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Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

Automated Performance Optimization ofGSM/EDGE Network Parameters

Examensarbete utfört i kommunikationssystemvid Tekniska högskolan i Linköping

av

Jonas Gustavsson

LiTH-ISY-EX--09/4310--SE

Linköping 2009

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

Automated Performance Optimization ofGSM/EDGE Network Parameters

Examensarbete utfört i kommunikationssystemvid Tekniska högskolan i Linköping

av

Jonas Gustavsson

LiTH-ISY-EX--09/4310--SE

Handledare: Mikael Olofssonisy, Linköpings universitet

Erik MagnussonEuP PS BSS I&V, Ericsson

Examinator: Mikael Olofssonisy, Linköpings universitet

Linköping, 18 December, 2009

Avdelning, InstitutionDivision, Department

Division of Communication SystemsDepartment of Electrical EngineeringLinköpings universitetSE-581 83 Linköping, Sweden

DatumDate

2009-12-18

SpråkLanguage

� Svenska/Swedish� Engelska/English

�

�

RapporttypReport category

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

�

URL för elektronisk versionhttp://www.commsys.isy.liu.se

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-52565

ISBN—

ISRNLiTH-ISY-EX--09/4310--SE

Serietitel och serienummerTitle of series, numbering

ISSN—

TitelTitle

Automatiserad prestandaoptimering av GSM/EDGE-nätverksparametrarAutomated Performance Optimization of GSM/EDGE Network Parameters

FörfattareAuthor

Jonas Gustavsson

SammanfattningAbstract

The GSM network technology has been developed and improved during severalyears which have led to an increased complexity. The complexity results in morenetwork parameters and together with different scenarios and situations they forma complex set of configurations. The definition of the network parameters is gen-erally a manual process using static values during test execution. This practicecan be costly, difficult and laborious and as the network complexity continues toincrease, this problem will continue to grow.

This thesis presents an implementation of an automated performance opti-mization algorithm that utilizes genetic algorithms for optimizing the networkparameters. The implementation has been used for proving that the concept ofautomated optimization is working and most of the work has been carried out inorder to use it in practice. The implementation has been applied to the Link Qual-ity Control algorithm and the Improved ACK/NACK feature, which is an apartof GSM EDGE Evolution.

NyckelordKeywords GSM, EDGE, Link Quality Control, LQC, Genetic Algorithms, Metaheuristics,

automation

AbstractThe GSM network technology has been developed and improved during severalyears which have led to an increased complexity. The complexity results in morenetwork parameters and together with different scenarios and situations they forma complex set of configurations. The definition of the network parameters is gen-erally a manual process using static values during test execution. This practicecan be costly, difficult and laborious and as the network complexity continues toincrease, this problem will continue to grow.

This thesis presents an implementation of an automated performance opti-mization algorithm that utilizes genetic algorithms for optimizing the networkparameters. The implementation has been used for proving that the concept ofautomated optimization is working and most of the work has been carried outin order to use it in practice. The implementation has been applied to the LinkQuality Control algorithm and the Improved ACK/NACK feature, which is anapart of GSM EDGE Evolution.

SammanfattningGSM-nätsteknologin har utvecklats och förbättrats under lång tid, vilket har letttill en ökad komplexitet. Denna ökade komplexitet har resulterat i fler nätverkspa-rameterar, tillstånd och standarder. Tillsammans utgör de en komplex uppsättningav olika konfigurationer. Dessa nätverksparameterar har hittills huvudsakligenbestämts med hjälp av en manuell optimeringsprocess. Detta tillvägagångssätt ärbåde dyrt, svårt och tidskrävande och allt eftersom komplexiteten av GSM-nätenökar kommer problemet att bli större.

Detta examensarbete presenterar en implementering av en algoritm för automa-tiserad optimering av prestanda som huvudsakligen använder sig av genetiska al-goritmer för att optimera värdet av nätverksparametrarna. Implementeringen haranvänts för att påvisa att konceptet med en automatiserad optimering fungeraroch det mesta av arbetet har utförts för att kunna använda detta i praktiken. Im-plementeringen har tillämpats på Link Quality Control-algoritmen och ImprovedACK/NACK-funktionaliteten, vilket är en del av GSM EDGE Evolution.

v

Acknowledgments

I would like to thank:

Erik Magnusson for supervising, feedback and our discussions.Mikael Olofsson for helping me in writing my thesis and your

excellent courses.Erik Persbo for helping me troubleshoot THC, the BSC,

the BB, ABIS, ATE GUI, [insert acronym here].Olof Manbo for assisting me with your technical expertise.Håkan Axelsson for assisting me with your technical expertise.Roland Sevegran for giving me this opportunity.Torbjörn Larsson for your advices about the choice of a suitable

optimization algorithm.Christian S. Perone from Brazil, for your Python library Pyevolve [20]

and personal support of it.www.di.fm for the endless hours of music that has put my mind

in a state of trance, helping me to focus on the thesis.All classmates I would have given up a long time ago without you

people. Thanks for a great time!Hanna For three years of love.

vii

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Thesis Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Organization of this Thesis . . . . . . . . . . . . . . . . . . . . . . 2

2 Theoretical Background of GSM 32.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Data Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3 Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.4 The Evolution of GSM Data Transmission . . . . . . . . . . . . . . 5

2.4.1 GSM Phase 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 52.4.2 GSM Phase 2+ . . . . . . . . . . . . . . . . . . . . . . . . . 52.4.3 General Packet Radio Service . . . . . . . . . . . . . . . . . 6

2.5 The GSM Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.6 Enhanced Data for Global Evolution . . . . . . . . . . . . . . . . . 7

2.6.1 The Modulation in EDGE . . . . . . . . . . . . . . . . . . . 82.6.2 Link Quality Control in EDGE . . . . . . . . . . . . . . . . 92.6.3 Improved ACK/NACK in Evolved EDGE . . . . . . . . . . 11

3 Problem and Solution Description 153.1 The Underlying Problem . . . . . . . . . . . . . . . . . . . . . . . . 153.2 The Existing Solution . . . . . . . . . . . . . . . . . . . . . . . . . 153.3 General Description of the New Solution . . . . . . . . . . . . . . . 173.4 The Test Environment - THC . . . . . . . . . . . . . . . . . . . . . 19

3.4.1 The Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.4.2 Operation of THC . . . . . . . . . . . . . . . . . . . . . . . 20

3.5 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.5.1 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . 23

3.6 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.6.1 Limitations of the Setup . . . . . . . . . . . . . . . . . . . . 253.6.2 Limitations of the Problem . . . . . . . . . . . . . . . . . . 273.6.3 Simplifications of the Problem . . . . . . . . . . . . . . . . 27

ix

x Contents

4 Implementation 294.1 The Crossover Operator . . . . . . . . . . . . . . . . . . . . . . . . 29

4.1.1 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1.2 Help Function . . . . . . . . . . . . . . . . . . . . . . . . . . 304.1.3 The Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 314.1.4 The Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2 The Mutation Operator . . . . . . . . . . . . . . . . . . . . . . . . 334.2.1 The Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 334.2.2 The Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.4 Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5 Results 375.1 File Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395.3 Quality of Found Optimum . . . . . . . . . . . . . . . . . . . . . . 405.4 Optimization Without Elitism . . . . . . . . . . . . . . . . . . . . . 415.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6 Future Research and Conclusions 456.1 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6.1.1 Algorithm Parameters to Adjust . . . . . . . . . . . . . . . 456.1.2 Improvements in the GA Implementation . . . . . . . . . . 466.1.3 Improvements in Usage . . . . . . . . . . . . . . . . . . . . 486.1.4 A Different Approach . . . . . . . . . . . . . . . . . . . . . 496.1.5 Beyond the APOA . . . . . . . . . . . . . . . . . . . . . . . 49

6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Bibliography 53

A Data of File Sizes 55

B Poor Individual 57

Abbreviations

3GPP 3 rd Generation Partnership Project8-PSK Eight-Phase Shift KeyingACK/NACK Acknowledged/Not AcknowledgedAF Automatic FrameworkAPOA Automated Performance Optimization AlgorithmBEP Bit Error ProbabilityBLER Block Error RateBSC Base Station ControllerBTS Base Transceiver StationC/I Carrier to Interference levelCV Coefficient of VariationEDGE Enhanced Data Rates for Global EvolutionEGPRS Enhanced GPRSGA Genetic AlgorithmsGGSN Gateway GPRS Support NodeGMSK Gaussian Minimum Shift KeyingGPRS General Packet Radio ServiceGSM Global System for Mobile CommunicationIAN Improved Acknowledged/Not AcknowledgedLQC Link Quality ControlMCS Modulation and Coding SchemeMS Mobile StationRLC Radio Link ControlSGSN Serving GPRS Support NodeTDMA Time Division Multiple AccessTHC Test Harness CoreTU3 Typically Urban, 3km/h

xiii

Chapter 1

Introduction

1.1 BackgroundThe GSM network has been developed and improved during several years which hasled to an increased complexity. The complexity results in more network parametersand together with different scenarios and situations they form a complex set ofconfigurations.

The definition of the network parameters is generally a manual process usingstatic values during test execution. This practice can be costly, difficult and la-borious and as the network complexity continues to increase, this problem willcontinue to grow.

A deeper description of the background of this thesis will follow in chapter 3.1.

1.2 Thesis ObjectiveThe amount of automation applied in the definition of network parameters hasuntil now been limited. The main objective for this thesis is to investigate ifit is possible to develop an automated performance optimization algorithm forGSM/EDGE networks in order to find the best configuration of some chosen net-work parameters by using a metaheuristic. The second objective is to create animplementation that is fully usable. The key question to answer in this thesis is:

Is it practically possible to automate the process of finding the bestconfiguration of network settings?

If the answer is yes, it is likely that such a solution will result in a betterperformance at a lower cost.

1.3 ObjectivesThe outline of this thesis can be grouped into a number of activities:

1

2 Introduction

• Perform a study of literature of the most common metaheuristics and choosea suitable metaheuristic for this problem.

• Choose a specific set of network parameters that will be used as an exampleduring development.

• Investigate the possibilities of using the metaheuristic by making an imple-mentation of it with the help of the existing test environment, THC.

• Test the software for automated performance optimization algorithm.

• Draw conclusion whether the main objective of the thesis has been fulfilled.

• Fulfill the second objective by completing the implementation to a fully op-erational state.

1.4 Organization of this ThesisChapter 2 gives the theoretical background of GSM that is needed in order to

understand the problem and the solution.

Chapter 3 explains the background of the problem in more depth than chapter1.1 and gives a more extensive description of the solution than chapter 1.2.It also presents the tools and theory that has been used for implementing thesolution and the limitations and simplifications that has been made aboutthe problem.

Chapter 4 describes the implementation of the metaheuristic.

Chapter 5 presents the results of the measurements and tests done.

Chapter 6 presents the possible improvements and conclusions of the work donein this thesis.

Chapter 2

Theoretical Background ofGSM

In this chapter, a general overview will be given in chapter 2.1 and the chaptersafterwards describes the technical details that are central in this thesis. As EDGEis a result of the evolution of GSM, it is necessary to briefly describe the basicsof it’s predecessors. Since they all are complex technologies, this also means thatthere are quite a lot of things that will not be mentioned about them, mainly dueto it’s lack of relevance in this thesis, and some things will be saved to chapter 2.6.The focus will be on the effective user data-rates, since that’s the focus of thisthesis. But first the general overview of GSM.

2.1 General

GSM is an abbreviation for Global System for Mobile communication, and thespecification is maintained and developed by the 3 rd Generation PartnershipProject (3GPP) and is often referred to as the second generation mobile telephony,2G.

One might question why a further development of GSM is needed in the pres-ence of third generation (3G) networks like UMTS. The GSM system is today themost widely spread mobile telephony technology, with over 3 billion users spreadover 219 countries and 80% of the world’s mobile subscriptions1, accoring to GSMAssocitation [1] and WCIS [3]. This means that the market for improvements ofGSM is huge.

1These figures can be compared to the third generation technology UMTS, which has about380 million subscribers in 105 countries [2] and 5% of the worlds mobile subscriptions.

3

4 Theoretical Background of GSM

2.2 Data MappingThis brief summary will not dig deep into data mapping and the different frames,and the reader may find a more in depth explanation in [6, Ch. 15.2.1].

