler has tomake resource reservations for these retransmissions at the correct time instants.The synchronous HARQ can be either adaptive or non-adaptive. Non-adaptiveHARQ means that the retransmission must have the same exact transmission for-mat (number of assigned bits, code rate and modulation scheme) and frequencyallocation as at the first transmission attempt. For the adaptive HARQ the fre-quency allocation can be different. For asynchronous HARQ the timing of theretransmissions is not limited to certain time instants, but since retransmissionsshould be transmitted within a reasonable time the scheduler often prioritizesretransmissions. The advantage of the synchronous non-adaptive HARQ in com-parison to the adaptive synchronous and asynchronous HARQ is the reduction ofdownlink signalling since the base station does not have to send a new frequencyallocation to the user terminal.

The uplink HARQ is likely to be synchronous. It has not been finally decidedwhether it will be adaptive or nonadaptive or if both should be supported.

2.4 E-UTRA (Enhanced Universal Terrestrial Radio Access) 13

2.4 E-UTRA (Enhanced Universal Terrestrial Ra-dio Access)

UTRA, the air interface of a 3G network consists of three layers: the physicallayer, the data layer and the network layer. The data layer is divided into twolayers: the Radio Link Controller (RLC) and the MAC. The RLC layer offersservices (Radio Bearers) to higher layers and provides ARQ retransmission ser-vices. The MAC layer offers services to the RLC layer. The tasks of the MAClayer include mapping the logical channels to transport channels, link adaptation,scheduling and providing the HARQ protocol. The logical channels are betweenRLC and MAC and the transport channels are between the MAC layer and thephysical layer. Higher layers for web traffic is the Internet Protocol (IP) and theTransmission Control Protocol (TCP). The UTRA layers and channels and theirconnections are shown in Figure 2.4.

Figure 2.4. The layers of E-UTRA.

E-UTRA, the air interface of an evolved 3G network (a LTE network), includesa new physical layer. The new physical layer is based on OFDM for the downlinkand SC-FDMA for the uplink.


Chapter 3

Channel DependentScheduling

Channel dependent downlink scheduling has shown promising results. Channeldependent scheduling of the SC-FDMA uplink is different from the channel depen-dent scheduling of the OFDM downlink. This chapter describes channel dependentscheduling for both downlink and uplink. It explains why the uplink scheduling ismore challenging and contains suggestions from previous work for solutions to theproblem of deriving channel knowledge.

3.1 Downlink SchedulingThe channel dependent scheduler bases its decisions on CQI reports. The OFDMtransmission scheme allows a nonconsecutive assignment of RUs to users, see Fig-ure 3.1.

3.1.1 CQI ReportsPeriodically the user terminals provide CQI reports based on measurements of thegain-to-interference ratios (GIR) on downlink reference signals of known power.These CQI reports are transmitted on uplink control channels to the schedulersituated in the base station. The CQI report of a user may consist of one GIRmeasurement value per every subband. A higher GIR indicates a better subband.The power signal gain, G, multiplied with the transmitting power, P, of the in-tended base station results in the received power of the user. The signal power ischanged when a signal travels from transmitting to receiving antenna because ofdistance attenuation, shadow fading, multipath fading and antenna gain (see chap-ter 5.2). The interference includes interfering signals, I, from other transmittingbase stations. I is defined as the total received power, Itot, from all base stationssubtracted by the received power from the intended base station,

15

16 Channel Dependent Scheduling

Figure 3.1. Downlink scheduling.

I = Itot − P ·G. (3.1)

GIR is defined as signal power gain over interference and noise, N,

GIR =G

I + N. (3.2)

The CQI report describes the channel quality experienced by the user in thelatest measurement. This is an indication of the channel quality in the next TTI,but for example the interference might have changed somewhat in the next TTIcausing the estimated CQI to be different from the true one.

If each GIR value component of a user CQI report is multiplied with the powerassigned to the subband corresponding to that component it results in a signal-to-interference ratio (SIR) value per every subband,

SIR =P ·GI + N

. (3.3)

3.1.2 Scheduling AlgorithmsThere are different downlink scheduling algorithms known from previous work: e.g.Maximum Signal to Interference Ratio (Max-SIR), Proportional Fair (PF) and FairThroughput (FT). Max-SIR is an algorithm that maximizes total SIR every TTI.An algorithm that maximizes some measure of performance, e.g. total throughput,tends to make a scheduling that favors users with good channel conditions. Max-SIR is an example of such an algorithm. The algorithm is not fair to all users and

3.2 Uplink Scheduling 17

some users have much lower user throughput than others. PF and FT considerfairness. FT strives after throughput fairness, i.e. equal throughput for all users,by assigning the user with the lowest throughput the RU with the highest SIR. Thegoal of PF is to schedule users when their channel conditions are good comparedto their average ones. A certain RU is assigned to the user who for that particularRU has the maximum RU-user specific throughput over average throughput. Anadaptation of the PF algorithm is the Exponential Rule which acts as a PF as longas there are tolerable delays for all users, but prioritizes users with large delaysas soon as there are any. There are also schedulers specifically designed for VoIPwhere delays also are of a larger importance.

It should be noted that finding the optimal scheduling according to some crite-rion , e.g. maximizing SIR, is a complex optimization problem. In the algorithmsmentioned above sub-optimal scheduling is used to lower the complexity of theproblem. The sub-optimal scheduling is based on the strategy of choosing theuser-RU pair which has the largest criterion value and assigning the RU to theuser. After that the next user-RU pair is considered in the same manner and soon until all RUs have been assigned. The complexity of the scheduling can beeven further reduced by assigning every resource block its best user or turning itaround and assigning every user its best resource.

3.1.3 Quality of Service SchedulingThe question of QoS can also be taken into consideration. QoS means that differenttraffic flows are divided into different QoS classes (e.g. web traffic, VoIP etc) whichhave different performance demands and are of different importance. One user canhave many different traffic flows. It is the task of the scheduler to maximize qualitywithin a QoS class while differentiating quality between different QoS classes.Laneri, [15], and Zhang, He and Chong, [22], describe efforts that have been madeto develop algorithms that give a good overall performance at the same time asthey are fair to the users and consider QoS differences. The algorithms start byassigning resource blocks to the QoS class with the highest priority and apply afairness algorithm if necessary and then go on like that for all QoS classes. Theoptimization algorithms including fairness or not might look different for differentQoS classes. The reason for doing QoS scheduling is that there can be users whohave bad channel conditions but high-priority data to transmit.

3.2 Uplink SchedulingThere are three main reasons why uplink channel dependent scheduling is differentfrom downlink channel dependent scheduling in an FDD framework:

• The difference between the reference signals in the downlink and the uplinkis that in the downlink the base station transmits reference symbols con-tinuously whereas in the uplink the mobiles only transmit reference signalswhen they transmit data. This is partly because the uplink is power-limitedand mobile batteries have a hard time surviving continuous transmission of


reference signals and partly because a continuous transmission of referencesignals from all users occupy resources that can be used for data transmission.The consequence is that there is not as much channel quality informationavailable for the uplink as for the downlink.

• The downlink scheduling algorithms have to be adjusted to fit the uplinksince users can only be assigned RUs being contiguous in the frequencydomain, see Figure 3.2. This extra condition added to the optimizationproblem makes it complex.

• In the downlink there is full buffer knowledge since the scheduler situatedin the base station knows exactly what the base station wishes to transmit.In the uplink the buffer knowledge exits in the user terminals and has to betransmitted to the base station.

Figure 3.2. Uplink scheduling.

The channel dependent scheduling in the TDD framework can seem easierbecause the uplink and the downlink sharing the same bandwidth makes it possibleto predict the uplink CQI from measurements of the downlink CQI. However,it has to be taken into consideration that uplink interference is different fromdownlink interference. Another observation is that the interruption of downlinkreference signal transmission by uplink transmission periods reduces the accuracyof downlink CQI estimation and uplink CQI prediction.

As QualComm, [7], mentions the gains of uplink channel dependent schedulingcan probably never succeed the ones of downlink channel dependent schedulingdue to the lack of complete channel knowledge. The costs of channel dependentscheduling in forms of signalling, parts of the bandwidth and time that could havebeen used for transmitting data are used for transmitting reference signals, must


also be taken into consideration when deciding if channel dependent scheduling isworth adopting.

3.2.1 Uplink CQIFor uplink CQI the CQI GIR value of a subband for a user is obtained by the basestation by deriving the subband gain of the user, G, and the subband gain of thescheduled user, Gsch, from uplink reference signals. The scheduled user is situatedin the same cell as the user and is the user who used the subband for transmissionin the previous TTI. The signal power the base station receives from the cell wherethe user is located is calculated by multiplying Gsch with the transmission powerof the scheduled user, Psch. Psch is known by the base station if it were assigned tothe scheduled user by the base station in the previous TTI. The uplink interferenceper subband, I, is estimated by subtracting the received signal power from the cellof the user, Gsch · Psch, from the total received signal power from the users in allother cells , Itot,

I = Itot −Gsch · Psch. (3.4)

The uplink GIR has the same definition as downlink GIR,

GIR =G

I + N. (3.5)

If it is assumed that user transmissions are orthogonal, an RU cannot be usedby more than one user for transmission, the base station experienced the sameinterference regardless of which one of the users of the own cell the base stationscheduler scheduled for transmission in the previous TTI. The assumption that theinterference is the same as in the previous TTI prerequisites that the interferenceis approximately the same regardless of which users the neighboring cell schedulersschedule for transmission or that the neighboring cell schedulers schedule the sameusers as in the previous TTI. This is not probable. It makes a difference if aneighboring cell scheduler schedules a cell edge user or a user standing close tothe base station and a dynamic scheduler varies its scheduling over time andpossibly also frequency. The interference estimate should therefore have to beimproved, e.g. by filtering it in the time domain or the frequency domain orboth. An alternative to filtering interference is to reduce interference variationsby controlling the intercell interference.

As mentioned above there are no continuous reference signals from all mobiles.The implication of only transmitting reference signals when transmitting data isthat there is no updated CQI information for users who were not scheduled fortransmission in the previous scheduling time period. According to Parkvall, [20],there could be a possibility of using uplink reference signals transmitted for otherreasons, e.g. the reference signals provided when the long blocks are used forcontrol signaling, CQI report transmissions and ACK/NACK transmissions. Fornew users who have never been transmitting CQI could perhaps be derived fromresource requests or preambles. It may be possible to calculate the average gain,


the gain excluding multipath fading, over the entire bandwidth from downlinkCQI reports.