GSM uses Time Division Multiplex together with Frequency Division Duplexfor dividing the channel into several sub-channels. There is a hierarchy of differentframes, varying in size. This explanation will start from the level of the conven-tional frame, which has a duration of 4.615 msec and is divided into eight slots.This gives each slot a duration of 577 µsec. One slot fits exactly one burst, whichis a short RF-transmission. Each burst contains training-, tail-, flag-, guard- anddata-symbols. The interesting part is the data symbols. One burst can carry 2×57information symbols. Have a look at figure 2.1

Frame = 4.6155 msec

Data

(57 symbols)

Data

(57 symbols)

Slot / Burst = 577 µsec

1 2 3 4 5 6 7 8

Figure 2.1. Data Mapping (Empty fields in the burst are mentioned in the text)

The data consists of coded user data, coded and uncoded control bits andtogether they form a radio block. The total size of a radio block is counted insymbols, and is always 456 symbols. This means that one radio block needs fourbursts to be transmitted. The radio block is also interleaved over these four bursts,to counteract burst errors. The relative size of user and control data depends onmodulation and coding used. User data is divided into one or two Radio LinkControl (RLC) blocks, also depending on modulation and coding used.

2.3 ModulationIn the beginning, GSM used only Gaussian Minimum Shift Keying (GMSK) as themodulation method. In GMSK, each bit is represented by a phase-shift relative

2.4 The Evolution of GSM Data Transmission 5

to the previous symbol; a phase-shift of +90 ◦ represents ’1’ and a phase-shift of−90 ◦ ’0’. This means that it is not the absolute position in the phase diagramthat defines the bit, but the relative phase change from the previous position (orsymbol). One symbol therefore carries one bit. An illustration of the symbol canbe seen in figure 2.2.

1

0

Q

I

Figure 2.2. GMSK Modulation

The reason for using GMSK was that it fulfilled the need for data speedat that time and is a quite simple modulation technique, which gives low-costtransceivers [6, Ch. 15.2.1].

2.4 The Evolution of GSM Data Transmission

2.4.1 GSM Phase 1The first version of GSM had no intention of giving high throughput of data, butrather to make the system work in practice. As the system was digital, it wasfairly simple to also implement a data service. Keeping what has been said inchapter 2.2 in mind, the reason for the throughput achieved can be analyzed.

Typically, each user is allocated one slot each within each frame and maytherefore send and receive 114 symbols of data every 577 µsec. Since GMSK isused as modulation, one symbol corresponds to one bit. Every 13:th slot is reservedfor control traffic and this gives a data rate of 114×(12÷13)÷4.615 msec = 22.8kbit/s. Adding error-correction and error-detection, we end up in a user data rateof 9.6 kbit/s. This is true for the first GSM standard, which was commerciallyavailable in 1991.

2.4.2 GSM Phase 2+There was a need for higher throughput as use of Internet access through mobiledevices increased. In GSM Phase 2+, a more aggressive convolutional codingpuncturing was implemented, making it possible to fit more information bits into


each slot (burst), and thereby increasing the data rate to 14.4 kbit/s/slot [5, Ch.17.1]. As another part of GSM Phase 2+, High-Speed Circuit-Switched Data wasspecified, allowing for a Mobile Station to access up to four time-slots in a framewhich increased the maximum data rate up to 57.6 kbit/s [6, Ch. 15.2.1].

2.4.3 General Packet Radio ServiceThe next step in improving the throughput was General Package Radio Service(GPRS), often called 2.5G. The earlier techniques mentioned above are all circuit-switched services, which means that each user is assigned a data traffic channel thatcannot be shared with other users and is held until the user ends it’s session (untilthe call is terminated). This essentially corresponds to using a dial-up modem ina land-line connection. When using the connection for Internet access, this is farfrom optimal utilization of the wireless network capacity since use of Internet isbursty. The channel is often idle, waiting for the user to utilize the channel, andthe capacity may not be enough when the user is active. A more flexible solutionwas needed.

GPRS introduces packet-switched data transmission, which means that thechannel is accessed only when packets are transmitted. It is then released foruse by other users’s packet transmissions. GPRS also introduces different codingsystems, which makes it possible to adapt the channel coding to the current channelcondition. The idea of adaptive channel coding was further developed in EDGE.Two new nodes has to be added in the core network if an operator wants tointroduce GPRS in a GSM network. GPRS still uses GMSK modulation.

2.5 The GSM NetworkFigure 2.3 is an overview of a GSM/GPRS/EDGE network. It should be stressedthat this is a strongly simplified picture with the purpose of presenting only theparts that are relevant in this thesis.

A GSM network consists of several nodes. To begin with, we have the MobileStation (MS) or the user, which in practice most often is a mobile cellular phone.The MS is connected to a Base Transceiver Station (BTS), often seen on hills orroofs. The air interface between them is divided into downlink and uplink. Thedownlink is the traffic from the BTS to the MS, and the uplink is the traffic fromthe MS to the BTS. The BTS does the channel coding before sending the signalover the air. As the user moves (may be traveling in a car), the MS changes whichcell it is connected to, called cell-reselection2.

The different BTSs communicates with a Base Station Controller (BSC), whichsorts out circuit switched traffic (speech) to the MSC and the data traffic to theSGSN. As the nodes from the Mobile Switching Center MSC and further arerelated to circuit switched traffic, they are of no interest for us.

2The reader might be familiar with the “hand-over”-procedure, which is the term for the cor-responding procedure done when the mobile is in circuit switched mode (speech). Cell-reselectionis the term used when the mobile is in packet transfer (data) or idle mode.

2.6 Enhanced Data for Global Evolution 7

BSC

MSC

SGSN

GGSN

BTS

BTS

Internet (ISP)

Uplink

Downlink

Figure 2.3. A GSM/GPRS/EDGE Network

SGSN is an acronym for Serving GPRS Support Node and it was added to theGSM network when GPRS was introduced. It is fully dedicated to data traffic andperforms encryption, attachment and detachment of MSs, authenticates MSs andfetches subscriber data. It also routes the traffic between the BSC and the GGSN.

The Gateway GPRS Support Node (GGSN) is an interface between the Inter-net (or other external packet network) and GSM network. It stores informationneeded to route incoming data packets to the SGSN. The GGSN can be connectedto several SGSNs, and a SGSN can be connected to several GGSNs.

More about the GSM-network may be found in [5].

2.6 Enhanced Data for Global EvolutionAs the use of multimedia application continued to increase, the need for higherthroughput continued to grow. The third generation (3G) mobile telephony offersa significant increase in throughput but at a financial cost. WCDMA is a totallydifferent system from GSM, and therefore requires huge investments by the op-erators. An upgrade of the existing GSM/GPRS-network to an EDGE networkcan be done at a fraction of the cost of introducing WCDMA. The operators donot need to acquire any new frequency licenses to implement EDGE, but only


to do software upgrades of their existing GSM/GPRS-networks in order to getEDGE-capability. This is why EDGE is a cost-efficient upgrade.

As GPRS is called 2.5G and EDGE is considered as a bridge between GPRSand 3G, EDGE is sometimes called 2.75G. The physical channel organization is thesame as conventional GSM/GPRS, and therefore EDGE can operate seamlesslytogether with conventional GSM. In the same time, the first standard of EDGEoffers a theoretical maximum throughput of up to 473.8 kbit/s.

EDGE includes both packet- and circuit-switched data transmission, calledEnhanced GPRS (EGPRS) and Enhanced Circuit Switched Data (ECSD) respec-tively [7, p. 58]. But as packet-switched data transmission is much more efficient,EGPRS is the technique that has been implemented by the operators and hasalmost become synonymous to EDGE.

The two main differences of EGPRS compared to GPRS is the introductionof 8-PSK modulation and possibility to do continuous modifications of the radiolink control to improve link transmission quality in a more dynamic way than inGPRS.

2.6.1 The Modulation in EDGEOne GMSK symbol contains one bit, whereas one 8-PSK symbol contains 3 bits.The bit-throughput can thus theoretically be increased three-fold by using 8-PSK.The downside is that the euclidean distance between each signal point is decreasedin 8-PSK. Increasing the transmit power of the 8-PSK transmissions would be asolution, but as 8-PSK needs to follow the same regulations as GMSK, that cannotbe done. A partial solution has been made using Gray coding. The constellationdiagram of 8-PSK can be seen in figure 2.4.

Q

I

(1,1,1)

(0,1,1)

(0,1,0)

(0,0,0)

(0,0,1)

(1,0,1)

(1,0,0)

(1,1,0)

Figure 2.4. 8-PSK Modulation

EDGE has the capability of using both GMSK and 8-PSK as modulation,depending on the channel conditions. GMSK is more robust than 8-PSK, butcannot gain from an improved data link quality to the same extent as 8-PSK does.Therefore 8-PSK is more often used in good channel conditions. The procedure ofchanging modulation and coding is the topic of chapter 2.6.2.


2.6.2 Link Quality Control in EDGEEGPRS Link Quality Control (LQC) is a way of dynamically try to choose theoptimal modulation and coding scheme for transmission of data over the radiointerface, depending on the current radio conditions. LQC is Ericsson’s solutionof the framework given by the 3GPP standards of coding, modulation and how theperformance of a transmission is measured. (EGPRS) LQC aims at maximizingthe data throughput on the radio link while keeping the latency low. There aremainly two advantages of using LQC [8, Ch. 2]:

Higher bit rates is achieved by selecting the most optimal modulation and cod-ing scheme for transmission of data.

Increased system capacity is achieved since each transmission (given a specificamount of data) takes less time, which means that more users can be servedwithin the same time.

The algorithm of LQC is implemented in the BSC. A deeper presentation of theMCSs will be given next, since it is a central part of this thesis.

The Modulation and Coding Schemes

EGPRS uses three different coding families, called family A, B and C. By choosinga combination of these together with GMSK or 8-PSK modulation, nine differentModulation and Coding Schemes (MCS) has been specified, listed in table 2.1.

MCS CodingRate

Modulation CodingFamily

RLC data blocksize (bits)

RLC data bitrate (kbit/s)

MCS-1 0.53 GMSK C 176 8.8MCS-2 0.66 GMSK B 224 11.2MCS-3 0.85 GMSK A 296 14.8MCS-4 1.0 GMSK C 2×176 17.6MCS-5 0.37 8-PSK B 2×224 22.4MCS-6 0.49 8-PSK A 2×296 29.6MCS-7 0.76 8-PSK B 4×224 44.8MCS-8 0.92 8-PSK A 4×272 54.4MCS-9 1.0 8-PSK A 4×296 59.2

Table 2.1. The MCSs in EGPRS

Lower MCSs have high amounts of channel coding, giving lower data rates,and vice versa. The maximum data rate for the GMSK based MCS’s is in generalreached at lower radio link quality than for the 8-PSK based MCS.

What is common within each family is that the payload (user data) unitshas the same number of bits; 176, 224, (272) or 296. What differs each familymember is the number of payload units carried within; 1, 2 or 4. This makes itpossible to use different coding rates within a coding family. The coding familiesare illustrated in table 2.2.


Family A296 bits 296 bits 296 bits 296 bits

MCS-3 -

MCS-6 -

MCS-8 -

MCS-9 -

Family B224 bits 224 bits 224 bits 224 bits

MCS-2 -

MCS-5 -

MCS-7 -

Family C176 bits 176 bits

MCS-1 -

MCS-4 -

Table 2.2. The Different Coding Families

The idea of using different coding families is to make it possible to resenderroneous packets with a lower MCS. As the MCS might be changed every 100ms,this prevents getting trapped in one MCS that does not offer enough robust codingin order to make a successful transmission during several attempts, which otherwisewould be the case if the channel is changing rapidly. In order to be able to resenddata using another MCS, the size of the RLC blocks (see ch. 2.2) of the MCSsneeds to be the same, which is the case within each family [9, ch 6.6.4.5.2].

The following example is given with the purpose of making things clearer: Atransmission using MCS-9 means that one radio block has been transmitted, whichcontains two RLC blocks, each containing 2 payloads of 296 bits of user data. Soour transmission contains 4×296 bits of data. If this transmission is erroneous, itmay be resent with either MCS-9 again, or MCS-6 using two radio blocks, eachcontaining one RLC block of size 2×296 bits. The third alternative is to resend thedata with MCS-3 using four radio blocks of one RLC block and 1×296 bits each.If we use MCS-3 and thereby use four radio blocks to send the same amount ofdata as carried within one MCS-9 block, this means that we have a throughput of14 of the MCS-9 throughput. This is in agreement with the calculation 59.2

4 = 14.8kbit/s.3

The reason why MCS-8 is not an integer of the payload unit of 296 bits, butinstead 272 bits is to gain one extra MCS. With the implementation of MCS-8there is an increased possibility to achieve a higher throughput. When switchingfrom MCS-8 down to either MCS-6 or MCS-3, padding is performed in order toachieve the payload unit size of 296 bits. This means that a retransmission ofa MCS-8 block still can be made with two MCS-6 or four MCS-3 blocks. The

3All of these calculations are purely theoretic, as we in practice have a varying error rate thataffects the throughput.


throughput is decreased since a part of the block consists of padding instead ofuser-payload. In practice, this has an inconsiderable effect on the throughput. Adeeper description of the different MCSs is found in [9, Ch. 6.5.5.1.2] and [12, Ch.7.2.2.3].

The decision of which MCS is to be used is made on the basis of the meanBit Error Probability (BEP) and the mean of the variation of the BEP. This ismeasured over one radio block, which means four bursts (see chapter 2.2) accordingto equation 2.1 and 2.2 [11, Ch. 8.2.3.2]. The measurement is done by the MS onthe downlink and the BTS on the uplink.

MEAN_BEPblock = 14

4∑i=1

BEPburst i (2.1)

CV_BEPblock =

√13

∑4k=1(BEPburst k − 1

4∑4

i=1 BEPburst i)2

14

∑4i=1 BEPburst i

(2.2)

When MEAN_BEPblock and CV_BEPblock are obtained, these values are thencalculated by averaging and filtering them in the linear domain. CV_BEP iscreated from that calculated value by a final quantization, giving it a range of0 − 7. However, before quantization of the calculated value that will result inCV_BEP, it is put in a logarithmic scale. This gives MEAN_BEP a range of0 − 31, where low values means high BEP (due to the logarithm). A high valueof CV_BEP means a larger spread of the MEAN_BEP values. These values arealso affected by factors like time dispersion and interleaving, caused by velocityand frequency hopping [8, ch. 3.1]. The exact mapping may be found in [11, ch.8.2.5].

Based on the values of MEAN_BEP and CV_BEP the LQC algorithm decideswhich MCS that should use with the help of a table with 32 × 8 cells. Thetable specifies which MCS that gives the best throughput and latency for eachcombination of MEAN_BEP and CV_BEP and has been empirically generated.An example of what a possible table could look like is given in table 2.3.

During a transmission of data to/from a BSC from/to a MS, the BEP is reg-ularly measured. From these measurements, MEAN_BEP and CV_BEP is cal-culated. The BSC uses such a table as table 2.3 for deciding which MCS that theBTS shall use for transmitting the next blocks. The table-concept is implied bythe 3GPP-standard and an example is given in [11, Annex D]. The table-conceptis central in this thesis and will be further explained in the end of next chapter.

2.6.3 Improved ACK/NACK in Evolved EDGEThe next upcoming standard of GSM is Evolved EDGE4. It consist of severalenhancement compared to EDGE, which improves both throughput and latencyamong other things. The interested reader may find all the objectives of evolvedEDGE in [15, Ch. 4.2]. One specific feature of Evolved EDGE was chosen to

4Following the convention of calling it xG, where x is the mean of the latest standard and3(G), this would be called (2.75G+3G)/2 = 2.875G. This has not been adopted for some reason.


0 1 2 3 4 5 6 70 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-11 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-12 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-13 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-14 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-1 MCS-15 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-26 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-27 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-28 MCS-3 MCS-3 MCS-2 MCS-2 MCS-2 MCS-2 MCS-2 MCS-29 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-2 MCS-2 MCS-210 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-311 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-312 MCS-4 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-3 MCS-313 MCS-4 MCS-4 MCS-4 MCS-4 MCS-4 MCS-4 MCS-4 MCS-414 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-515 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-516 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-517 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-518 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-519 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-620 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-621 MCS-6 MCS-6 MCS-7 MCS-6 MCS-6 MCS-6 MCS-6 MCS-622 MCS-6 MCS-6 MCS-7 MCS-7 MCS-6 MCS-6 MCS-6 MCS-623 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-8 MCS-824 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-8 MCS-8 MCS-825 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-826 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-827 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-828 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-8 MCS-829 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-8 MCS-830 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-931 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9

Table 2.3. Example of a table. X-axis: CV_BEP, Y-axis: MEAN_BEP (Note: thistable is only an example and not a part of Ericsson’s LQC algorithm.)


be the subject of optimization of this thesis, namely Improved Acknowledged/NotAcknowledged (IAN). For this reason, this and only this feature will be given abrief description.

The reader is assumed to be familiar with the principle of acknowledgmentin packet-based communication systems. In EDGE, the BTS regularly polls theMS for acknowledged/not acknowledged (ACK/NACK) RLC blocks. With IAN,the MS (the receiver) may report missing data blocks immediately by includingthe ACK/NACK in any sent RLC data block in what is called a Piggy-backedACK/NACK (PAN). As there is no spare room for extra PAN bits in the RLCblock, it must be made. This is done by increasing the coding rate for each MCS,which inevitably means that there will be a lower immunity against noise. As acoding rate cannot be above 1.0, this means that MCS-4 and MCS-9 cannot containa PAN. This change is summarized in table 2.4, which is similar to table 2.1. Thedifferences are displayed in parenthesis. Details may be found in [9, ch. 6.5.5.1.2]

MCS CodingRate

Modulation CodingFamily

RLC data blocksize (bits)

RLC data bitrate (kbit/s)

MCS-1 0.60(0.53)

GMSK C 176 8.8

MCS-2 0.75(0.66)

GMSK B 224 11.2

MCS-3 0.96(0.85)

GMSK A 296 14.8

MCS-4 n/a(1.0)

n/a(GMSK)

n/a(C)

n/a(2×176)

n/a(17.6)

MCS-5 0.40(0.37)

8-PSK B 2×224 22.4

MCS-6 0.52(0.49)

8-PSK A 2×296 29.6

MCS-7 0.81(0.76)

8-PSK B 4×224 44.8

MCS-8 0.98(0.92)

8-PSK A 4×272 54.4

MCS-9 n/a(1.0)

n/a(8-PSK)

n/a(A)

n/a(4×296)

n/a(59.2)

Table 2.4. The EGPRS MCSs with PAN.

Using this technique, there will be no dedicated ACK/NACK blocks, whichmeans less control-traffic and thereby a higher user-data throughput. The higherthroughput is somewhat compensated by the fact that MCS-4 and MCS-9 cannotbe used. The effect PAN has on the choice of MCSs will be discussed in chapter3.6.3, number 6. In an overall perspective the PAN-technique may increase thethroughput about five to ten percent. More about IAN and PAN may be foundin [15, Ch. 10.2].


The PAN-standard implies that we use separate MCS-tables depending onwhether we have a PAN or not. This means that the choice of which table is tobe used, depends on the current state of the MS. Another example of a state-dependent table is whether we use GMSK or 8PSK modulation. In addition tothis, we also have different tables for uplink and downlink and a number of otherGSM-standards that uses other forms of LQC which needs several other tables.As we see, the number of tables used within GSM grows rapidly.

Other Parameters

Associated with the tables are a number of parameters that are part of the LQCalgorithm and these also affects the performance of the data transmission. Someof these are defined in the 3GPP-standards, while some originates from Ericsson’sLQC algorithm. Not all of these are subject to optimization as they have no effectof the throughput or needs to be set to a specific value for correct operation, andhence not mentioned here. The parameters will be described to a minimum, asthe parameters are part of the LQC algorithm, which is subject to confidentiality.The parameters that are subject to optimization in this thesis are summarized intable 2.5.

Parameter Min value Max Value Expected optimumP1 0 14 10-14P2 0 10000 -P3 0 10000 2000-4000P4 0 100 0-20

Table 2.5. Some of the parameters of the LQC-algorithm

Those persons who are versed of the LQC algorithm have gained a knowledgeabout these parameters and has an expectation about where to find the optimumof the parameters. “Expected optimum” in the table refers to those expectations.These parameters are only examples, and there are a lot more of other parametersincluded in the LQC algorithm.

The term “network parameters” will be used later on when referring to boththe tables and other parameters in the LQC algorithm. “Other parameters” willoften be called just “parameters”.

Chapter 3

Problem and SolutionDescription

3.1 The Underlying ProblemThe purpose of LQC is to maximize the performance of the transmission over theradio link during fading and interference, as stated in chapter 2.6.2. The 3GPPstandards give the tools for handling the radio link, and from there has Ericssonderived the LQC algorithm. The values of the network parameters are static onceset, but are not possible to derive from the 3GPP standards, although the LQCalgorithm is highly reliant on them. Determining values of the network parametersthat just makes the LQC algorithm operational is not a problem. The problem isto find values that give optimum performance. Performance may be measured interms of end-user throughput, latency and GSM network capacity. The number ofnetwork parameters are huge, as seen in chapter 2.6.3. Different subsets of theseform optimization problems of different parts of the LQC algorithm.

It is not possible to calculate the optimal values of the network parameters, nei-ther analytical nor numerical. The reason is that there is no model that describesthe changes of the quality of the radio link and other network characteristics ina way that makes it possible to predict which MCS that will be the optimal ata specific time. For these reasons, the determination of their optimal values isdifficult.

The outline of this thesis originates from this problem. A solution to theproblem already exists, but the problem of this thesis is to implement anothersolution.

3.2 The Existing SolutionToday, the determination of the network parameters is mainly a manual proce-dure, where a person called operator sets a set of network parameters, performsa measurement of the performance, obtains the result and analyzes the perfor-

15

16 Problem and Solution Description

mance. From that analysis the operator decides which parameters that should beadjusted and to which values in order to get closer to the optimum. This is doneuntil the operator thinks that an optimum has been found, or that the performanceis sufficient. An overview of the process may be found in figure 3.1.

Perform measurement(s)Adjust parameters Obtain measurement values

Operator

Figure 3.1. An overview of the manual optimization process.

There are a number of problems associated with this solution:

No common approach There is no official method or guideline describing howthis process should be done in practice. The decision of adjusting net-work parameters is made on the basis of what the operator thinks will helphim/her to find an optimum, and is mainly built on experience and con-jectures. Therefore the found optimal network parameters may depend onwho is performing the optimization. It would be preferable if one commonapproach existed to solving the problems.

Cost The main problem with today’s solution is the time-aspect. The number ofoptimization problems is large and the process of determining the parametervalues is difficult and laborious and therefore time consuming. In projectswhere tuning of different parts of the network parameters has been donepreviously, it has taken about 700 man-hours to perform (two persons work-ing full-time for two months) [14, Ch. 7.1.9.5]. This does of course imply aconsiderable cost.

Usage of shared resources The optimization requires access to hardware thatis limited in availability as it is shared with others. The time-aspect meansthat the optimization requires a substantial amount of access to these re-sources. The existing solution is mainly a manual process, which meansthat the resources are used during office-hours when the availability of therecourses are limited.

Sub-optimal It is a heuristic method, and as a consequence the optimizationresults may be sub-optimal which means that the performance will not beas good as possible. There might be better optima(s), but they will not bedramatically better, as we know that there is a theoretical upper limit of theperformance. At best, the difference between the theoretical limit and thepractical limit could be reduced if an alternative solution was used. Thereare thoughts within Ericsson that the IAN-feature may have an optimum

3.3 General Description of the New Solution 17

that gives up to ten percent better throughput compared to the optimumthat is used today.

3.3 General Description of the New SolutionAn alternative to the existing solution is to use an Automation Framework (AF)that utilizes metaheuristic search in a way that is often called search based test-ing. More about search based testing is found in [16]. Search based testing ispopular to use within the industry and will in this thesis be used for searchingfor optimum values of the network parameters. The AF consists of an AutomatedPerformance Optimization Algorithm (APOA), hardware and software for per-forming measurements together with software for linking the AFs different partstogether. The APOA consists of a metaheuristic (explained in chapter 3.5.1) andsoftware for utilizing the different parts of the AF. As most of the parts of the AFalready exist, the main time of this thesis has been spent on linking them togetherand implementing the APOA. The target of this thesis is to implement the AFand APOA and to verify that the concept is working.

The APOA is part of the AF, but in practice in this thesis there will be noclear separation between them as they both will be developed simultaneously.The reason for introducing two separate notions instead of one will be clarified inchapter 6.1.5.