Three possible reference signal structures are a localized reference signal struc-ture, a distributed reference signal structure and channel sounding. In the local-ized reference signal structure users who transmit data transmit reference signalson the part of the bandwidth where they transmit data. In the distributed ref-erence signal structure users who transmit data transmit reference signals overthe entire bandwidth. Channel sounding means that the transmission of local-ized reference signals is regularly interrupted by a period where all users transmitdistributed reference signals. Common for the distributed and localized referencesignal structures is that there is no CQI available for new users who have not beguntransmission unless it is assumed that it is possible to derive CQI from schedulingrequests, preambles or reference signals that have been transmitted when trans-mitting control signals. The CQI is only updated every scheduling time periodfor the users who are transmitting that time period. For channel sounding thechannel information is updated for all users when there is a channel sounding timeperiod.

For a distributed reference signal structure the channel gain information usedfor users who were not transmitting in the previous scheduling time period is fromtheir latest transmission. This CQI delay can affect the behavior of the schedulingif the true channel quality is very different from the delayed one.

If the reference signals are narrowbanded updated channel gain informationis not only restricted to the users currently transmitting, but also to the partof the bandwidth on which the users are currently transmitting. This limits thepossibility of doing channel dependent scheduling in frequency and time domain.Perhaps an algorithm including frequency-hopping, the frequency allocation ofthe user is changed over time according to a pre-determined pattern, can be usedinstead. In this way the user has collected wideband CQI information after awhile. Users who are not transmitting data are transmitting control signals andif frequency-hopping is used for control signalling as well these users also haveCQI covering the entire bandwidth after a certain period of time. For a timedomain scheduler a localized reference signal structure is equal to a distributedreference signal structure since the user transmission bandwidth is equal to thetotal bandwidth.

If the channel sounding reference structure is applied narrowband referencesignal periods are interrupted by wideband reference signal periods. The widebandtransmission reference signal periods make it possible to estimate CQI for the entirebandwidth for all users.

In a lot of the previous work done on uplink scheduling, e.g. in [16], the lackof channel knowledge is not dealt with. The work is concentrated on differentscheduling algorithms and assumes instantaneous knowledge of the uplink channelconditions. There are however some suggestions about how to solve the problemof deriving CQI.

QualComm Europe, [8], suggests several different methods for obtaining CQI.They propose that one long block could be used for wideband CQI estimationinstead of one of the short blocks making both short blocks available for detec-


tion/modulation. Another option is an alternative to channel sounding whereone narrowband reference signal is sent for each subband periodically every T msmaking the channel knowledge complete after N · T ms if there is a total of Nsubbands.

The reference signal structures of NTT DoCoMo, [4], [3], [19] and [5], arecombined with total scheduling solutions. Different channel dependent schedulingmethods are considered. In one of them the transmission bandwith is pre-assignedto each user based on the data rate of each user and traffic distribution of eachcell. The transmission bandwidths of all users are of the same size and do notvary with CQI. If there are several users that have been pre-assigned the samepart of the frequency domain the one with the best channel quality is assignedthat part. This method is combined with a localized reference signal structure.Another method divides the frequency domain into multiple subbands and allowsadaptive transmission bandwidth, i.e. the bandwidth varies with channel quality.On trial it applies a downlink channel dependent scheduling algorithm and resortsthe subbands using a priority based ranking system if the first trial assignment isnonconsecutive. This method requires wideband reference signals for all users.

The drawbacks of wideband reference signal transmission are listed in the con-tributions [3] and [19]. It fills up the bandwidth with reference signalling, increasesthe user power consumption and gives increased CQI measurement error due toreduced signal power density e.g. for cell-edge users. Therefore it is proposedthat also the reference signal transmission bandwidth is adaptive. First transmis-sion bandwith for each user is decided based on traffic size or data rate. Thenthe reference signal transmission bandwidth is determined based on transmissionbandwidth, amount of traffic and transmission power limit. The reference signalsare transmitted and based upon the CQI derived from them frequency domainparts are assigned to users in a consecutive way. To avoid CQI measurement er-rors for cell edge users contribution [5] proposes that path loss between user andbase station should be considered when deciding the reference signal transmissionbandwidth.

3.2.2 Scheduling AlgorithmsThe downlink scheduling algorithms mentioned in chapter 3.1.2 have to be mod-ified since uplink RUs have to be assigned consecutively. The consecutive assign-ment makes algorithms involving frequency multiplexing of users complex. Toreduce the complexity of the uplink scheduling algorithms RUs can be grouped to-gether in frequency to form larger resource blocks to distribute among users. Lim,Hyung, Kyungjin and Goodman, [16], propose an approach for the SC-FDMAuplink where there are as many resource blocks as users and at every TTI eachuser is allocated a maximum of one resource block.

Nokia, [18], proposes a CDFT scheduling where the frequency domain is di-vided into four parts that are assigned to different users consecutively. The per-formance of this scheduling method is compared to a CDFT scheduling allow-ing nonconsecutive assignment and both total throughput and the 5th percentileuser throughput show that the consecutive assignment condition limits the perfor-


mance. The simulations of NTT DoCoMo, [4], indicate that channel dependentscheduling with adaptive transmission bandwidth performs better than channeldependent scheduling with fixed transmission bandwidth, at least for small fixedtransmission bandwidths. This comes at the cost of a more complicated algorithm.

It can be discussed if the channel dependent scheduling should be performedin the time domain or in the frequency and time domain. Ekstrom, Furuskar,Karlsson, Meyer, Parkvall, Torsner and Wahlqvist, [6], recommend that primar-ily time domain scheduling is used, but for terminals with limited power, limitedamount of data to transmit or both time and frequency domain scheduling is used.Time domain scheduling can potentially lead to inefficient bandwidth utilizationin these cases. The user does not use all the bandwidth it has been assigned andif frequency and time domain scheduling were used instead it would be possible toassign the unused bandwidth to other users. Frequency and time domain schedul-ing algorithms are typically more complicated than time domain algorithms andfrequency and time domain scheduling requires more downlink signalling since thebase station has to signal frequency allocations to the users.

3.2.3 HARQ and Retransmission EffectsSynchronous HARQ has effects on the behavior of the scheduler. If a user haspending retransmissions it has to be assigned RUs regardless if it has good chan-nel quality or not. For adaptive HARQ it is enough that the user is assignedany frequency allocation. If the HARQ is nonadaptive however, the user has tobe assigned exactly the same RUs as in the previous transmission attempt. Bothcases limit the decision possibilities of the scheduler, especially the nonadaptivecase since it leads to fragmentation of the frequency band. It is difficult to doa consecutive channel dependent time and frequency domain assignment of re-sources to users in a frequency band where parts here and there are occupied byretransmissions.

To avoid having a HARQ controlled scheduling it is important to keep thenumber of retransmissions low. This can be measured by the block error rate(BLER) which is defined as the number of incorrectly received blocks over thetotal number of received blocks,

BLER =Incorrectly received blocks

total number of received blocks . (3.6)

The link adaptation often decides modulation and code rate based on esti-mated SIR. According to Ruberg, [21], the BLER can be held at a selected targetby using a SIR backoff. The SIR backoff decreases the value of estimated SIR. Anoverestimation of SIR causes a faulty link adaptation resulting in retransmissionsdue to difficulties of decoding at the receiver side. The SIR backoff factor can bestatic or dynamically adapted to the number of correctly and incorrectly receivedblocks. A code rate limit that is less than 1 also decreases the number of retrans-missions. If the code rate is close to 1 there is almost no coding and that alsocomplicates the decoding at the receiver side.


3.2.4 Link Adaptation and Interference Estimate EffectsLink adaptation and scheduling are linked. Based on the quality of the resourcea user has been assigned the link adaptation decides how many bits the user cantransmit. If a user is assigned a high quality resource it is assigned many bits.For the link adaptation it is important that the estimated CQI of the resource isclose to the true channel quality of the resource. If the estimated CQI is too highthe link adaptation assigns more bits to the user than the receiver can manage tocorrectly receive and if it is too low the link adaptation assigns fewer bits to theuser than it is possible for the receiver to receive.

The interference estimate described in chapter 3.2.1 affects the decision of thelink adaptation. If the estimated interference is very different from the true oneit causes the link adaptation to make incorrect modulation and coding rate selec-tions. This incorrect link adaptation can make it impossible for the receiver todecode the received information and it sends a NACK resulting in a retransmis-sion. A lot of retransmissions result in large delays and low throughput. Theseretransmissions are often caused by an overestimated channel quality, in this casean underestimation of the interference. The extra assigned bits which an overes-timated channel quality causes can compensate for a retransmission or two everynow and then and result in a throughput which is the same if not even higher thanit would have been if there were no retransmissions, but many retransmissionscause a cell throughput loss. For a type II HARQ every retransmitted block iscombined with earlier received block and eventually it is possible to decode theblock. If the channel quality is largely overestimated, a lot of retransmissions arerequired to enable a successful reception of the transmitted block. The retrans-missions of a HARQ process are typically only allowed for a certain time and afterthat the user has to start all over again. If this occurs often it gives rise to largedelays. To underestimate the channel quality, in this case to overestimate the in-terference, also causes throughput loss since the link adaptation assigns fewer bitsthan it would have done if the channel quality estimation had been correct.

The channel dependent scheduling is also affected by the erroneous interferenceestimate. If the CQI input to the scheduler is incorrect the scheduler makesincorrect scheduling decisions.

3.2.5 Time AspectsIt is good if scheduling is done as often as possible since it is unnecessary to scheduleusers who have emptied their send buffers. However, it can be discussed how oftenthe channel quality should be updated, is it every TTI or more seldom? The trueCQI can be very different from the delayed one if CQI is updated infrequentlyand this affects the performance of the scheduling algorithm. The schedulingdecisions become almost random if the CQI is updated more seldom than thechannel changes. At the same time it is unnecessary to update the CQI fasterthan the channel changes. This can be worth thinking of when deciding how oftenthere should be channel sounding periods in the uplink channel sounding referencesignal structure. How often the channel gain needs to be updated can be found bysampling the channel gain with the smallest possible sample period, the scheduling


time period, and then investigate, e.g. utilizing the sample theorem, see appendixB, how much it is possible to reduce the sample period without changing thefrequency content and appearance of the channel gain substantially. It should bekept in mind that the rate of change of the channel gain depends on the velocity ofthe user terminals. If channel sounding is done often enough the channel soundingreference signal structure could possibly provide CQI with sufficient accuracy forchannel dependent scheduling.