The main difference between the existing solution and the new one is the re-placement of the operator with an optimization algorithm (metaheuristic). Theloop that figure 3.1 make up corresponds to the inner loop of figure 3.2. TheAPOA can be summarized in a number of different conceptual states:

Perform measurement(s)

Adjust parameter(s)

Obtain measurement value(s)

Optimization algorithm

Initialization

Termination

Optimum found

Figure 3.2. An overview of the APOA.


Initialization An operator configures the optimization algorithm to solve a spec-ified problem. A varying amount of configuration is needed in order to solvethe problem efficiently, but it does at least include a definition of whichnetwork parameters that are subject to optimization.

Optimization algorithm The optimization algorithm can be any of those com-monly used within metaheuristics. It takes the measured performance asinput, evaluates it, and outputs a new set of network parameters.

Adjust parameter(s) The optimization algorithm selects new values of some orall network parameters that are included in the optimization and initiates achange of them.

Perform measurement(s) For measuring the performance of the current set ofnetwork parameters, a framework for doing so is needed. Such a frameworkexists at Ericsson and will be described in chapter 3.4. Several measurementsmight be needed in order to evaluate the performance.

Obtain measurement value(s) The software that is executing the measure-ments returns the obtained performance values. Some performance param-eters may need to be post-processed before handed over to the optimizationalgorithm.

Termination The optimization algorithm has to be terminated at some point andthere might be multiple reasons for doing so. More about the terminationin chapter 3.5.1.

Optima found When the optimization algorithm has been terminated, the APOAreturns the best set of network parameters found.

3.4 The Test Environment - THC 19

3.4 The Test Environment - THCAt the Integration and Verification department at Ericsson, there is a test envi-ronment called Test Harness Core (THC) used for testing the hardware in such arealistic way as possible. THC is a prerequisite of this thesis as a essential part ofthe AF. THC mimics the characteristics of a real-world GSM/EDGE network andcan execute several performance tests during pre-determined conditions in an au-tomatic but deterministic fashion. THC can be used to measure the performanceof a set of network parameters, but to measure another set, manual configurationis required.

3.4.1 The SetupAs the reader may not be familiar with THC, it will be given a presentation inthis chapter. The relevant hardware are illustrated in figure 3.3.

BSC

SGSN

GGSN

BTS

Internet (ISP)

THC-ServerPC

Mobile (MS)

Server

BTSFading

THC-Client

Figure 3.3. The hardware of THC.

The heart of THC is the THC-server which is controlled by an operator via aTHC-client. The THC-server is where most of the software that performs tests isexecuted and is therefore connected to all the hardware that needs to be controlledby the AF or APOA.

The PC is an ordinary PC that is connected to a mobile, which togetherrepresents the user. The THC-server is connected to the PC in order to executedifferent scripts on the PC, which performs tasks for measuring the end-user-performance. It may be pinging servers, loading web-pages, receiving mails andin this thesis it’s exemplified by getting files from a FTP-server. The PC also has


protocol loggers and other software for monitoring the PC in order to measure theuser-performance. One reason for why not just a mobile phone is used to representthe user is since it is easier to write such programs and install monitoring programson a PC than on a mobile. The other reason is that it makes it easy to switchbetween different mobiles in order to perform test with mobiles supporting differentGSM standards, without the need for new software. No reconfiguration of the PCis needed except for establishing a connection with the new mobile.

The most significant difference of THC and the real world is the radio link.It would not be practically possible to connect all the mobiles in the labs atEricsson to the different BTSs via an air-interface, and therefore are cables usedinstead. Cables constitute an ideal radio link, compared to an air-interface andas optimization over an ideal radio link is trivial (always use MCS-9), it has beennecessary to simulate fading in order to make the optimization problem morecomplex and realistic. The hardware used for simulating fading was a SpirentSR5500, which used a model called “TU3” as specified by 3GPP. The model usesRayleigh fading with a simulated velocity of the mobile of 3 km/h. Both phase andfrequency shift was 0, and the Doppler shift was 2.6Hz. The Carrier to Interferencelevel (C/I) has been kept constant at -15dB. TU3 is an abbreviation for “TypicallyUrban” and “3” as in the velocity of the MS. More about TU3 is found in [10].

From the fading, there is a connection to the BTS, which in turn is connectedto the BSC. The BSC is the hardware that contains the LQC algorithm with thetables and parameters, and therefore there is a need for communicating with it.SGSN and GGSN are part of the traffic flow, but not in focus in this thesis. TheGGSN is connected to Internet, just like the server. This server is the one thatthe PC will get the files from with the help of the FTP-protocol and through allof the network hardware. It could also be used for latency tests (pinging).

3.4.2 Operation of THCIn comparison with figure 3.2, but with THC as the center viewpoint, the APOAwill basically be executing in the following way:

1. The operator starts THC with a specific command for running the APOA.

2. The THC-server initiates all necessary hardware and the APOA.

3. The THC-server commands the PC to get a file from the FTP-server andthe transfer is started.

4. After the download is complete, the average throughput will be calculatedusing the monitoring software on the PC.

5. This throughput will be reported back to the THC-server and the optimiza-tion algorithm (in the APOA).

6. The optimization algorithm uses this information to calculate new parame-ters for the next iterations.

7. New network parameters are written into the BSC by the APOA.

3.4 The Test Environment - THC 21

8. The whole procedure is repeated from step 3 until the optimization is ter-minated.

This is also basically the procedure that has been done when hand-tuning hasbeen made, but with the optimization algorithm replaced by the operator.


3.5 MetaheuristicsThis chapter describes the problem from a theoretical point of view. The readermight already have noticed that the problem is combinatorial since the values ofthe network parameters are discrete. Each cell in each table and the parameterstogether make up the variables of the optimization problem. The variables are allintegers that are restricted in range, e.g. the table-variables has a range of (MCS)1-9, and the parameter-variables has individually specified ranges (see chapter 2.6.3).

To understand the concept of metaheuristics, one has to know the concept ofheuristics. Heuriskein is Greek and means to find or discover and is the origin ofthe word heuristic. Heuristics is defined by [18, ch. 1.3], as:

“Heuristics is a technique for seeking good (i.e. near optimal) solutions at areasonable computation cost, without being able to guarantee either feasibility oroptimality, or even to state how close to optimality a particular found solution is.”

Many heuristics incorporate problem specific knowledge beyond the knowledgefound in the problem definition in order to reach a trade-off between solutionquality and computation complexity. This means that a successful heuristic thatis suitable for one problem may not be so for another problem.

What is special about the problems (of the LQC algorithm) in this thesis, isthat they consist of several different types of variables, and the relation betweenthem varies. To illustrate this varying dependence, consider the following example:The value of the cell corresponding to MEAN_BEP = 28 and CV_BEP = 2 ismore likely to be of the same value as the cell corresponding to MEAN_BEP= 28 and CV_BEP = 3, than of the cell corresponding to MEAN_BEP = 4and CV_BEP = 7. Apart from that is there a totally different and much weakerrelation between any table-variable and any of the parameters.

Different parts of the LQC algorithm results in different optimization problems,as mentioned earlier in chapter 3.1. These problems can be seen as a class ofproblems, which are all similar, but has small differences such as included networkparameters and performance parameters. The class of problems can with greatcertainty be stated to be unique, and thus is there no existing problem-specificheuristic algorithm suitable for these problems.1 A more general algorithm whichcan be applied to this class of problems is needed, a metaheuristic.

Metaheuristics are a form of general heuristic methods which are applicable toa wide range of combinatorial problems. There is no commonly agreed definitionof metaheuristics, as pointed out by The Metaheuristic Network [17], but they usethe following definition:

“A metaheuristic is a set of concepts that can be used to define heuristic meth-ods that can be applied to a wide set of different problems. In other words, ametaheuristic can be seen as a general algorithmic framework which can be appliedto different optimization problems with relatively few modifications to make themadapted to a specific problem.”

This definition is further clarified by [19, Ch. 1]: The goal of metaheuris-tics is to efficiently explore the search space using approximate and usually non-

1One exception is complete enumeration, where all possible solutions are evaluated and thebest one picked. This is of course impossible to do in practice.

3.5 Metaheuristics 23

deterministic methods with some form of embodied memory in order to guide thesearch towards (near-)optimal solutions. Metaheuristics are not problem-specific,apart from many heuristic methods, but may make use of domain-specific knowl-edge in the form of heuristics that are controlled by the upper level strategy. Anextensive overview of metaheuristics in combinatorial optimization is found in [19].

There are several different metaheuristics existing today, some examples aresimulated annealing, tabu search, iterated local search, evolutionary algorithmsand ant colony optimization. A decision of which one of them to use in this thesishad to be made.

Figuring out which metaheuristic that solves the class of problems best is notan easy task, and there are probably several metaheuristics that would performalmost equally good. Although there are differences in efficiency, the over-allefficiency is dependent on how the problem is simplified and how sophisticatedthe implementation of the metaheuristic is. In addition to this, is the time theAPOA spends on executing the metaheuristic negligible relative to the total timeof the APOA. Choosing the best metaheuristic would require to actually test allof them on this particular class of problems, and that is not an alternative due tothe limited gain of doing so. Therefore has a strictly limited time been spent onchoosing and motivating the use of this optimization algorithm, and the focus willbe on the implementation of it.

Together with Torbjörn Larsson, professor at the Division of Optimization atLinköping University, I’ve chosen to use Genetic Algorithms (GA) as optimizationmethod for this problem. GA are sometimes mentioned to be a little bit moreefficient in dealing with uncertainties of the evaluation function compared to oth-ers. We will see that the uncertainty is a problem in chapter 5. This is althoughnothing that I have found to be motivated by theory or scientific studies2, butrather seems to originate from a conclusion based on experience.

A conceptual and succinct description of GA from the viewpoint of how it hasbeen implemented in this thesis follows next.

3.5.1 Genetic AlgorithmsGA are part of a group of certain heuristic techniques based on the principle ofnatural evolution, called evolutionary algorithms or evolutionary computing. GAis the technique within this group that is most often used for solving combinatorialoptimization problems.

GA are inspired by nature’s capability to evolve living beings well adaptedto their environment. GA are a population-based metaheuristic, meaning thatevery iteration of the algorithm deals with a set of solutions, a population. Thepopulation is made out of individuals. An individual is one solution to the givenproblem and in this thesis it corresponds to a set of network parameters. Fromhere on, a set of network parameters will be called an individual when speakingin GA-terms. An individual is made up of genetic material, which corresponds tothe variables of a solution (network parameters).

2The field of research within metaheuristics is still quite young.


An individual has a fitness which tells the quality of an individual. The fitnessis based on evaluating a target function, which in this case is the performancemeasured THC (see chapter 3.4). The measurement in THC gives a raw score(e.g. throughput in kbit/s) and the fitness of an individual is calculated by com-paring the individual’s raw score with the raw score of the other individuals in thepopulation. More details about this will be given in chapter 4.4.

Crossover is the process of combining individuals to produce new individuals.The individuals that are subject of crossover are naturally called parents, and thenew individuals offspring. The hope is that the offspring will get the best geneticmaterial from each parent, making the offspring even better than their parents.The crossover is, alike in nature, not a deterministic process and involves a degreeof randomness which means that the offspring of the same parents are not exactlyequal. The number of parents does not need to be two, but in this thesis it hasbeen chosen so. The same holds for the number of offspring. The reason is that itis required by the idea behind the implementation.

When two parents are crossed, they do not need to form a new individual. Analternative is that the offspring of the parents are exact copies of the parents. Thechance of this not happening is governed by a probability, a crossover rate.

Several couples of parents are crossed for each iteration of the GA and theiroffspring forms the population of the next iteration of the GA. A new populationis created for each iteration of the GA, and these populations are referred to asgenerations. In this thesis has the size of the population been chosen to be keptconstant, as it is the easiest to implement and follows from the idea behind theimplementation of the crossover. A variable population size is not necessarily abetter solution to GA problems.

The driving force of GA are the selection of parents based on their fitness.There are many ways of choosing the parents, but they most often rely on theprinciple that individuals with a higher fitness have a higher probability to bechosen as parents for the generation of new individuals. This corresponds to theprinciple of survival of the fittest in natural evolution. It is nature’s capability toadapt itself to a changing environment which gave the inspiration for GA.