Chapter 4

Uplink SchedulingAlgorithms

This chapter describes the uplink scheduling algorithms that are used in the sim-ulations. In the chapter it is stated what channel knowledge is used by the linkadaptation in the different channel knowledge cases, how the algorithms are mod-ified in case of pending retransmissions and how the power is allocated. Expectedoutcome of using the different algorithms is also included.

4.1 No Channel Knowledge4.1.1 Time Domain SchedulingWhen there is a total lack of channel knowledge a time domain scheduler of aRound Robin type is applied. It assigns all RUs to the user who has waited thelongest to transmit among the active users, i.e. users with data to transmit.

RR algorithm:

• Begin

1. Check which user that has waited the longest for allowance to transmitand assign all RUs to this user

• End

An alternative channel independent time domain scheduler is a static timedomain scheduler (STATT). It selects the user who has waited the longest forallowance to transmit and allows it to every TTI use all resources until it leavesthe cell. When it leaves the cell a new user is selected.

STATT algorithm:

The user which is selected to transmit until it leaves the cell is called users.

25

26 Uplink Scheduling Algorithms

• Begin

1. If users has left the cell- Check which user has waited the longest for allowance to transmit,assign all RUs to this user and make it into users

2. If users has not left the cell- Assign all RUs to users

• End

4.1.2 Frequency and Time Domain SchedulingAn alternative channel independent scheduler is a time and frequency domainscheduler which every scheduling time period divides the frequency domain intoresource unit groups (RUgroups), consisting of the same number of consecutiveresource units, and assigns one group to every active user until there are no groupsleft. The number of groups can be equal to the number of active users as longas the number of active users does not exceed the total number of resource units.It is also possible to select a limit, L, to the number of groups which is smallerthan the number of active users. If such a limit is selected the scheduler works asa Round Robin scheduler with frequency multiplexing of users. This means thatthe L users which has waited the longest for allowance to transmit are assignedone RUgroup each every scheduling time period.

Round Robin Frequency and Time domain scheduler (RRFT) algorithm:

The number of active users is N and the number of available resource units isNr. The number of RUgroups is RUgroupsize and the RUgroup index is j. Thereare bNr

N c RUs per every RUgroup. The maximum number of RUgroups is L.

• Begin

1. If N <= L- RUgroupsize = N

2. If N > L- RUgroupsize = L

3. For j = 1 to RUgroupsize- Allocate RUgroup j to unassigned user with maximum transmissionwaiting time

• End

The fact that there are bNr

N c RUs per every RU group is important whenselecting L. If L e.g. is set to 51 and Nr is 100 there are 49 unused subbands if Nis equal to 51. This makes the algorithm very bandwidth inefficient. If L is 5 thereis a maximum of one unused subband if N is equal to 3 and if L is 10 there are 0-4

4.2 Perfect Channel Knowledge 27

unused subband depending on N. In this study there are 100 subbands and theselected limit is 10. Other ways of avoiding the bandwidth inefficiency would beto allow the users to be assigned different numbers of subbands or to only allowthe RUgroupsize to be equal to numbers for which Nr is divisible.

A static alternative to the RRFT scheduler is the static frequency and timedomain scheduler (STATFT) which from the start selects the L users who havewaited the longest for allowance to transmit (or the number of users in the cellif the number of users in the cell is less than L) and assign them one RUgroupeach. The L RUgroups are reserved for the selected L users until one of themleaves the cell. Then the L-1 remaining users and a new user, selected based onits assignment delay, are assigned L RUgroups. In this master thesis work L is setto 10.

STATFT algorithm:

The number of users is N and the number of available resource units is Nr.The number of RUgroups is RUgroupsize and the RUgroup index is j. There arebNr

N c RUs per every RUgroup. The maximum number of RUgroups is L and thegroup of L selected users is called users.

• Begin

1. If one of the users in users has left the cell

- If N <= L- RUgroupsize = N

– If N > L- RUgroupsize = L

– Find user with maximum transmission waiting time and make it apart of users

- For j = 1 to RUgroupsize- Allocate RUgroup j to unassigned user in users

2. If no one of the users in users has left the cell

- Allocate each user in users its resource

• End

4.2 Perfect Channel KnowledgeWhen channel knowledge is available channel dependent scheduling is performed.Perfect channel knowledge means that the channel gain values are updated for allusers and all subbands every scheduling time interval.


4.2.1 Channel Dependent Time Domain SchedulingThe following algorithm uses CDT scheduling. Every scheduling time interval theactive user that has the largest average GIR is assigned all resource units.

CDT algorithm:

The user index is i and the RU index is k. The number of RUs is Nr.

• Begin

1. Get GIR vectors, for all users, containing GIR values per every RU:

(GIR)i = (. . . , GIRi,k, . . . )

2. Calculate average GIR per user:

(GIR)i,average =1

Nr

Nr∑

k=1

GIRi,k

3. Check which user has the largest average GIR and assign all RUs tothis user

• End

4.2.2 Channel Dependent Frequency and Time Domain Schedul-ing

Time domain scheduling can lead to inefficient bandwidth utilization when userterminal buffers are limited. This problem can be dealt with by having bothtime and frequency domain scheduling. A channel dependent frequency and timedomain (CDFT) scheduler is derived from the RRFT scheduler if the user-RUgroupassignment is done based on CQI instead of time. The user-RUgroup pair whichhas the largest GIR is found, the RUgroup is assigned to the user and then itcontinues in that way until all RUgroups have been assigned. This schedulingalgorithm is basically the same as a downlink maximum SIR algorithm exceptthat in a downlink maximum SIR algorithm a user can be assigned more than oneresource block. This is not allowed in the uplink algorithm to prevent that RUsare assigned to users in a nonconsecutive manner.

CDFT algorithm (Max-GIR):

The user index is i and the RU index is k. The number of active users is Nand the number of available resource units is Nr. The number of RUgroups isRUgroupsize and the RUgroup index is j. All RUgroups contain bNr

N c RUs. Auser can only be assigned one RUgroup. The maximum number of RUgroups is L.

• Begin

4.3 Incomplete Channel Knowledge 29

1. If N <= L- RUgroupsize = N

2. If N > L- RUgroupsize = L

3. Get GIR vectors, for all users, containing GIR values per every RU:

(GIR)i = (. . . , GIRi,k, . . . )

4. Calculate the average GIR per RUgroup and user:

GIRi,j =1

bNr

N c

bNrN c·j∑

k=(bNr/Nc·(j−1))+1

GIRi,k

5. While there are unassigned RUgroups- Find the unassigned user i - unallocated RUgroup j pair with max-imum GIR and allocate RUgroup j to user i

• End

The same discussion about the selection of the RUgroupsize limit L that ismade in 4.1.2 can be done for this scheduler as well. If there are different numberof subbands per user a Max SIR-algorithm has to be used instead of a Max GIR-algorithm. There are also other factors that are important when selecting L. Ifmany users are scheduled simultaneously every TTI it requires a lot of downlinksignalling since the base station has to signal frequency allocations to many users.In this study L is 10.

4.3 Incomplete Channel Knowledge4.3.1 Channel SoundingChannel sounding means that the transmission of localized reference signals is ona regular basis interrupted by a period of transmission of reference signals thatare distributed over the entire frequency domain. This means that channel gainknowledge for the entire bandwidth and all users can only be updated after achannel sounding period has occurred. The channel gain for a particular user whois scheduled for transmission in a certain part of the bandwidth could be updatedfor that frequency domain part. This is not done in this study. Here all usersonly get channel gain updates for the entire bandwidth every xth scheduling timeperiod where x is found by investigating how far it is possible to down samplethe gain without altering it substantially. The algorithms for channel dependentscheduling in chapter 4.2 are used with this CQI as input.


4.3.2 Distributed Reference Signals

In a distributed reference signal structure users transmit reference signals over theentire bandwidth when they transmit data. The channel gain part of the CQI isonly updated for users who were scheduled for transmission in the latest schedulingtime period. It is assumed that the initial channel gain of a new user is known.The channel dependent scheduling algorithms in chapter 4.2 are used with thisCQI as input.

4.4 Link AdaptationFor the cases of no channel knowledge and perfect channel knowledge the linkadaptation uses the same channel quality information as for the perfect channelknowledge case. When there is limited channel knowledge for the scheduler thesame limited channel knowledge is used for the link adaptation as well.

4.5 Synchronous HARQSynchronous adaptive HARQ is assumed. This means that for all scheduling al-gorithms users with pending retransmissions have to be scheduled regardless ofthe channel quality or previous assignment delay of other users. The differentschedulers are affected differently by this restriction. For the time domain sched-ulers the user who is supposed to use all resources is replaced by the user havingpending retransmissions. The CDFT scheduler allows an unassigned user withpending retransmissions to choose the RU group for which is has the largest GIRand remove the user who is supposed to be assigned this RU group. The RRFTscheduler removes the assigned users who have waited the shortest amount of timefor permission to transmit and replace them with unassigned users with pendingretransmissions.

4.6 Transmit PowerThe channel dependent scheduling algorithms described in this chapter have beenconstructed under the assumption that there is no power control and that theuplink users always are assigned their maximum power. The user maximum poweris evenly distributed over the RUs assigned to the users. The power allocated to asubband is the user maximum power divided by the number of subbands the userhas been assigned. The power per subband is the same regardless of which userthat is scheduled since all users are assigned the same power and the same numberof subbands. This is the reason why GIR is used as an input to the schedulingalgorithms instead of SIR. If the power allocation for different users and differentRUs varies a Max SIR algorithm is used.

4.7 Receiver Diversity 31

4.7 Receiver DiversityIf the base station only has one antenna per cell and perfect user orthogonality isassumed the base station experiences the same interference regardless of which userthat is scheduled. The scheduling decision is not affected by the interference partof the GIR and the Max-GIR algorithm can be turned into a Max-G algorithm.For the E-UTRA uplink however receiver diversity will be implemented and therewill be two antennas per cell. Receiver diversity results in a total received GIRthat is the sum of the GIR of all receiver antennas in the cell. The consequence ofthis is that the scheduling decision depends on the interference. Receiver diversityalso causes the GIR to fluctuate less than if there is only one receiver antenna sincethe probability of frequency dips is less for two antennas than for one antenna.This can decrease the gain of channel dependent scheduling.

4.8 Expected ResultsIn case of perfect channel knowledge it is expected that CDT scheduling performsbetter than RR scheduling and CDFT scheduling better than RRFT scheduling.When the send buffers of users are limited it is likely that CDFT performs bet-ter than CDT due to the inefficient bandwidth utilization of the time domainscheduler. The user power-limitation can also cause the CDFT scheduling to dobetter than the CDT scheduling. The CDFT scheduler is assumed to be morefair than the CDT scheduler since it assigns several users every time period whilethe time domain scheduler favors one user with good channel conditions. Thechannel independent Round Robin schedulers are most likely more fair than theirchannel dependent correspondents since they in time vary their scheduling over allusers. Limited channel knowledge probably decreases channel dependent schedul-ing gains.


Chapter 5

Simulation Model

The simulation model includes a cellular network model, a propagation model, asystem model and a user and traffic model.

5.1 Cellular Network ModelA cellular network consists of base station coverage areas or sites. In the centerof each site is a base station. The site consists of several hexagonally shaped cellsor sectors. There are two simulation scenarios: A and B. In simulation scenario Athe network has one site with one cell and in simulation scenario B the networkhas seven sites with three cells. In simulation scenario B the cells are repeatedusing a wrap-around technique to limit border effects and in simulation scenarioA there is wrapping that stops users from leaving the cell when they cross thebase station site border. In both scenarios the cell radius, the radius of a circlethat circumscribes the cell, is 500 meters. In simulation scenario B this cell radiusresults in a site-to-site distance, the distance between two base stations, of 1500meters. One difference between the one cell scenario and the 21 cell scenario isthat there is no interference in the one cell scenario. In Figure 5.1 illustrations ofa three-cell base station site and a one-cell base station site are found.

Figure 5.1. A base station site with three cells and a base station site with one cell.

33

34 Simulation Model

5.2 Propagation ModelThe propagation model describes the signal attenuation a signal experiences as ittravels from the transmitter to the receiver. The model consists of four elements:distance attenuation, shadow fading, multipath fading and antenna gain. The pathgain caused by these elements is defined as the received transmission power overthe transmitted transmission power. The total gain is obtained by multiplying thegains of the four components.

Distance attenuation is the signal attenuation caused by the distance R thesignal travels,

Distance attenuation =1

β ·Rα. (5.1)

β depends on transmission frequency and α depends on the environment. Inurban environment α varies between 2.7 and 6.5 depending on cell size accordingto Heyman, [12].

A signal is subject to shadow fading when there are objects blocking the straightpath between the transmitter and the receiver. The shadow fading can be modeledas a lognormal distribution. It is spatially correlated and is sometimes called slowfading as it varies slowly with position. The decorrelation distance is the distanceat which the autocorrelation of shadowing is equal to e−1 of its maximum value.It varies between 50 and 100 meters in outdoor environments. Another simulationparameter which affects the shadow fading gain is the correlation between basestation sites.

Multipath fading is caused by a signal taking different paths between trans-mitter and receiver making the received signal a sum of different versions of thetransmitted signal having different attenuations, delays and phases. The multi-path fading is called fast fading. How fast the fading is depends on user terminalspeed. In these simulations a multipath fading typical for urban environment isselected.

In simulation scenario B there are two directional receiver antennas directedtowards each cell, a total of six antennas per base station site. Directional antennasare provided a certain antenna gain, g(φ), for a certain angle φ,

g(φ) = −min(12.0 · ( φ

φ3)2, gfbr) + gmax dB. (5.2)

Parameters affecting the antenna gain are the maximum antenna gain, gmax,φ3 and gfbr (results in the minimum antenna gain). If there is only one cell persite as in simulation scenario A and subsequently two omnidirectional antennasper site the antenna gain is decided by a one cell maximum antenna gain.

Table 5.1 includes the propagation parameter values used in the simulations.

5.3 System modelThe system model involves such factors as bandwidth, maximum transmissionpower, the frequency reuse factor, TTI length, number of resource units, number

5.3 System model 35

Parameters Valuesβ 102.903

α 3.52Shadow fading mean value 0 dBShadow fading deviation 8 dBDecorrelation distance 100 metersCorrelation between sites 0.5gmax 14 dBφ 1.2217 radiansgfbr 20 dBOmniantenna maximum gain 10 dBReceived noise per subband -149.1 dB

Table 5.1. The propagation parameters.

of data blocks per resource unit, supported modulation methods and code.The system model parameter values are given in table 5.2.

Parameters ValuesBandwidth 20 MHzMaximum transmission power per user 0.25 WattMaximum transmission power per subband 0.25 WattFrequency reuse factor 1TTI 0.5 msNumber of subbands 100Number of data blocks per RU 60Modulations QPSK, 16QAM, 64QAMSIR backoff 0.5 dBCode 3GPP turbo codeCode rate limit 0.9

Table 5.2. The system model parameters.

The link adaptation is performed each TTI and consists of modulation adapta-tion and code rate adaptation. Based on the equalized total SIR over the assignedRUs of a user the received bit information, an indication of the number of cor-rectly received bits, of the different modulations methods is decided. The numberof transmitted coded bits per user resource, the code block length, is calculatedby multiplying the number of RUs, the number of data blocks per RU and thenumber of bits per data block corresponding to each modulation (2 bits/data blockfor QPSK, 4 bits/data block for 16QAM and 6 bits/data block for 64QAM). Themodulation that provides the highest received bit information rate, received bitinformation over code block length, is selected. With the code block length andreceived bit information rate, corresponding to the selected modulation, as input

36 Simulation Model

the code rate is found from a table that is created to fit a certain BLER target.In this study the BLER target is 0.1. The code rate multiplied with the numberof coded bits results in the number of information bits.

The HARQ system and the scheduler are also parts of the system model. TheHARQ system is a synchronous adaptive N-channel stop- and -wait Type II HARQsystem with 8 HARQ processes. A HARQ process transmission that has not beenACKed after 0.05 seconds, typically 5 transmission attempts, results in a HARQtimeout which means that the user has to redo the transmission. There is onescheduler per cell. The scheduling is done according to algorithms specified inchapter 4. For all algorithms the scheduling is performed every subframe timeperiod, i.e. every 0.5 ms. The resource units that are scheduled are half as largein time as the resource units mentioned in chapter 2. A frequency reuse factor of1 means that all cells have access to the entire bandwidth.

5.4 User and Traffic ModelThere is a constant number of users who are present from the beginning of thesimulations. The users are randomly placed over the entire network according toa uniform distribution. In simulation scenario A the users move at a speed of 3km/h and in simulation scenario B the users are stationary. The stationarity of theusers of simulation scenario B means that the distance attenuation, the antennagain and the shadow fading are constant. The multipath fading, however, variesdue to the fact that the surroundings of the users move at a speed of 3 km/h inreference to them. All simulations are run with an average of 2, 20 and 40 usersper cell. Simulation scenario A with perfect channel knowledge is also simulatedfor 10 and 60 users per cell.

Simulation scenario A and B have different web traffic model scenarios. Inboth scenarios every user uploads a packet and is then eliminated from the system.The eliminated user is immediately replaced by a a new user which keeps the totalnumber of users in the system constant. The placement of the user in the networkis randomly selected. The scenarios differ in packet size. In scenario A the packetsize is 5 MByte and in scenario B it is 200 kByte. The large packet size of scenarioA means that apart from a short setup time where the base station requests thepacket and a TCP slow start is performed a user always has data to transmit.

5.5 Simulation TimeThe simulation times are different for different number of users and different al-gorithms. The simulation time is decided by the number of generated packetswhich needs to be large enough to give statistically reliable results. The target isto generate at least 500 packets per simulation for simulation scenario A and 2500packets per simulation for simulation scenario B. For simulation scenario A thesimulation times vary between 300 and 600 seconds and for simulation scenario Bbetween 10 and 40 seconds.

Chapter 6

Simulation Results

This chapter presents the results and evaluations of the algorithms explained inchapter 4 using the simulation models described in chapter 5. The performance ofthe channel dependent schedulers are compared to the performance of the channelindependent Round Robin schedulers. It is also investigated how limited channelgain knowledge affects the channel dependent scheduling.

6.1 Performance MeasuresThe measures used to evaluate performance are packet bit rate and cell throughput.Cell throughput estimates the overall system performance of the schedulers. It isdefined as total number of transmitted bits per cell during the simulation timeover the simulation time,

Cell Throughput =Transmitted bits per cell

simulation time . (6.1)

Packet bit rate estimates user performance. Both median packet bit rate (50thpercentile packet bit rate) and 10th percentile packet bit rate are of interest. 10thpercentile packet bit rate, the packet bit rate of the worst off users, provides afairness measure. Packet bit rate is defined as the packet size over the time ittakes to transmit the packet,

Packet Bit Rate =Message size in bitstransmission time . (6.2)

Two alternative load measures are cell throughput and the average number ofusers per cell.

BLER is also logged to investigate if the different schedulers result in differentnumber of retransmissions.

37

38 Simulation Results

6.2 Perfect Channel KnowledgePerfect channel knowledge simulations results are based on simulations using sim-ulation scenario A and B.

6.2.1 Simulation Scenario ASimulation scenario A includes a network with one site and one cell. It providesperfect channel knowledge since new channel gain updates are always available forall users and there is no interference.

As can be seen in Figure 6.1 the cell throughput gain is small for CDT schedul-ing in comparison to RR scheduling for low load but increases with increasing load.It ranges from 1 percent for 2 users per cell to 18 percent for 60 users per cell. Forhigher loads there are more users competing for the RUs making it more importantto select users providing good throughput. If there is one user with high averageGIR and many users with low average GIR the RR scheduler suffers from it sinceit schedules the user with high average GIR seldom. The CDT scheduler on theother hand only schedules this user.