An option often mentioned in descriptions of GA is to use elitism, which meansthat the best individual of a generation is inserted into the next generation. Thisleads to a faster convergence towards an optimum, but the optimum may be sub-optimal, and therefore is elitism not always preferable. More aspects of elitismwill be discussed in chapter 5.4.

The GA has to be completed with mutation in order not to converge intosub-optimal solution, which would be highly dependent on the initial population.New genetic material is added by the process of mutation, allowing the GA toexplore the search-space outside the area that the initial population makes up.Mutation diversifies the population, but it has to be limited in order to make theGA strive towards optima. The mutation is therefore governed by a mutation ratethat affects the probability of a mutation occurring and the amount of geneticmaterial that is mutated. The mutation rate stretches from zero to one, wherezero means no change of the values, and one means a maximum change of thevalues (meaning of "‘maximum"’ depends on implementation).

3.6 Limitations 25

The GA can be terminated for several reasons. A termination may occur ifa good enough individual has been found that satisfies a desired performance.Another reason might be that a limited number of iterations or generations havebeen made or that a time limit has been reached.

The algorithm for GA is given in in pseudo code 3.1.

P ← GenerateInitialPopulation()Evaluate(P )while termination criteria not met do

P ′ ← Combine(P )P ′′ ←Mutate(P ′)Evaluate(P ′′)P ← P ′′

endwhilePseudo Code 3.1: Algorithm for GA.

This is the basics of GA and there is a lot of literature written about GA thatgoes into more details. The interested reader may for example have a look at [17]and [18], which has been the inspirational sources for this chapter.

The reader might have noticed that this description is very broad and notproblem-specific, which is characteristic for metaheuristics. The next step is there-fore to adopt it on the given class of problems. This means figuring out and im-plementing how to calculate the fitness, how to perform crossover of individuals,how to mutate them and how to select them for crossover. Most of the time ofthis thesis has been spent on these activities.

3.6 LimitationsAs this thesis is limited in time some limitations has to be made in order to makeit practically possible to implement a APOA that could be used in practice. Evenbefore implementing something that could be used in practice, it is wise to makea ’Proof of concept’ which allows for even more simplifications to be made. Notuntil the concept is proven is there a point of actually implementing a practicallyusable APOA. By doing so, a number of different limitations has been made.

Chapter 3.6.1 lists the practical limitation of the setup and chapter 3.6.2 statesthe limitations in the optimization problem. Chapter 3.6.3 explains the simplifi-cations that have been made about the problem from a more theoretical point ofview, in order to make the implementation more efficient.

3.6.1 Limitations of the SetupA GSM-system is complex and it is therefore not possible to configure THC mimicall of the characteristics of a real-world GSM network within this limited time. Thefollowing limitations have been made in the setup:


One mobile The algorithms focus on optimizing the throughput of one singlemobile, which is the only one in the cell. One reason is that the configu-ration of one mobile is naturally less complicated than configuring multipleof them. Another reason is that there have been some stability issues withthe prototype mobile that supports IAN, and using multiple of them wouldfurther increase the instability of the system. When these problems are over-come, it should be fairly easy to apply the concept to multiple mobiles in acell. It is not even for certain the optimization has to be done using multiplemobile, as the LQC-algorithm operates individually on each MS.

No cell-reselection To simplify the configuration of the test environment duringthe development, the mobile has been stationary within one cell. The APOAis expected to perform equally good if cell-reselection was introduced, asthere is no direct relation between the LQC-algorithm and cell-reselection.

C/I The only fading parameter that is not a part of the TU3-model is the C/I andis therefore a subject to explanation. By choosing C/I = 15dB,MEAN_BEPmainly varied between 3 and 21 over the MEAN_BEP-scale. This spreadis fairly large, which means that the APOA has to handle a more complexoptimization problem as the optimal table probably contains several differentMCSs. A better alternative would have been to use a variable C/I, as itwould have been more realistic and caused an optimization over the wholetables. This has not been done at this stage, due to complex implementation.A variation of MEAN_BEP between 3 and 21 should still be large enoughfor proof of concept. Increasing the variation to include MEAN_BEP valuesfrom 0 to 31 should cause no problems with the implementation of the APOA,except for a fairly larger optimization problem.

Fading model There might be questions why TU3 has been chosen as the model,as different models mimic different environments. The relevant part in thisthesis is to use fading, the exact model is of minor matter. Therefore a modelthat is often used within Ericsson was chosen3.

Performance only measured by throughput Only FTP-transfers has beenused, which isn’t affected by latency to any considerable extent, but is animportant factor of time-critical applications such as IP-telephony. A so-lution would be to also include ping-tests and weigh the results togetherwith the results from the FTP-transfer. This has not been done partly dueto the implementation time, and partly due to the difficulty of weighingthe throughput and latency together in order to make a decision whetherthe combination of them is good or poor. The performance of the networkparameters is measured in terms of end-user-throughput, as explained inchapter 3.4.1.

3Within Ericsson, it is known that this model is not a perfect match to real-world scenarios.When the optimized LQC has been evaluated in field-networks, the expected gain has not beenseen. Despite this, there is currently no better model available that can be chosen.

3.6 Limitations 27

3.6.2 Limitations of the ProblemDifferent subsets of the network parameters form different optimization problems,as stated in chapter 3.1. One of these subsets has been chosen to exemplify theproblem described in chapter 3.1. This has been done as this thesis is not only atheoretical study, and has to be tested in practice.

Standard The IAN standard was chosen to be subject of optimization. One rea-son for choosing this specific feature is that it is similar to other optimizationproblems of the LQC algorithm. Other reasons exists, but are not mentioneddue to confidentiality.

Table Simultaneously optimizing over both down- and uplink theoretically takestwice the time compared to only optimizing one of them. The downlinkwas chosen as a subject of optimization, mostly due to an easier setup ofhardware for fading. The choice of MCS in the downlink in the IAN standardis governed by two GMSK-tables and two 8-PSK-tables. The 8-PSK tableswere chosen since it is somewhat more easy to optimize in practice (but notin theory). The reason for having two tables per modulation is that thereare separate tables if PAN is included or not. The table for packets withoutPAN was selected, as most of the packets do not contain a PAN.

Parameters Parameters are common in most of the problems that can be formedout of the LQC algorithm. Therefore it is necessary to prove that parametersand tables can be optimized simultaneously. The four parameters presentedin chapter 2.6.3 were included in the optimization and the reason why isconfidential.

3.6.3 Simplifications of the ProblemThe implementation needs to be adopted so that it can generate individuals in anintelligent way, but still general enough to be applicable to any part of the LQC-algorithm. Theoretical simplifications can be used in order to limit the search-landscape and are almost necessary for decent performance. If no simplificationscould be made, the number of different combinations of just one single table withnine possible MCSs in 8× 32 cells would be 98∗32 ≈ 10244. This is fortunately notthe case.

There are a number of characteristics known about the problems or the search-landscape; either characteristics that we can assume or characteristics that we arefairly sure about. Below are the simplifications that can be made about the theo-retical problem. They are given in descending order relative to a rough certaintyof the statement.

1. The first simplification is the most firm assumption and is followed by the as-sumption of ascending MCS values. AsMEAN_BEP increases, which meansthat the BEP decreases, the quality of the radio link gets better. This meansthat the same or a higher MCS should be used as MEAN_BEP increases.This simplification has a great impact on the number of combinations of onetable.


2. MSC-9 gives the highest throughput for high C/I, as assumed in [13, ch. 2].“High C/I” corresponds to MEAN_BEP ≤ 31.

3. There is a great probability that a MCS that is closer to the optimum MCSof cell is a better choice than a MCS far from the optimal MCS (e.g. if theoptimum MCS is MCS-7, MCS-6 would be better than MCS-9).

4. There is a great probability that the optimal MCS of a table-variable is equalto or close to the optimal MCS of a neighboring table-variable. An exampleof this was given in chapter 3.5.

5. There is a great probability that an optimal table utilizes all possible MCSs,as this improves the accuracy of the choice of modulation and coding.

6. As explained in chapter 2.6.3, blocks containing a PAN and coded with a spe-cific MCS are less robust against noise than blocks without PAN, but codedwith the same MCS. Given a combination of MEAN_BEP and CV_BEP,this means that the same or lower MCS should be used for a packet contain-ing PAN compared to a packet without PAN. E.g. The radio conditions areMEAN_BEP = 23 and CV_BEP = 1. If we know that a packet withoutPAN is optimal to be sent with MCS-7, then we conclude that the opti-mal MCS to send a packet with PAN is MCS-7 or lower (during the samecombination of MEAN_BEP and CV_BEP).

7. We have prior knowledge about some ranges of the parameters that are lessor more likely to be included in an optimum. The parameters of today’soptimum are within the more likely ranges. One method of reaching anoptimum could be by making minor adjustments of the optimum used today.

Chapter 4

Implementation

It should be clarified already at this point that there are numerous different waysof how to implement the different parts of the GA. The different ways may varyin complexity in the idea, complexity of implementation, efficiency and suitability.Only one idea has been implemented of each of the GA parts, as time has been alimiting factor. Roughly has the decision been made on the implementation timerelative to suitability.

Another thing to point out is that a lot of minor details and special-cases havebeen left out in the description of the implementation, as it otherwise would bea tremendous description. Pseudo code has been used to for complementing thedescription of the algorithms.

The implementation of the GA was done using Python and with the help of aGA-library called Pyevolve [20].

4.1 The Crossover OperatorSeparate crossover operators have been made for the parameters and tables dueto the fundamental differences between them, but they do have some things incommon. The crossover operator operates on two parents and the offspring shouldbe something in between those. They are also both easier to describe if a nomen-clature is used.

4.1.1 NomenclatureIt should be clarified that the nomenclature used is not officially used within GA,and is invented by me in order to ease the outline of my algorithms.

Relationships

One of the parents is randomly picked to be called mom and the other dad. Inthe same way are the offspring called son and daughter. The closest parent tothe son is dad, and the son is more like dad than mom, which is called the other

29

30 Implementation

parent. The relationship between daughter and mom is the same; mom is theclosest parent and dad the other parent1. Although the offspring is more alike it’sclosest parent, it still has features from the other parent. The randomness decidesthe amount of influence from the other parent.

Coordinates

A cell in a table can be localized with the coordinates described with the nota-tion 〈MEAN_BEP,CV_BEP〉. A pair of cell coordinates may be assigned to avariable, e.g. E = 〈15, 1〉. The following operation can be made on the variable:MEAN_BEP(E) and it returns the value 15.

Borders

Table 2.3 (on page 12)follows the assumption of simplification number 1, ascendingMCS values. In a table with ascending MCSs, a border can be defined for eachMCS. The lowest MEAN_BEP where a MCS occurs for each CV_BEP makesup a border. For example, in table 2.3, the cells : 〈25, 0〉, 〈25, 1〉, 〈25, 2〉, 〈25, 3〉,〈25, 4〉, 〈24, 5〉, 〈23, 6〉 and 〈23, 7〉 make up the border for MCS-8.

A MCS does not always have a border stretching from CV_BEP 0-7. Oneexample of this is MCS-7 in table 2.3. For such cases is the definition of a borderextended to being the lowest MEAN_BEP that is equal to or greater than thecurrent MCS. For MCS-7 in the example table, the border is: 〈23, 0〉, 〈23, 1〉,〈21, 2〉, 〈22, 3〉, 〈23, 4〉, 〈23, 5〉, 〈23, 6〉 and 〈23, 7〉.

Edges

The cells 〈25, 0〉 and 〈23, 7〉 makes up the left and right edge of the border ofMCS-8. The edge to the left is called the left edge and has a value of CV_BEP of0. The right edge has a value of CV_BEP of 7. The edges of MCS-7 are 〈23, 0〉and 〈23, 7〉.

4.1.2 Help FunctionThe following function is used for generating new values and is the key idea in thecrossover for both the parameters and the tables. The function takes a variable(that has a value) from the closest parent (Vcp)) and the other parent (Vop) each asinput and generates the variable of the offspring (Vof ) as output. The generatedvalue of the variable of the offspring (Vof ) is more likely to be closer to the closestparent (Vcp) than the other parent (Vop). Pseudo code 4.1 clarifies the idea of thisfunction.

An example of how the probability distribution of Vof could look like is foundin figure 4.1. It is described with a linear function within a limited interval. In thisexample is the derivative of the function positive, since Vcp ≥ Vop. The derivativeis negative if Vcp ≤ Vop. The value of the derivative depends on the distance

1I must hereby apologize for using such a heteronormative concept of family relationships. Itsimply was the most intuitive way of defining the relationships.

4.1 The Crossover Operator 31

define GenerateOffspringVariable(Vcp, Vop)Generate Vof that fulfills

Probability function:probability(Vof = Vop) : 0probability distribution(between Vop and Vcp): linearprobability(Vof = Vcp) : max

EndGenerate Vof

Return Vof

end define

Pseudo Code 4.1: The help function used in crossover.

between Vcp and Vop, since the integral of the triangular area needs to be equalsto 1.

Probab

ility

Vop VcpVof

Figure 4.1. The probability distribution of Vof .

4.1.3 The ParametersThe crossover for parameters is mostly built on the idea of the help function andis therefore quite straight forward. The algorithm is described in pseudo code 4.2.

The algorithm makes it more likely that the parameters of the offspring arecloser to the values of the parameters of the closest parent. Notice that the algo-rithm does not permit values of the parameters of the offspring to be outside theinterval of the values of the parent’s parameters. Such values are only generatedin the mutation in order to have a better control of the degree of diversification.

4.1.4 The TablesThis is the most complex part of the implementation, and the explanation of themain idea will be as brief as possible using pseudo code and some extra comments.

32 Implementation

for each parameter dofor each offspring do

Pcp ← parameter value(closest parent)Pop ← parameter value(other parent)Pof ← GenerateOffspringVariable(Pcp, Pop)

endfor offspringendfor parameter

Pseudo Code 4.2: The GA crossover for parameters.

The crossover of the table-parameters is not done on a cell-level, but on a table-level. It’s the whole table that gets crossed, rather than individual cells (table-parameters). If the parents contain several table-types (one table-type could be8-PSK, no PAN, downlink and IAN) is the crossover operator applied to each ofthem.

The algorithm iterates over each of the MCS contained within the parents,starting the iteration with the top (MEAN_BEP = 0) MCS and iterates over thedifferent MCSs downwards. At each iteration are the left and right edges of theborders of the parents identified. The MEAN_BEP coordinate is extracted fromeach of them (CV_BEP equals 0 for left and 7 for right) and a new MEAN_BEPcoordinate is created for each side for the offspring, using the help function. Theedges generated are put in the set of border for each offspring.

The edges of the border of the offspring are now set, and the next step is togenerate the rest of the cells that will make up the border-set. The parents willhave no influence of what the rest of the border will look like. The border is insteadconstrained in terms of the MEAN_BEP-value of the lower and upper value ofMEAN_BEP(Left Edge) and MEAN_BEP(Right Edge) of the the offspring.

A second constraint (Constraint2) is that the MEAN_BEP-coordinate is notallowed to both increase and decrease over the CV_BEP-scale (but it is allowedto remain constant). In other words, looking at the whole border, it will eitherincrease in MEAN_BEP values or have the same value over the CV_BEP-scale,alternatively it will have a decreasing MEAN_BEP values or the same values overthe CV_BEP-scale.

Many details about these constraints and other, minor, constraints are left outin this description, as they are too detailed and complicated. The algorithm isoutlined in pseudo code 4.3.

This implementation has used the following simplifications from chapter 3.6.3(on page 27):

Simplification 1 Borders could not be defined without this simplification.

Simplification 5 It may not be clear in this description, but all the MCSs fromthe parents are preserved, as no MCS may “vanish” in the crossover. Thisalso holds if one parent contains a MCS which the other does not.

Simplification 2 This is implicitly implemented, as the first generation usesMCS-9 for the highest MEAN_BEP, and the offspring will preserve all the

4.2 The Mutation Operator 33

for each table-type dofor each MCS in table do

for left and right (L/R) dofor offspring do

Ecp(L/R) ← FindEdge(Closest Parent)Eop(L/R) ← FindEdge(Other Parent)Eof(L/R) ← GenerateOffspringVariable(Ecp(L/R), Eop(L/R))

endfor offspringendfor left and right (L/R)for offspring do

Borderof = EofL ∪ EofR

for 1 ≤ CV_BEP ≤ 6 doGenerate Ycorr that fulfills

Ycorr ∈ [MEAN_BEP(EofL),MEAN_BEP(EofR)]Constraint2OtherConstraints

EndGenerate Ycorr

Borderof = Borderof ∪ 〈Ycorr,CV_BEP〉endfor CV_BEP

endfor offspringSet all cells below border to current MCSendfor MCS in table

endfor table-type

Pseudo Code 4.3: The crossover for tables.

MCSs in the same order. This means that MCS-9 will be used for the highestMEAN_BEP in all offspring in all generations.

Simplification 6 has not been implemented. This is only due to lack of time.

4.2 The Mutation OperatorThe probability of an individual being processed by the mutation operator is gov-erned by a mutation rate, Rm. This parameter is also used for determining thedegree of mutation.

4.2.1 The ParametersThe main idea is to generate mutated parameters with the use of a normal distri-bution with a mean equal to the current value of the parameter, Pc, and a standarddeviation dependent on the mutation rate, Rm. The new parameter value will bePn.

The standard deviation is: max−min2∗√

6∗(1−Rm) , where 0 ≤ Rm < 1 and max and minmakes up the parameter interval (given in table 2.5 on page 14).

34 Implementation

If Rm = 0 and max−min2 = Pc the probability distribution will be a triangular

probability distribution, with the highest probability of the current value. This isillustrated in figure 4.2.

Please note that the figures in this chapter are only for illustrating the prin-ciples, and neither are their relative sizes proportional, nor are the probabilitydistributions continuous (the parameters has discrete values).

min maxPc

Probab

ility

Pn

Figure 4.2. The probability distribution of Pn when Rm = 0 and max−min2 = Pc.

Most often is max−min2 6= Pc, which will move the triangular probability outside

the maximum and minimum parameter interval. Pn < min or Pn > max are notallowed, and if such values are generated, a new Pn is generated. This will resultin a probability distribution that is different to that of figure 4.2. A illustrationof principle is found in figure 4.3 .

min maxPc

Probab

ility

Pn

Figure 4.3. The probability distribution of Pn when Rm = 0 and max−min2 < Pc.

Rm 6= 0 results in a probability distribution that is stretched out and as Rm

increases, the distribution will become more and more like a uniform probabilitydistribution. Values outside the interval are not allowed and handled in the same

4.3 Selection 35

way as previously. This is illustrated in figure 4.4

min maxPc

Probab

ility

Pn

Figure 4.4. The probability distribution of Pn when Rm 6= 0 and max−min2 > Pc.

4.2.2 The TablesAs with the crossover, the mutation is not done on a cell-level, but rather on atable-level.

The implementation is very simple: For every iteration the following threevariables are randomly picked:

1. row or column

2. clone or delete

3. shift up/left or down/right.

If row/column was picked, a random row/column is picked. Then clone or deleteis picked, which decides what should be done with the row/column. The lastchoice governs how the rest of the table is affected. If a row/column is deleted,the rows/columns above/to the left or below/to the right are shifted to fill up theempty row/column. The outermost row/column of the table is cloned. If cloneis picked, the selected row/column is cloned and the rows/columns above/to theleft or below/to the right are shifted and the outermost row/column is shifted out(deleted).

Each time this algorithm is carried out, a variable is accumulated with thenumber of cells in a row or column, depending on which that has been picked.The algorithm restarted until this variable exceeds the value of Rm ∗ 8 ∗ 32. If theparents contain multiple table-types, this is carried out for each of the table-types.

4.3 SelectionThe selection is based on the fitness of the individuals. A number of individualsare picked at random and put in a tournament pool. The number of individuals is

36 Implementation

determined by the variable tournament pool size. The individual with the highestfitness of those is picked to become a parent. This process is needed to be repeatedin order to get another parent.

More ideas of how to select the parents are found in [18, Ch. 4.4.1].

4.4 FitnessThe fitness is based on the quality of an individual relative to the rest of thepopulation. Using the throughput as the fitness might be an alternative, but thisis not preferable if a diversified population is used in the initial population. In thiscase, the search would be highly affected by the best individual at a very earlystage. Also, at the end of a search, when the population is converging towardsan optimum, there might not be much difference in throughput of the individuals.Neither of these properties are desirable.

For this problem I’ve decided to use linear scaling, which is a common methodwithin GA. The fitness f , is calculated from the throughput t by: f = a ∗ t + b,where the coefficients a and b are calculated each generation to make the maximumfitness to be a specified multiple of the average throughput of the population, tavg .This constant c is calculated by f = c ∗ tavg, using the individual with the highestthroughput. The average fitness is equal to the average throughput. When weknow the scaling from the maximum throughput to maximum fitness and averagethroughput to average fitness, the constants a and b can be calculated as a simplelinear equation.

c has been kept at 1.2 in this implementation, as default in the Pyevolve library[20]. A higher value of c gives a quicker converge towards an optimum and viceversa. Other ways of calculating the fitness are found in [18, Ch. 4.4.1].

Chapter 5

Results

In this chapter are the results from the empirics of this thesis presented.

5.1 File SizeAlthough the settings and setup are the same between each FTP-transfer, thereis a natural variation of the average throughput due to the fading. The effect ofthe fading is more prominent with smaller files. As the average throughput is theway of determining the quality of an individual, this means that the evaluation ofan individual is based on data with an amount of uncertainty.

Overall this means that there is a balance between the speed of the iterationsand the quality of iterations. A faster algorithm may compensate for the uncer-tainty of the measurements by doing more iterations during the same time budgetas a slower algorithm with better quality. On the other hand, an algorithm withbetter quality may converge to an optimum much faster as it makes the decisionson more reliable measurements.

THC was used in order to investigate the uncertainty by letting the PC down-load files of different sizes from the FTP-server under the same conditions. Thefile sizes have been varied between 512kB, 1MB and 2MB. The measurements wascarried out by doing 2 ∗ 17 transfers of each file size and the resulting data arelisted in table 5.1.

The time was measured not only over the actual transfers, but also includedthe time between each transfer. This was done in order to avoid any unforeseenside-effects in the execution time of the APOA from using different file sizes. Forexample, a larger file results in a larger protocol-logging file (from the monitoringsoftware on the PC), which takes a little bit more time to both process (in order tocalculate the average throughput) and transfer (from the PC to the server wherethey are stored) and these things are done in between each of the measurements.Another reason to include the time between the transfers is to get a perspectiveof the transfer-time compared to the over-all time. As we see, it takes much lessthan 4 times the time to use 2MB files instead of 512kB. The factor is instead 2.56

37

38 Results

512kB 1MB 2MBSamples 2*17 2*17 2*17Avg. time per sample (min:sec) 00:45 01:25 02:41Effective transfer time (min:sec) per 17 samples 12:45 24:05 45:37Avg. time per 17 samples (min:sec) 22:47 34:37 58:20Min throughput (kbit/s) 78.7 86.5 93.6Avg. throughput (kbit/s) 96.6 99.4 99.8Max throughput (kbit/s) 107.7 110.4 109.6Max difference from avg. 17.7 12.9 9.8Avg. deviation per 17 samples 6.2 4.9 3.5

Table 5.1. Comparison of different file sizes.

(for 2MB) and 1.52 for 1MB. The time gained of using a smaller file might be lessthan one expect.

A note should also be made about the processes between the transfers. Ithighly consists of time for executing the THC-software, which includes connectingto and logging in to the FTP-server, copying different log files to their properstorage folder, starting and exiting the monitoring software on the PC, executingthe GA and several other small processes. The execution of the GA takes aboutthree seconds per individual. This supports the statement made in chapter 3.5,where I claimed that the choice of metaheuristics is of minor importance, as theover-all execution time is negligible relative to the total time of the APOA.

There is a higher average throughput as the file size increases. This was ex-pected as it takes some time to start the transmission before reaching the higherthroughput values and to end the transmission. The times for these processes areconstant independently of the file sizes. These processes takes less time relative tothe whole transfer time with a larger file compared to a smaller file. The changeof average throughput also affects the min and max throughput, lower averagethroughput generally means lower minimum and maximum throughput. One ex-ception is the max of 1MB and 2MB, where the difference is small and probablydue to chance.

The most interesting data is the deviance, as it is a good way of measuringthe uncertainty of the measurements. It was calculated by taking the averagedeviation of two sessions with 17 samples each. As seen, there is a clear tendencythat the deviation decline as the file size increases. If one would look at thevariance (deviation2), then the differences would be even larger. A higher value ofthe deviation means greater uncertainty in the measurements. This is confirmedby looking at the differences between the average values and the minimum andmaximum, where it decreases as the file size increases.