0 10 20 30 40 50 6035

40

45

50

55

60

65

Number of users

Cel

l Thr

ough

put (

Mbi

t/s)

RRCDTCDFTRRFT

Figure 6.1. The cell throughput for 2, 10, 20, 40 and 60 users in simulation scenario A.

The time and frequency domain schedulers have higher cell throughput thanthe time domain schedulers. For 20 users per cell the cell throughput of the CDFTscheduler is 36 percent larger than the CDT scheduler. The corresponding figurefor the RRFT scheduler in comparison to the RR scheduler is 49 percent. This isprobably because the CDFT scheduler and the RRFT scheduler provide a lot ofpower over the entire bandwidth when several power-limited users are scheduled


simultaneously. The result is that the SIR per subband is larger for the time andfrequency domain schedulers than the time domain schedulers.

The channel dependent scheduling cell throughput gain is smaller for the CDFTscheduler than for the CDT scheduler for all loads. The gain is 0 percent for 2users per cell, 2 percent for 10 users per cell, 7 percent for 20 users per cell, 6percent for 40 users per cell and 5 percent for 60 users per cell. The large powerper subband allocation of the CDFT and RRFT schedulers result in high SIR persubband for all users, even for users with relatively low GIR. The consequence ofthis is that many of the users who are scheduled by the CDFT or RRFT schedulerare assigned the maximum transport format size. This is also the reason why thecell throughput increase of the CDFT scheduler levels out for higher numbers ofusers per cell than 20. Already for 20 users it can select 10 users with SIR valuesthat result in maximum transport format sizes for all users. The cell throughputof the CDT scheduler continues to increase for higher loads than 20 users percell, which means that the cell throughput gain of CDFT in comparison to CDTdecreases.

In these simulations the highest available modulation is 64QAM. If it werepossible for the link adaptation to select modulations of higher order than 64QAMboth the channel dependent scheduling gain of the CDFT scheduler and its cellthroughput gain in comparison to the CDT scheduler would be larger. If it is notpossible to select 64 QAM and 16QAM is the modulation of highest order which ispossible to choose the channel dependent and frequency domain scheduling gainsare probably smaller. In table 6.1 the cell throughput results of all schedulerswith a load of 20 users per cell and 16QAM and QPSK as available modulationsare presented. To enable a comparison the table also contains the cell throughputvalues when 64QAM, 16QAM and QPSK are available modulations. The channeldependent scheduling gain of CDT scheduling is 11 percent instead of 13 percent,which is the channel dependent cell throughput gain of the CDT scheduler when64QAM is an available modulation method. For the CDFT scheduler the channeldependent gain is 1 percent instead of 7 percent. The cell throughput gain of theCDFT scheduler in comparison to the CDT scheduler is 10 percent instead of 36percent.

Scheduler Cell throughput (Mbit/s) Cell throughput (Mbit/s)16QAM 64QAM

RR 34 39CDT 38 44CDFT 42 62RRFT 41 58

Table 6.1. Cell throughput for 20 users per cell in simulation scenario A with 16QAMas modulation of highest available order and 64QAM as modulation of highest availableorder.

Median packet bit rate is plotted against the number of users per cell in Figure6.3 and against cell throughput in 6.5. Figure 6.4 and Figure 6.6 show 10thpercentile packet bit rate plotted against the number of users per cell and cell


throughput. The median and 10th percentile packet bit rates are read off fromcumulative distributive functions (CDFs). An example of such a packet bit rateCDF is seen in Figure 6.2.

When the number of users per cell is used as load measure the CDT scheduleris the winner when it comes to median packet bit rate, see Figure 6.3, and theloser when it comes to 10th percentile packet bit rate, see Figure 6.4. It is notstrange since it favors users with good channel conditions. 10th percentile packetbit rate with number of users as load measure provides a fairness measure. TheRR scheduler is more fair since it varies its scheduling of users over time, butit also has the worse median packet bit rate because of this. RRFT is as couldbe expected more fair than CDFT since it let all users transmit every now andthen while CDFT favors users with good channel conditions. For the same reasonCDFT has higher median packet bit rate than RRFT. When the numbers of usersper cell is 10 or less the RRFT and CDFT schedulers have approximately thesame 10th percentile packet bit rate since they both allow all users to transmitevery TTI. The CDFT scheduler has higher 10th percentile packet bit rate thanthe CDT scheduler since it assigns several users instead of one every TTI. Thevery unfair CDT scheduler has higher median packet bit rate than the more fairCDFT scheduler.

0 5 10 15 20 25 30 35 40 45 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Packet Bit Rate (Mbit/s)

Pro

babi

lity

RRCDTCDFTRRFT

Figure 6.2. The packet bit rate CDF for 20 users in simulation scenario A.

If instead cell throughput is used as load measure the picture changes as can beseen in Figure 6.5 and Figure 6.6. First of all it is difficult to compare the frequencyand time domain schedulers with the time domain schedulers since the frequencyand time domain schedulers generate a much larger cell throughput. It is howeverstill evident that the median packet bit rate of the channel dependent schedulers is


0 10 20 30 40 50 600

5

10

15

20

25

30

35

40

Number of users

Med

ian

Pac

ket B

it R

ate

(Mbi

t/s)

RRCDTCDFTRRFT

Figure 6.3. Median packet bit rate for 2, 10, 20, 40 and 60 users in simulation scenarioA.

larger than the median packet bit rate of the channel independent schedulers. Themain difference between Figure 6.4 and Figure 6.6 is that when 10th percentilepacket bit rate is plotted against cell throughput the channel dependent schedulershave higher 10th percentile packet bit rate than the channel independent schedulersexcept for very low loads. This is due to the larger cell throughput generation ofthe channel dependent schedulers.

From these results it can be concluded that small transmission bandwidths re-sult in high cell throughput, high 10th percentile packet bit rate, low median packetbit rate and low channel dependent scheduling gain in comparison to large trans-mission bandwidths. The more users that are scheduled simultaneously every TTIthe higher the SIR per subband becomes. This does not however automaticallymean that the cell throughput increases with the the number of simultaneouslyscheduled users. Until a certain limit it does and then it stops increasing. It de-pends on that there is a maximum transport format, a maximum number of codedbits and code rate, and this maximum transport format is reached for more orless all users for a certain number of simultaneously scheduled users. When thisnumber of users is surpassed the cell throughput remains the same even if the SIRper subband increases.

The BLER is approximately 0 for all schedulers and all number of users as canbe seen in Figure 6.7. There are some relative differences between the schedulers,but the numbers of retransmissions are so low that that they do not have an impacton the relative comparison between the schedulers.


0 10 20 30 40 50 600

5

10

15

Number of users

10th

Per

cent

ile P

acke

t Bit

Rat

e (M

bit/s

)

RRCDTCDFTRRFT

Figure 6.4. 10th percentile packet bit rate for 2, 10, 20, 40 and 60 users in simulationscenario A.

35 40 45 50 55 60 650

5

10

15

20

25

30

35

40

Cell Throughput (Mbit/s)

Med

ian

Pac

ket B

it R

ate

(Mbi

t/s)

RRCDTCDFTRRFT

Figure 6.5. Median packet bit rate and cell throughput for 2, 10, 20, 40 and 60 usersin simulation scenario A.


35 40 45 50 55 60 650

5

10

15

Cell Throughput (Mbit/s)

10th

Per

cent

ile P

acke

t Bit

Rat

e (M

bit/s

)

RRCDTCDFTRRFT

Figure 6.6. 10th percentile packet bit rate and cell throughput for 2, 10, 20, 40 and 60users in simulation scenario A.

0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

−3

Number of users

BLE

R

RRCDTCDFTRRFT

Figure 6.7. BLER in simulation scenario A.


6.2.2 Simulation Scenario B

In simulation scenario B the network has seven sites each consisting of three cells.Perfect channel knowledge is assumed in the sense that user channel gains areupdated continuously for all users. This scenario however includes the imperfectinterference estimate mentioned in chapter 3.2.1.

In this scenario the cell throughput measurements indicate large gains for bothCDT and CDFT scheduling compared to RR and RRFT scheduling, see Figure6.8. The gains increase with increasing load, from 20 percent for 2 users per cellto 52 percent for 40 users per cell for the CDT scheduler and from −6 percentfor 2 users per cell to 50 percent for 40 users per cell for the CDFT scheduler.It may be surprising that the gains are a lot larger for this scenario than forsimulation scenario A. One explanation is that when interference is included inthe CQI there are larger CQI variations and selecting users with good CQI hasa larger effect. Another explanation may be the BLER found in Figure 6.9. Thechannel dependent schedulers have lower BLER than their channel independentcorrespondents (except for the CDFT and RRFT schedulers for an average of 2users per cell). The high BLER of the channel independent schedulers may be aresult of the imperfect interference estimate. An incorrect interference estimatecauses incorrect SIR estimates with faulty link adaptation and high BLER asconsequences. A lot of retransmissions due to overestimated channel quality andassignments of low number of bits due to underestimated channel quality affectcell throughput negatively. The interference estimate used in this study is basedon that the interference is the same as in the previous TTI. This prerequisitesthat neighboring cells perform the same scheduling as in the previous TTI. Thisis never true for a Round Robin scheduler which results in the high BLER ofthe channel independent schedulers. The channel dependent schedulers are morestatic in their behavior and subsequently have lower BLER.

The cell throughput gain of frequency multiplexing users seen in simulationscenario A has almost disappeared. In this simulation scenario the cell throughputof the CDFT scheduler for an average of 40 users is only 5 percent larger than thecell throughput of the CDT scheduler. In simulation scenario A the correspondingfigure is 34 percent. Frequency and time domain scheduling in a multi-cell scenariodoes not only mean that the power per subband is larger than when performingtime domain scheduling. The interference is also increased since the neighboringcells are also performing frequency and time domain scheduling. If the scenariois interference limited, the noise is negligible in comparison to the interference,there is no effect of increasing the power per subband since the interference is alsoincreased. The consequence is that the cell throughput is the same for time domainschedulers and frequency and time domain schedulers. The erroneous interferenceestimate may also contribute to decreasing the cell throughput gain of frequencymultiplexing users. The CDT scheduler has a more static behavior and lowerBLER than the CDFT scheduler. The time and frequency domain schedulersare more sensitive to the incorrect interference estimate than the time domainschedulers. They are more dynamic since they can change their user assignment intwo dimensions and can have many users with underestimated interference values


0 5 10 15 20 25 30 35 405

6

7

8

9

10

11

12

13

14

Average number of users per cell

Ave

rage

Cel

l Thr

ough

put (

Mbi

t/s)

RRCDTCDFTRRFT

Figure 6.8. The cell throughput for an average number of 2, 20 and 40 users per cell insimulation scenario B.

resulting in a lot of retransmissions each TTI instead of only one user for thetime domain schedulers. Furthermore, as adaptive synchronous HARQ is appliedthe frequency and time domain schedulers are allowed to change the frequencyallocation of retransmissions.