Based on these measurements, I have decided to focus on using 2MB files forthe ensuing measurements, since the usage of smaller file does not decrease theexecution time of the APOA to such an extent for motivating a higher uncertainty.

Some more data from these measurements are found in Appendix A. The set

5.2 Optimization 39

of network parameters used are found in appendix B.

5.2 OptimizationFinally, we arrive to the core of this report that will answer the basic question:Does this APOA generate individuals that converge towards an optimum? I.e.does this APOA generate sets of network parameters that yield an increasedthroughput?

To answer this question, an individual were created that generated poor perfor-mance compared to the individual that previously had been created using hand-optimization. Copies of the poor individual was used as the initial population.The data of the poor individual can be found in Appendix B. The settings for theGA can be found in table 5.2.

GA-setting ValueElitism YesTournament Pool Size 2File Size 2MBPopulation Size 17Mutation Rate 0.2Crossover Rate 0.9Table-Type to Optimize 8-PSK, IAN, no PAN, downlink

Table 5.2. The GA-settings for the run in chapter 5.2.

The result of the execution of the APOA is found in figure 5.1. This shows themaximum, average and minimum throughput of each generation.

The first generation, generation 0, consists of copies of the poor individual.This generation can therefore be seen as a rough benchmark of the uncertainty ofthe measurements. With a non-varying radio link would the maximum, averageand minimum throughput of the first generation be the same, but as we see wehave a variation between 89.1 and 103.9 kbit/s.

The reason for why the maximum throughput never decreases is the usage ofthe elitism option. As earlier explained, this means that the best individual fromthe previous generation is inserted into the next. A better individual is found ingeneration 5 (113.6 kbit/s), generation 10 (114.0 kbit/s) and generation 14 (114.6kbit/s).

The mutations do not necessarily generate better individuals. The effect of thiscan be seen in the ensuing generations of generation 0. It has a negative overallimpact on the performance of the individuals, as the average throughput decreasessignificantly from 98.1 to 84.8 kbit/s. But as we see, it improves the averagethroughput in an overall perspective. Both the average and maximum throughputincreases over the generations, which is an indication that the implementationof the GA works in practice. The best individual found during this session was

40 Results

50

60

70

80

90

100

110

120

130

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Generation

Throughput (kbit/s)

Max

Avg

Min

Figure 5.1. Evolution

measured to give a throughput of 114.6 kbit/s. This is significantly higher thanthe maximum measurement of the first generation (103.9 kbit/s).

One should keep in mind that the quality of the radio link together with theradio-standard used, sets an upper limit for the maximum throughput.

5.3 Quality of Found OptimumIt is natural to question the quality of the best found individual, as there is anuncertainty in measuring the quality of the individuals. This was tested by doinga verification session with 17 FTP-transfers using the individual found in theoptimization run in chapter 5.2. The results are found in table 5.3.

Read out from the result of the verification, it is clear that the optimum foundwas an optimistic measurement of the individual. Not even the maximum mea-surement done in the verification session is higher than the value given in theoptimization session. The explanation is chance or a so-called “lucky” measure-ment. If a sufficient number of verification measurements where to be done, itwould result in at least one measurement with a throughput equal to or higherthan that given by the individual in the optimization run.

The most important conclusion of this verification is that even the minimumvalue is higher than the maximum value of the initial individual in the optimization

5.4 Optimization Without Elitism 41

ThroughputMeasured in optimization session 114.6 kbit/sMax. in verification session 113.2 kbit/sAvg. in verification session 109.3 kbit/sMin. in verification session. 106.9 kbit/s

Table 5.3. Data for verification of found optimum.

run. The probability that the found optimal individual does not give a performancethat is better than the performance of the initial individual can therefore be seenas negligible. This means that an improvement of the network settings has beenmade, and it thus proves that the implementation of the GA and APOA works asintended.

5.4 Optimization Without ElitismEven though a proof of concept has been made, different setting may be adjustedto get a better result. As seen in previous chapter, there is a problem with theuncertainty of the elitist. The elitist is probably a result of a “lucky” measure-ment, and the elitists genes may be quite average and may generate offspring withmediocre performance. The elitist is benefited on the expense of those individualwhich on a statistical average has a better fitness than the elitist. Mediocre in-dividuals that has a high influence on the population is an undesirable propertythat affects the evolution negatively. One solution to this is to simply disablingelitism, and see what the result is like. When elitism is not used, the search doesnot benefit the best individuals to the same extent. The tournament pool size wasincreased to somewhat compensate for this.

The GA settings are found in table 5.4 and the initial individual in Appendix B.

GA-parameter ValueElitism NoTournament Pool Size 6File Size 2MBPopulation Size 17Mutation Rate 0.2Crossover Rate 0.9Table-Type to Optimize 8-PSK, IAN, no PAN, downlink

Table 5.4. GA-settings for the run in chapter 5.4.

The results can be found in figure 5.2.

42 Results

50

60

70

80

90

100

110

120

130

0 1 2 3 4 5 6 7 8 9 10

Generation

Throughput (kbit/s)

Max

Avg

Min

Figure 5.2. Evolution without elitism

Looking at the average throughput over the generations, it is clear that thegenerations strives towards an optimum. This is a clear indication that the GAworks as intended. The maximum throughput seems to reach a limit, as it quitequickly reaches the maximum throughput in generation 4 with a value of 111.7kbit/s. The throughput in generation 10 is almost the same with a value of 111.5kbit/s.

If one compare this run with that of chapter 5.2, we see that a higher through-put is reached with elitism within 10 generations. This could indicate that the useof elitism is preferable, but could as well be due to chance. More runs are neededbefore concluding anything.

5.5 SummaryAs seen in chapter 5.1, it takes almost one hour to perform measurements of ageneration with 17 individuals. This means that those APOA-runs presented inthis chapter have taken about 10-15 hours to complete. It’s an optimization thattakes a whole lot of time and is therefore highly reliant on the stability of the wholeAF. This is something that has not been satisfactory, mainly due to the mobilethat has been used. The mobile is a prototype that supports IAN, and as it is nota mature technology, it has had stability issues. With the current implementation

5.5 Summary 43

the APOA needs to be restarted from the beginning if the session crashes. A wayof limiting the effects of the instability will be presented in chapter 6.1.3.

Another reason for why there are not more results presented is due to thelimited access to THC. THC is a shared resource and as other projects sometimeshave higher priority, the runs of the APOA have been put on hold. More accessto THC obviously means more successful runs.

An interesting test would have been to compare the performance of the foundoptimum in chapter 5.2 with the performance of the optimum found by manualoptimization. However it would not have been a fair comparison, as the handoptimization has been optimized using several different C/I, and the optimizationin chapter 5.2 is done with a constant C/I. The interesting part would be to see ifthe APOA manages to generate an individual with a higher throughput than thehand optimization, rather than the difference between them.

I had hope of presenting more results with longer evolutions in this chapter,but due to the reasons mentioned, that has not been possible. Even though,the runs which are most important for this thesis have been carried out. Thosepresented are the longest run and it is those who show the clearest strive towardsa higher throughput. Shorter runs consisting of less than 7 generations have beenaccomplished and even though they all show a similar behaviour, they are all tooshort for making conclusions from them separately.

Chapter 6

Future Research andConclusions

6.1 Future ResearchGA are complex and offers a wide range of alternatives of how to implement thedifferent operators and different values for associated input parameters. Duringthis thesis, I’ve come up with many ideas of how to change the behavior of the GAusing different implementations and settings. The time available has almost notat all allowed me to try out these different ideas. Some of them might be betterthan the current implementation, while it sometimes might be better to alternatebetween different settings and implementations during an APOA run. In the nextchapters I will present some of my ideas that might improve the performancecompared to the current implementation.

6.1.1 Algorithm Parameters to AdjustThere are a number of GA-parameters in the current implementation that may beadjusted for better performance1.

Population size I’ve been using 17 during all runs. This is a very small popu-lation, and [18] for example mentions 30 as a guideline for a lower limit forthe population size. A smaller population easily converges towards a sub-optimum. One reason for using 17 has been mainly due to my feeling thatthere are not especially many optima, and that they all are close to eachother. Another reason is to generate more generations within a limited timebudget with the hope of seeing any affects of the optimization quicker. Onthe contrary, a GA with a larger population size handles the effect of noise(e.g. variation of throughput) better. There has been interesting researchdone within this area that probably could give a guideline of the population

1Ironically, this could be formed into an optimization problem of the optimization parameters.

45

46 Future Research and Conclusions

size for this specific problem. Some of the research is presented in [21] and[22].

Mutation rate The current value of the mutation rate of 0.2 may be an optimalvalue. More thoughts about the mutation in chapter 6.1.2.

Size of FTP file This is the only GA-parameter that has been investigated onsome level. Even more testing would be preferable to verify the results fromchapter 5.1. It would also be interesting to see what the results would be ofusing files larger than 2MB. One should also look at the effects of a changeof this parameter over a whole run of the APOA.

Number of FTP samples An alternative to increasing the file size is to use sev-eral samples of the same file for measuring the performance of one individual.The current implementation only uses one sample.

Tournament pool size The number of individuals included in the tournamentpool might not be optimal, and is therefore a subject of optimization. Again,interesting research within this area has been done in [22].

Linear scaling c The parameter c in chapter 4.4 is sometimes mentioned to havea standard value of 2. It might be an idea to try out different values of c,although I suspect that the effect of a change of this variable has limitedimpact due to the current implementation of the selection operator.

Elitism Further tests has to be made in order to conclude whether the use ofelitism or not is preferable.

6.1.2 Improvements in the GA ImplementationThere are a number of alternative ways to implement the GA, as mentioned earlier.

Initial individuals This is one of the main flaws of the current implementation.Currently the initial population consists of copies of the same individual.This results in a limited search of the search-landscape. The possibility ofmanually defining which individuals that constitutes the initial populationwould result in a more diversified generations. To get a more diversified pop-ulation, a higher mutation rate has been used. The mutation rate has beenconstant during the runs, which means that a high mutation rate has beenused even though the GA is close to the optimum, where smaller mutationsare preferred. A possibility to define the initial individuals is also a way ofutilizing the knowledge of the operator. As the operator might have ideasof what the general characteristics of a solution might look like, he mightinsert several examples of these which probably will make the GA find anoptimum faster.

Other mutation algorithms Currently there is only one mutation operator im-plemented for the tables and parameters each, and other implementationsmight suite the problem better. Here are some examples of alternative waysto implement the mutator operators.

6.1 Future Research 47

Improved Parameter Mutation For low mutation rates the current pa-rameter mutator generates values in the whole parameter interval. Abetter implementation would for example be to generate values uni-formly within an interval determined by the mutation rate. If the mu-tation rate is 0, the interval would be 0, only generating values equalsto the current value.

Remove MCS Simplification 5 in chapter 3.6.3 is a guess more than thetruth. There is no guarantee that all MCSs that are technically pos-sible to be contained in a table is part of the optimum table. As thecurrent algorithm leaves out the possibility to selectively remove all oc-currences of a MCS, it might not be possible to reach the optimum. A’remove-MCS’-mutator also implies the need for the possibility to insertMCSs that are not contained within the table, otherwise it would be adominant process, resulting in tables with only one type of MCS. Byadding and removing MCSs from individuals, the population would getmore diversified.

Fine A mutator specified in making small changes would be more suitablefor very low mutation rates. The current implementation is very roughand not efficient when the optimum is thought to be close by.

Leaning right A truly non-scientific study of the other tables that are im-plemented in the EGDE LQC (about 18 of them) suggests that theborders often have a lower value of MEAN_BEP for low CV_BEP,than for high CV_BEP. A mutator that mimics this appearance mightbenefit the mutated individual, but this is far from certain.

Alternation between these Probably is the best implementation an im-plementation that alternates between some or all of these suggestedimplementations. This would benefit the diversification of the popula-tion and probably yield a better optimum.

Better table crossover operator I am pretty satisfied with the crossover op-erator, but there are of course things that can be improved. One thing isto remove the dominance of parent that has a MCS that the other parentdoes not have. If a parent contains a MCS that the other does not, it is forcertain that both the offspring will have it. This is in agreement with simpli-fication 5 in chapter 3.6.3, but it’s not built on firm assumptions. There arealso many minor improvements that could be done to the current crossoveroperator.