For an average number of 2 users the cell throughput of the RRFT scheduleris higher than the cell throughput of the CDFT scheduler. Once again this candepend on that the interference estimate is more correct for a static scheduler thanfor a non static scheduler. When the number of users in a cell is equal to or less than10 the time and frequency domain schedulers let all users transmit. This makesthe RRFT scheduler static, while the scheduling of the CDFT scheduler variesover the bandwidth. The incorrect interference estimate does not only cause linkadaptation problems, it may also influence the scheduling decisions of the channeldependent schedulers.

The problem of high number of retransmissions is possibly smaller if the avail-able modulation of highest order is 16QAM instead of 64QAM. 16QAM resultsin a smaller maximum transport format size than 64QAM. If the SIR of a userresource is largely overestimated and the user is assigned more bits than it is pos-sible for the base station to receive the maximum difference between the numberof bits that is transmitted and the number of bits that can be received is lower if16QAM is the possible modulation selection of highest order instead of 64QAM.The result of this is that fewer retransmissions is required if it is not possible toselect 64QAM as modulation. A simulation for the RRFT scheduler with an aver-age of 20 users per cell and QPSK and 16QAM as available modulation results ina BLER of 0.31 and a cell throughput of 8.5 Mbit/s. The BLER is only somewhat


0 5 10 15 20 25 30 35 400.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4


BLE

R

RRCDTCDFTRRFT

Figure 6.9. BLER in simulation scenario B.

lower than the BLER of 0.32 which is obtained when 64QAM is available. Toremove 64QAM from the available modulations causes a cell throughput loss of 4percent. This small loss indicates that when 64QAM is available it is only seldomused and removing it from the available modulations subsequently only results ina minor BLER decrease.

In Figure 6.11 and Figure 6.13 median and 10th percentile packet bit rateare plotted against the average number of users per cell and in Figure 6.12 andFigure 6.14 median and 10th percentile packet bit rate are plotted against cellthroughput. The median and 10th percentile packet bit rates are exactly as insimulation scenario A found in CDFs. An example of such a packet bit rate CDFis seen in Figure 6.10.

The median packet bit rates of the schedulers follow more or less the samepattern as in simulation scenario A. The strong favoring of users with good channelconditions of the CDT scheduler renders it a higher median packet bit rate than thechannel independent RR scheduler. The same holds true for the CDFT schedulerwhen it is compared to the RRFT scheduler except for low loads. This exceptionis explained by the same reasoning about static and dynamic schedulers which isdone for the cell throughput. The expected fairness results, which are supportedin simulation scenario A when number of users per cell is used as load measure, arenot seen in Figure 6.13 for low loads. First for higher number of users per cell theexpected fairness behavior of the schedulers appears. For an average of 40 usersper cell the channel independent schedulers have higher 10th percentile packet bitrates than the channel dependent correspondents which are so unfair that their10th percentile packet bit rates are 0. If cell throughput is used as load measure


the channel dependent schedulers have higher 10th percentile packet bit rate thantheir channel independent correspondents for all comparable loads except for theCDFT and the RRFT schedulers at low loads when it is the other way around.It is possible that the incorrect interference estimate influences median and 10thpercentile packet bit rate results.

0 1 2 3 4 5 6 7 8 90

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Packet Bit Rate (Mbit/s)

Pro

babi

lity

RRCDTCDFTRRFT

Figure 6.10. The packet bit rate CDF for an average of 20 users per cell in simulationscenario B.

In Figure 6.15 and Figure 6.16 the difference between estimated SIR at thetransmitter side and received SIR at the receiver side is plotted for a user scheduledby the RR scheduler in a system with an average of 2 users per cell and a userscheduled by the CDT scheduler in a system with an average of 2 users per cell. Theuser is situated on the same location and in the same cell for the RR scheduler asfor the CDT scheduler. This illustrates how the estimated SIR is always incorrectfor a dynamic scheduler, but more correct for a more static scheduler and how achannel independent RR scheduler suffers from its dynamic behavior when it iscompared to the more static channel dependent scheduler. The 90th percentileabsolute SIR error of the RR scheduler is more than 10 dB, see Figure 6.17 for theCDF of the absolute SIR differences of the RR and CDT scheduled user. If theSIR is overestimated with 10 dB it takes many retransmissions before the receivercan receive the transmitted block. If it on the other hand is underestimated with10 dB, the user is assigned very few bits compared to what it would have beenassigned if the channel quality had been correctly estimated. There are sometimeslarge SIR differences for the CDT scheduler as well. The CDT scheduler is notcompletely static, e.g. when a user is finished and removed from the cell the CDTscheduler changes its scheduling to another user making the interference estimateof a scheduler in a neighboring cell incorrect.


0 5 10 15 20 25 30 35 400

1

2

3

4

5

6

7

8


Med

ian

Pac

ket B

it R

ate

(Mbi

t/s)

RRCDTCDFTRRFT

Figure 6.11. Median packet bit rate for an average number of 2, 20 and 40 users percell in simulation scenario B.

To investigate if it is the channel dependent scheduling or the the always in-correct interference estimate of the channel independent Round Robin schedulersthat explains the large cell throughput gain of the channel dependent schedulersthe cell throughput values of the channel dependent schedulers and channel inde-pendent Round Robin schedulers are compared to the cell throughput values ofchannel independent static schedulers for an average of 20 users per cell. The cellthroughput results and the BLER values are presented in table 6.2. Here it is seenthat a time and frequency domain scheduler really benefits from being static. Thestatic channel independent scheduler STATFT has the highest cell throughput ofall investigated schedulers, it is 6 percent larger than the cell throughput of theCDFT scheduler. It also has a low BLER of 5.8 percent. This is much lower thanthe BLER of the CDFT scheduler which is 25 percent. The conclusion of this isthat perhaps there is a channel dependent time and frequency domain schedul-ing gain, but it is probably lower than the 29 percent that is indicated if CDFTscheduling is compared to RRFT scheduling. The STATT scheduler has the worstcell throughput of all schedulers. It has a low BLER of 9.0 percent, but obviouslysuffers from that it reserves the entire bandwidth for one user at a time even ifthis user does not use its assigned subbands and that there may be users with lowCQI who use the entire bandwidth for long periods of time.


5 6 7 8 9 10 11 12 13 140

1

2

3

4

5

6

7

8

Average Cell Throughput (Mbit/s)

Med

ian

Pac

ket B

it R

ate

(Mbi

t/s)

RRCDTCDFTRRFT

Figure 6.12. Median packet bit rate and average cell throughput for an average numberof 2, 20 and 40 users per cell in simulation scenario B.

Scheduler Average cell throughput (Mbit/s) BLERRR 7.9 0.29CDT 11 0.16STATT 5.4 0.090CDFT 11 0.25RRFT 8.8 0.32STATFT 12 0.058

Table 6.2. Average cell throughput and BLER for an average of 20 users per cell insimulation scenario B.


0 5 10 15 20 25 30 35 400

0.5

1

1.5

2

2.5

3


10th

Per

cent

ile P

acke

t Bit

Rat

e (M

bit/s

)

RRCDTCDFTRRFT

Figure 6.13. 10th percentile packet bit rate for an average number of 2, 20 and 40 usersper cell in simulation scenario B.

5 6 7 8 9 10 11 12 13 140

0.5

1

1.5

2

2.5

3

Average Cell Throughput (Mbit/s)

10th

Per

cent

ile P

acke

t Bit

Rat

e (M

bit/s

)

RRCDTCDFTRRFT

Figure 6.14. 10th percentile packet bit rate and average cell throughput for an averagenumber of 2, 20 and 40 users per cell in simulation scenario B.


0 0.05 0.1 0.15 0.2 0.25 0.3 0.35−20

−15

−10

−5

0

5

10

15

20

25

Diff

eren

ce in

est

imat

ed a

nd r

ecei

ved

SIR

(dB

)

Time (s)

Figure 6.15. The difference between estimated SIR and received SIR for an RR sched-uled user.

0 0.05 0.1 0.15 0.2 0.25−15

−10

−5

0

5

10

15

Diff

eren

ce in

est

imat

ed a

nd r

ecei

ved

SIR

(dB

)

Time (s)

Figure 6.16. The difference between estimated SIR and received SIR for a CDT sched-uled user.


0 5 10 15 20 250

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Absolute difference in estimated and received SIR (dB)

Pro

babi

lity

CDTRR

Figure 6.17. The CDF of the difference between estimated SIR and received SIR foran RR and a CDT scheduled user.


6.3 Incomplete Channel KnowledgeTwo different cases of incomplete channel knowledge are investigated: channelsounding and distributed reference signals. Both these cases are investigated us-ing simulation scenario A to avoid that the interference estimate influences theinterpretation of the results.

6.3.1 Channel SoundingChannel sounding means that at a regular basis all user terminals transmit refer-ence signals from which the channel gains of the terminals are derived. In orderto obtain enough CQI from the channel sounding to enable channel dependentscheduling the channel sounding has to be done often enough. In Figure 6.18 sam-plings of the average gain for a user over all frequencies for the two antennas areshown with a sample period of 0.5 ms. The TDFTs of the channel gains revealtheir frequency contents. The results of the TDFT operation, using [9], are foundin Figure 6.19. According to the sample theorem, if the sample period is selectedso that the original TDFT is zero when the frequency is larger than the samplefrequency divided by two the TDFT of the sampled signal has the same frequencycontent as the original signal. The sampling of the average gain with sample pe-riod 12.5 ms is shown in Figure 6.20. As can be seen in these figures the downsampled curves follow the original sampled curves well.

0 0.5 1 1.5 2 2.50

0.2

0.4

0.6

0.8

1

1.2

1.4x 10

−13

Gai

n

Time (s)

Antenna 1

0 0.5 1 1.5 2 2.50

0.2

0.4

0.6

0.8

1

1.2x 10

−13

Gai

n

Time (s)

Antenna 2

Figure 6.18. Average gain for the two antennas.

In the decision of a suitable channel gain update time the average channel gainover the entire frequency band is considered. This is due to the fact that the CQI


0 100 200 3000

1

2

3

4

5

6

7

8x 10

−14

TD

FT

Angular Frequency (rad/s)

Antenna 1

0 100 200 3000

1

2

3

4

5

6

7

8x 10

−14

TD

FT

Angular Frequency (rad/s)

Antenna 2

Figure 6.19. TDFT of average gain for the two antennas.

used in the CDT scheduler and the CDFT scheduler is based on average gain, overthe entire bandwidth for the CDT scheduler and over parts of varying size of theentire bandwidth for the CDFT scheduler. If instead all subbands are consideredseparately it is probably found that the down sampling period should be shortersince the gain variations for individual subbands are faster.

As is expected the cell throughputs of the channel dependent schedulers witha channel gain sample period of 12.5 ms, or 25 scheduling time intervals, aresomewhat smaller (1-2 percent smaller) than the cell throughputs of the channeldependent schedulers of the perfect channel knowledge case, see Figure 6.21. Thiscan depend both on incorrect scheduling decisions due to incorrect GIR and onincorrect link adaptation due to incorrect GIR resulting in more retransmissionsand too low assignments of number of bits. From Figure 6.22 it is clear thatBLER has increased. In Figure 6.21 it is also seen that if the channel gain issampled very seldom, every 500 ms or every 1000th scheduling time interval, thecell throughput loss is larger. It varies between 14 percent for 2 users per celland 18 percent for 20 users per cell for the CDT scheduler and between 3 percentand 14 percent for the CDFT scheduler. It is interesting to note that the cellthroughput loss decreases with load for the CDFT scheduler. This could be theresult of the high SIR per subband that for high number of users result in highnumber of bit assignments in spite of incorrect scheduling. The CDFT schedulerwith perfect channel knowledge would have had even higher cell throughput if the


0 0.5 1 1.5 2 2.50

0.2

0.4

0.6

0.8

1

1.2

1.4x 10

−13

Gai

n

Time (s)

Antenna 1

0 0.5 1 1.5 2 2.50

0.2

0.4

0.6

0.8

1

1.2x 10

−13

Gai

n

Time (s)

Antenna 2

Figure 6.20. Sampled average gain for the two antennas.

maximum available transport format had been larger. It would have resulted inthat the cell throughput loss for the CDFT scheduler with channel sounding CQIhad not decreased with increasing load. The increase in BLER seen in Figure 6.22is an indication of increased number of link adaptation mistakes due to incorrectlyestimated channel quality. For high numbers of users per cell the increase in BLERis smaller for the CDFT scheduler than for the CDT scheduler. The high powerper subband allocation of the CDFT scheduler often makes the link adaptationprovide users with the maximum transport format size almost no matter whattheir channel gain values are. In these cases there are no link adaptation mistakesdue to infrequently updated channel gains.


0 5 10 15 20 25 30 35 4030

35

40

45

50

55

60

65

Number of users

Cel

l Thr

ough

put (

Mbi

t/s)

CDTCDT CQI update every 12.5 msCDT CQI update every 500 msCDFTCDFT CQI update every 12.5 msCDFT CQI update every 500 ms

Figure 6.21. Cell throughput for channel dependent schedulers with perfect CQI andchannel sounding CQI.

0 5 10 15 20 25 30 35 400

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Number of users

BLE

R

CDTCDT CQI update every 12.5 msCDT CQI update every 500 msCDFTCDFT CQI update every 12.5 msCDFT CQI update every 500 ms

Figure 6.22. BLER for channel dependent schedulers with perfect CQI and channelsounding CQI.


6.3.2 Distributed Reference Signals

When a distributed reference signal structure is applied reference signals are onlytransmitted when data is transmitted. This means that the channel gain part ofthe CQI of a user is only updated when it transmits data.

For the CDFT scheduler the cell throughput loss caused by the CQI delayof this case is approximately the same as the cell throughput loss caused by the12.5 ms gain sampling of the channel sounding case (0-1 percent). For the CDTscheduler the cell throughput loss seems to be increasing with increasing load,from 0 percent for 2 users per cell to 9 percent for 40 users per cell, see Figure6.23. The CDT only schedules one user, the user with best CQI, every TTI, whichimplicates that only one user every TTI gets a channel gain update. If it is thesame user who is scheduled for a long period of time all other users get very longCQI delays. This can cause an incorrect scheduling. The problem increases withincreasing number of users since there are more and more users with incorrectCQI values. The CDFT scheduler does not have the same problem as the CDTscheduler since 10 users are scheduled every TTI instead of one and subsequently10 users get channel gain updates every TTI. Moreover, the CDFT scheduler ismore insensitive to incorrect scheduling. Its high power per subband concentrationoften gives it the maximum transport format size for all users even if some usersshould have been replaced by users with better channel conditions. The CDFTscheduler with perfect channel knowledge probably would have had even highercell throughput if the maximum available transport format had been larger andthe cell throughput loss of the CDFT scheduler with limited CQI had been larger.

0 5 10 15 20 25 30 35 4035

40

45

50

55

60

65

Number of users

Cel

l Thr

ough

put (

Mbi

t/s)

CDTCDT with limited CQICDFTCDFT with limited CQI

Figure 6.23. Cell throughput for channel dependent schedulers with perfect CQI anddistributed reference signal CQI.


The BLER values of the schedulers increase somewhat compared to the perfectchannel knowledge case, see Figure 6.24, probably due to incorrect CQI resultingin incorrect link adaptation. This effect is not as clear as in chapter 6.3.1. TheCDT and CDFT scheduler are rather static schedulers, i.e. they schedule thesame user or users every TTI. The implication of this is that for users that arealways transmitting there are no CQI delays and no incorrect SIR inputs to thelink adaptation.

0 5 10 15 20 25 30 35 400

1

2

3

4

5

6x 10

−3

Number of users

BLE

R

CDTCDT with limited CQICDFTCDFT with limited CQI

Figure 6.24. BLER for channel dependent schedulers with perfect CQI and distributedreference signal CQI.

Chapter 7

Conclusions

The simulations of this study indicate that there are channel dependent schedulinggains if channel dependent scheduling is compared to channel independent RoundRobin scheduling.

For the single-cell scenario with large packets there is a cell throughput gainof approximately 10 percent for channel dependent scheduling. It is higher forchannel dependent time domain scheduling than for channel dependent time andfrequency domain scheduling. The time and frequency domain schedulers haveabout 40 percent higher cell throughput than the time domain schedulers. Thisdepends on the high power per subband density of the time and frequency domainschedulers. Both the channel dependent scheduling gain and the frequency domainscheduling gain are limited by the available modulation of highest order. On auser level it is observed that when the number of users per cell is used as loadmeasure both channel dependent schedulers have lower 10th percentile packet bitrate than their channel independent correspondents due to favoring of users withhigh channel quality.

For a multi-cell scenario with small packets the gains of channel dependentscheduling are large, about 50 percent for high loads. Unlike the single-cell sce-nario the multi-cell scenario includes interference which contributes to an increasedpositive effect of channel dependent scheduling by providing larger CQI differencesbetween different users. The cell throughput gains of time and frequency domainscheduling in comparison to time domain scheduling are smaller than in the single-cell scenario since increasing the power per subband density also means increasinginterference.

When interpreting the multi-cell simulation results it has to be taken intoaccount that the dynamic channel independent Round Robin schedulers are in-fluenced negatively by the utilized interference estimate. To assume that theinterference is the same in the next transmission time interval as it was in theprevious transmission time interval is obviously incorrect for a dynamic scheduler.The channel dependent schedulers are less dynamic than the channel independentRound Robin schedulers. If the cell throughput of channel dependent frequencyand time domain scheduling instead is compared to the cell throughput of static

59

60 Conclusions

channel independent frequency and time domain scheduling there is a channel de-pendent scheduling loss. In this comparison the static scheduling benefits fromthat it is more static than the channel dependent scheduling, which means thatthere probably is a channel dependent scheduling gain even if it probably not isas large as the one indicated by the comparison between the channel independentRound Robin frequency and time domain scheduling and the channel dependentfrequency and time domain scheduling. The interference estimate needs improve-ment in order to establish how large the channel dependent scheduling gains for amulti-cellular network really are.

To update CQI every 25th scheduling time period instead of every schedulingtime period in a single-cell scenario has only a minor effect on the cell throughputof the channel dependent schedulers. The conclusion of this is that a reference sig-nal structure where channel sounding is done with sufficient frequency preservesmost of the channel dependent scheduling gain of a reference signal structure withcontinuous transmission of reference signals. If CQI only is updated for users whenthey transmit data the cell throughput of channel dependent time domain schedul-ing decreases for high loads while it is almost unchanged for channel dependentfrequency and time domain scheduling. The cell throughput loss of the channeldependent frequency and time domain scheduling is small since the cell through-put of the channel dependent frequency and time domain scheduling using perfectknowledge is limited by the maximum available order of modulation and code rate.The reference signal structure most suitable for channel dependent scheduling, ofthe two investigated ones, is channel sounding with sufficient channel soundingfrequency since it provides almost the same cell throughput as a reference signalstructure with continuous transmission of reference signals for channel dependentscheduling in time domain and in time and frequency domain.

Chapter 8

Further Studies

There are channel dependent scheduling factors that are not included in this studythat could be interesting to investigate.

The interference estimate that is used in this study is simple and straightfor-ward, but can be very erroneous for non static scheduling. It would be interestingto run the simulations of this study with a more correct interference estimate, e.g.an interference estimate that is the result of a filtration of the interference in timeor frequency or in both, and see how the result is affected. Another interestingstudy would be to investigate how varying the cell radius size affects the simulationresults. In the multi-cell scenario of this study the noise seems to be negligiblein comparison to the interference. For a scenario with cells of larger radii theinterference is perhaps negligible in comparison to the noise instead.

In the simulator used in this master thesis the intra-cell interference due toimperfect transmitter and receiver implementations is not modeled, see chapter2.3.2. The presence of this interference requires power control. The impact onthe simulations results caused by the interference and the power control needed toaccount for it would also be interesting to examine.

The channel dependent time and frequency domain scheduling method of themaster thesis has a transmission bandwidth that is dependent on the number ofactive users, but not on channel quality. It could be investigated if a schedulingmethod with a transmission bandwidth that depends on channel quality wouldmake channel dependent scheduling more advantageous compared to channel in-dependent scheduling. The study also has a transmission bandwidth size limit ofsize 2 MHz. It could be interesting to find the optimal limit.

The channel dependent scheduling algorithms of this study are based on down-link scheduling Max SIR algorithms and focus on maximizing total throughputin a suboptimal way. Alternatively the algorithms could be based on downlinkscheduling fairness algorithms that maximize throughput while observing fairness.A next step would be to also consider QoS.

The performance of the channel dependent scheduling algorithms could be com-pared to the performance of frequency-hopping algorithms. Frequency-hoppingalgorithms could be a thinkable solution for a SC-FDMA uplink with a localized

61

62 Further Studies

reference signal structure, see chapter 3.2.1.In chapter 6.3.1 it is concluded that it from a channel dependent scheduling

point of view would be alright to have a channel sounding reference signal structurewhere channel sounding is performed every 25th scheduling time period. Thisconclusion is made when assuming that the user terminals move at a speed of 3km/h. It could be interesting to investigate how user terminal speed affects theCQI update rate.

This master thesis studies FDD channel dependent scheduling. As is mentionedin chapter 3.2 it could be possible to use downlink CQI in a TDD system. It couldbe investigated in a future study if this is possible and if it would give a channeldependent scheduling gain.

Bibliography

[1] Lars Ahlin and Jens Zander. Principles of Wireless Communications. Stu-dentlitteratur, 1998.

[2] E Dahlman, P Frenger, Guey Jiann-Ching, G Klang, R Ludwig, M Meyer,N Wiberg, and K Zangi. A framework for future radio access. IEEE VehicularTechnology Conference, 5, May 2005.

[3] NTT DoCoMo. Channel-dependent packet scheduling for single-carrier fdmain evolved utra uplink r1-051390. Available online: www.3gpp.org, November2005.

[4] NTT DoCoMo, Fujitsu, NEC, and SHARP. Channel-dependent schedulingmethod for single-carrier fdma radio access in evolved utra uplink r1-050701.Available online: www.3gpp.org, August 2005.

[5] NTT DoCoMo, Fujitsu, NEC, Sharp, and Toshiba Corporation. Group-wised frequency resource allocation for frequency domain channel-dependentscheduling in sc-based e-utra uplink r1-061679. Available online:www.3gpp.org, June 2006.

[6] H Ekstrom, A Furuskar, J Karlsson, S Meyer, M Parkvall, J Torsner, andM Wahlqvist. Technical solutions for the 3g long-term evolution. IEEE Com-munications Magazine, 44, March 2006.

[7] QualComm Europe. Sub-band scheduling in e-utra uplink r1-060482. Avail-able online: www.3gpp.org, February 2006.

[8] QualComm Europe. Uplink broadband pilot structure r1-060474. Availableonline: www.3gpp.org, February 2006.

[9] Fredrik Gunnarsson. Matlab script: tdft.m. Available online:http://www.control.isy.liu.se/student/tsrt78/index.html, February 1999.

[10] Fredrik Gustafsson, Lennart Ljung, and Mille Millnert. Signalbehandling.Studentlitteratur, 2000.

[11] Seung Hee Han and Jae Hong Lee. Peak-to-average power ratio reduc-tion of an ofdm signal by signal set expansion. Available online: cct-lab01.snu.ac.kr/nrl/conference/ICC2004_Han.pdf, 2004.

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64 Bibliography

[12] Jessica Heyman. Intercell interference managment in an ofdm-based downlink.Master’s thesis, LiTH, 2006. Available online: www.ep.liu.se.

[13] A Jamalipour, T Wada, and T Yamazato. A tutorial on multiple access tech-nologies for beyond 3g mobile networks. IEEE Communications Magazine,43, February 2005.

[14] Yu-Kwong Kwok and V.K.N. A novel channel-adaptive uplink access controlprotocol for nomadic computing. IEEE Parallel and Distributed Systems, 13,November 2002.

[15] Jean Christophe Laneri. Scheduling algorithms for su-per 3g. Master’s thesis, KTH, 2006. Available online:www.cos.ict.kth.se/publications/publications/2006/2528.pdf.

[16] Junsung Lim, Hyung G. Myung, Oh Kyungjin, and David Goodman. Singlecarrier fdma technique for uplink wireless communication. Available online:wicat.poly.edu/internal/documents/04-06/Lim-Goodman.ppt, April 2006.

[17] 3GPP Technical Specification Group Radio Access Network. Physical layeraspects for evolved universal terrestrial radio acces. Technical Report TR25.814, June 2006.

[18] Nokia. Channel dependent scheduling in e-utra uplink and text proposalr1-060295. Available online: www.3gpp.org, February 2006.

[19] Sharp NTT DoCoMo, NEC. Channel-dependent packet scheduling forsingle-carrier fdma in evolved utra uplink r1-060048. Available online:www.3gpp.org, January 2006.

[20] Stefan Parkvall. Long-term 3g evolution - radio access. Available online:www.calit2.net/events/pdfs/S3G_Stefan_Parkvall.pdf, November 2005.

[21] Anders Ruberg. Frequency domain link adaptation for ofdm-based cellularpacket data. Master’s thesis, LiTH, 2006. Available online: www.ep.liu.se.

[22] Zhi Zhang, Ying He, and Edwin K. P. Chong. Opportunisticdownlink scheduling for multiuser ofdm systems. Available online:www.engr.colostate.edu/ echong/pubs/conf/01424680.pdf, 2005.

Appendix A

OFDM andDFT-Spread-OFDM

A.1 OFDMOFDM is the transmission scheme used in the LTE downlink. It is based ondividing the frequency bandwidth into subcarriers, sk, of equal width,

sk = cos(2πfkt) + i · sin(2πfkt), 0 ≤ t ≤ Ts (A.1)

fk = fo +k

Ts. (A.2)

In order to make these subcarriers orthogonal the frequency spacing betweenconsecutive subcarriers is one over symbol time, Ts,

Ts∫

0

si · sjdt = 0, i 6= j. (A.3)

The transmitted signal consists of subcarriers that have been modulated usinga suitable modulation scheme, e.g. QAM. The correlation receiver utilizes theorthogonality of the subcarriers by doing a multiplication of the received signalwith each subcarrier followed by integration over the symbol time resulting ininformation only from the subcarriers that are present in the transmitted signal.

Multipath fading causes intersymbol interference. The solution to the problemof intersymbol interference, causing the received symbol to be a weighted sumof the sent symbol and its neighbors, is the insertion of a cyclic prefix, i.e. thecopying and inserting of the last samples of the OFDM symbol at the beginning ofthe OFDM symbol. The number of copied samples is selected so that it is greaterthan or equal to the maximum multipath delay. This means that the multipathdelay of one symbol only affects the cyclic prefix of the next symbol and not theother samples of the symbol.

65

66 OFDM and DFT-Spread-OFDM

An Inverse Fast Fourier Transform (IFFT) creates the OFDM symbol of themodulated symbols. One way to understand this is that a Fast Fourier Transform(FFT) applied to a signal in the time domain reveals the frequency componentsof the signal. In the same way the subcarriers can be thought of as the frequencycomponents of the OFDM symbol.

One drawback of OFDM is the high PAPR of the transmitted signals, describedfurther by Han and Lee, [11]. A constructive addition of subcarrier signals canresult in a large peak power. The power consumption of a power amplifier dependson the peak power and high power peaks lead to low power efficiency.

The main OFDM scheme is depicted in Figure A.1.

Figure A.1. OFDM.

A.2 DFT-Spread-OFDMDFT-Spread-OFDM is the transmission scheme of the SC-FDMA uplink describedby Dahlman, Frenger, Jiann-Ching, Klang, Ludwig, Meyer, Wiberg, and Zangi,[2]. In the Figure A.2 M modulation symbols are transformed into the frequencydomain by an M-sized Discrete Fourier Transform (DFT). The DFT transforma-tion is followed by the insertion of a cyclic extension and a spectrum shapingperformed by a frequency domain filter.

The M spectrum shaping outputs are mapped into N IFFT inputs. Whichpart of the bandwidth that is used for transmission is chosen by inserting zerosat the IFFT inputs. In the case of localized transmission mapping is done intoconsecutive IFFT inputs or subcarriers. For distributed transmission mapping isinstead done into equally spaced subcarriers.

As mentioned before DFT-S-OFDM can be seen as pre-coded OFDM. The pre-coding is responsible for the lower PAPR of the SC-FDMA compared to OFDM.

A.2 DFT-Spread-OFDM 67

Figure A.2. DFT-S-OFDM.

Appendix B

TDFT and SamplingTheorem

The Time Discrete Fourier Transform of a discrete signal x[k] with sampling periodtime T is defined as

XT (eiωT ) =∞∑

k=−∞x[k]e−iωkT . (B.1)

The sampling theorem states that if the Fourier transform X(iω) of a con-tinuous signals x(t) is equal to 0 outside the interval [−ωs/2, ωs/2], where ωs

is the sampling frequency, then the sampling causes no sampling loss, see [10].X(iω) = XT (eiωT ) and the sampled signal has the same frequency content as theoriginal continuous signal. The frequency ωs/2 is called the nyquist frequency.

The sampling theorem can also be applied to a discrete signal that is downsampled. If the TDFT of the original discrete signal only has frequencies belowthe nyquist frequency then the TDFT of the sampled discrete signal is equal tothe TDFT of the original discrete signal.

68

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UplinkChannelDependentSchedulingforFuture CellularSystemsliu.diva-portal.org/smash/get/diva2:23046/FULLTEXT01.pdfKeywords channel dependent scheduling, uplink, SC-FDMA, channel quality