Aging Elitist There is a problem of how to handle the elitism, as described inchapter 5.4. Two alternative solutions could be to introduce a maximumage for the elitist individual. Once the age is reached, the individual isre-evaluated or simply excluded from the population.

Variable number of FTP samples The time for measuring the performanceof an individual depends on the file size. Individuals with poor performancetake even more time to measure, as the average throughput is lower. By using


for example two FTP samples, it would be possible to discard an individualas non-optimal after the first sample if the throughput is below a certainvalue. If the throughput is high enough, a second sample may be transferredin order improve accuracy of the measurement. The file sizes of the first andsecond sample do not necessarily need to be the same.

Dynamic mutation rate Some kind of intelligence that detects when the popu-lation approaches an optimum and lowers the mutation rate would be prefer-able. This would increase the efficiency of finding the optimum.

Implement simplification 6 in chapter 3.6.3. This could improve the efficiencyconsiderably if a optimization was done over both PAN and non-PAN tables.

Implement simplification 7 in chapter 3.6.3. This could be used to improvethe efficiency considerably of both the mutation and crossover of parameters.

These are most of the ideas that I could imagine to improve the efficiencyof the GA. A closer collaboration with those persons who that have an extensiveknowledge about the theory behind the LQC algorithmmay result in improvementsthat are not listed here.

6.1.3 Improvements in UsageAs the idea is to use this APOA in practice, it currently lacks some practicalfunctions. Another reason for improving the usage is to ease the use for a operatorwho is not me and who does not know all of the details of the APOA.

Continue last session Stability has been a major issue all through this thesis.Even if the system would be stable, it might sometimes be useful to terminatethe APOA and continue at some other time (perhaps due to IT-service orprojects with higher priority). With the current implementation, the testinghas to be restarted from the beginning. For these reasons, a functionality ofresuming last APOA session would be indeed very handy.

Initial individuals interface A simple way of inserting new individuals into theinitial population is needed for trying out different initial populations. Thecurrent implementation does not allow for a diversified initial population, andin order to add this functionality without making it tedious to insert newindividuals, the code for generating the first population has to be rewritten.

Display of best individual Currently it is hard to get an overview of the op-timum found. The optimum could be written out in a file and/or on thescreen in order to ease the work of the operator.

Auto generation of plot To produce a overview of the evolution, manual workhas to be done using the current implementation. This process could beautomated.

6.1 Future Research 49

Improved crash-handling If the APOA session crashes or is terminated toearly, a bit of manual work is needed to gather the information from theAPOA to one place. This process could be automated.

Complete history Only the individuals needed for calculating the next genera-tion are saved in memory. Saving all individuals would make it possible toget a detailed view of the evolution by looking at each individual. Anotherpossibility could be to revert the GA to a previous state. This functionalityprobably requires a quite extensive implementation.

6.1.4 A Different Approach

In chapter 5.1 and table 5.1 we see that there is a lot of time between each transferdue to the processes of handling the data from the transfer. A quite differentapproach that has the potential to eliminate most of these inter-transfer processesis to constantly do a FTP-transfer and do all the processes of the APOA on thefly. This means calculating the average throughput during a specific time interval,within the same amount of data would be transferred as e.g. a 2MB file. Theonly time which could not be used to measure the throughput is the time whenthe change the network parameters is taking place (≤ 10 sec) for each individual.This would increase the time efficiency rapidly as it removes most of the overheadbetween the transfers, caused by THC. Calculations could also be done in parallelwith the transfer.

A disadvantage of this idea could be that it lacks the starting and endingprocesses (also mentioned in chapter 5.1), which makes it less realistic. Although,the practical impact of this disadvantage is unknown, and may not even exist.

This is an idea that would require an extensive implementation, which is quitedifferent from the typical usage of THC today. But if it works, it would be con-siderable more efficient than the current implementation of the APOA.

6.1.5 Beyond the APOA

In the longer perspective, this thesis has laid the foundation of further developmentof the AF concept, apart from the APOA. Another application of the AF couldbe testing of robustness in order to find any configuration that does not givesatisfactory characteristics. The AF may also be applied to troubleshooting withthe purpose of finding the cause of some known failure or problem generatingunwanted characteristics. Although these two applications of the AF are notwithin the scope of this thesis, they are closely related and should be easier toadopt now that the APOA has been implemented. Parts of software developed forthe APOA could be used when implementing software for the other applications.This common part would then be part of the AF.


6.2 ConclusionsThe main objective of this thesis has been to prove that the concept of optimizingthe network parameters with the help of an APOA. A number of limitations (listedin chapter 3.6) have been made in order to make this possible within time. Theselimitations have the potential to affect the possibility of proving the concept.

From the viewpoint of the setup, the constant C/I and the lack of measuring thelatency are the limitations with the largest effect with respect to generalization toa realistic optimization case. The current implementation handles the uncertaintyof the measurement when using a constant C/I, but using a varying C/I mightfurther increase the uncertainty. This may be counteracted either by using a betterpopulation size or by increasing the file size, as described in chapter 6.1.1. Theuncertainty can certainly be handled by using a large enough file, although thisdecreases the speed of the algorithm. Taking the latency into account does onlycause a slightly larger optimization problem and a bit more implementation.

The limitations made in chapter 3.1 is used to specify a specific problem thatcould be used for testing. The implementation made in this thesis is general enoughto handle any type of problem that is formed out from this class of problems by re-moving those limitations. The only consequence would be the size of the problem.The current implementation may not find an optimum within a reasonable timeif the problem is extended too much (more network parameters included). Al-though, there are many improvements suggested (in chapter 6.1) that can improvethe efficiency and speed of the APOA, which would make it possible to handleeven larger problems.

The implementation of the GA has been done with the ambition of creatingsomething that is possible to run with correct operation, which also the number ofimprovements listed in chapter 6.1.2 confirms. Even though the implementation isput on a basic level, it does not counteract the possibility of proving the concept inany way. A too basic or dysfunctional implementation may restrict the possibilityto reach the desired effects (i.e. causing the evolution to strive towards a betterthroughput), but could not cause the opposite effect of making the throughputstrive towards an optimum although it would not do so in a practical optimiza-tion case. In other words, a basic working implementation is a requirement forproving the concept, rather than a limitation. The list of improvements in the GAimplementation are therefore only improvements, and the current implementationfulfills the needs for proving the concept.

The improvements listed in chapter 6.1.3 have no effect for proving the concept,but are rather a list of things that could be done in order to ease the use the APOAin practice.

Even though the list of subjects for future research is quite extensive and somelimitations have been made, none of them constitute such a restraint that proofof concept could not be made. From the basis of this conclusion together withthe obtained results in chapter 5.2 and 5.4, I conclude that the concept has beenproven to work and that there are no obvious reasons for why this would not workin practice.

A closely related question to ask is could the APOA find an optimum that is

6.2 Conclusions 51

better than that generated by hand?, which has not been given a answer in this the-sis. As this basic implementation has already proved to generate individuals withdecent performance and that the list of further improvements is quite extensive, Iam convinced that it is possible to make the APOA generate network parametersthat are of better quality than those generated by hand.

But before the APOA can be used in practice, there are a number of thingsthat have to be implemented. The minimum of things needed to be done beforeis:

Tuning of the GA-parameters This is a quite simple manual iterative processthat could improve the efficiency of the GA, without the need for any im-plementation.

Finer mutation The roughness in the current table and parameter mutator isnot suitable for finding optima, and limits the possibility to find optimabetter than those found by manual optimization.

Dynamic mutation This is a quite simple implementation and has the potentialto improve the performance of the APOA considerably.

Initial individuals A possibility to define the initial individuals is a simple wayof utilizing the knowledge and experience of the operator which could resultin a much faster optimization. It requires a little bit more time to implement,compared to the other suggestions in this list.

Improved user interface The current user interface is complicated, and needsto be improved for practical usage.

Continue last session The stability issues are still not solved, and a possibilityto put an optimization run on hold would be very handy. Therefore shoulda possibility to continue an earlier session exist.

Remove limitations As stated earlier, if the found optima are to be used in thereal world, it must be generated with an APOA that uses a variable C/I andthat has taken the effects of latency into consideration.

When these things are implemented, the APOA would be fully usable in prac-tice. The APOA solves all of the problems listed in chapter 3.1, and therefore I amconvinced that the effort and cost of taking the last steps in the implementation ofthe APOA is without doubt motivated. The APOA is a much more cost efficientalternative to the current solution and will probably give a better result. ThereforeI truly recommend Ericsson to complete the implementation of the APOA.

Bibliography

[1] GSM Association, www.gsmworld.com, 23:rd of June.

[2] UMTS Forum, www.umts-forum.org, 23:rd of June.

[3] World Cellular Information Service, www.wcisdata.com, 30:th of June.

[4] Magnus Ewert. 2001 GPRS, Studentlitteratur, ISBN 91-44-01923-8

[5] Per Wallander. 2000 GSM-boken, Perant, ISBN 91-86296-09-4

[6] Kaveh Pahlanvan and Allen H. Levesque. 2005 Wireless Information Net-works, Second Edition, Wiley, ISBN 13 978-0-471-72542-8

[7] Anders Furuskär, Sara Mazur, Frank Müller and Håkan Olofsson at Erics-son Radio Systems. June 1999 EDGE: Enhanced Data Rates for GSM andTDMA/736 Evolution, IEEE Personal Communications, 1070-9916/99/$10.0

[8] User Description, EGPRS Link Quality Control, (Ericsson Internal Docu-ment), 210/1553-HSC 103 12/14 Uen, (Revision A) by Olof Manbo 2009-03-23

[9] 3GPP TS 43.064 version 8.2.0 Release 8: General Packet Radio Service(GPRS); Overall description of the GPRS radio interface; Stage 2

[10] 3GPP TS 45.005 version 8.6.0 Release 8: Digital cellular telecommunicationssystem (Phase 2+); Radio transmission and reception

[11] 3GPP TS 45.008 version 8.2.0 Release 8: Radio subsystem link control

[12] Timo Halonen, Romero García Romero and Juan Melero. 2003 GSM,GPRS and EDGE performance, Second Edition, Wiley, ISBN 0470866942,9780470866948

[13] Algorithm Proposal for LQC for Improved ACK/NACK, (Ericsson InternalDocument), 1/0363-409/FCP 103 9009 Uen, (Revision PA3) by Olof Manbo2008-11-24

[14] EDGE Evolution - 16/32 QAM, Requirement Description, (Ericsson InternalDocument), 1/1551-100/FCP 101 8247 Uen, (Revision A) by Anders Malm2009-03-16

53

54 Bibliography

[15] 3GPP TR 45.912 version 8.0.0 Release 8: Digital cellular telecommunicationssystem (Phase 2+); Feasibility study for evolved GSM/EDGE Radio AccessNetwork (GERAN)

[16] Phil McMinn. Search-based Software Test Data Generation: A Survey, TheDepartment of Computer Science, University of Sheffield, 2004

[17] The Metaheuristics Network, www.metaheuristics.net, 13:th of November.

[18] Colin R Reeves. Modern Heuristic Techniques for Combinatorial Problems,Wiley, ISBN 0-470-22079-1

[19] Christian Blum and Andrea Roli. Metaheuristics in Combinatorial Optimiza-tion: Overview and Conceptual Comparison, ACM Computing Surveys, 2003

[20] Pyevolve by Christian S. Perone, http://sourceforge.net/projects/pyevolve/

[21] David E. Goldberg, Kalyanmoy Deb and James H. Clark Genetic Algorithms,Noise and the Sizing of Populations, Department of General Engineering atUniversity of Illinois, 1991

[22] David E. Goldberg and Brad E. Miller Genetic Algorithms, Tournament Se-lection and the Effects of Noise, University of Illinois, 1995

Appendix A

Data of File Sizes

55

56 Data of File Sizes

File Size Samples Raw min Raw deviance Raw max Raw mean Total time Time/sample

512kB 17 88,09 5,52 107,66 98,69 00:22:24

512kB 17 78,65 6,94 105,79 94,07 00:23:11

512kB 34 78,65 6,23 107,66 96,38 00:22:47 00:00:45

1MB 17 92,65 5,44 110,35 100,56 00:34:17

1MB 17 86,54 4,33 105,92 98,34 00:34:56

1MB 34 86,54 4,89 110,35 99,45 00:34:37 00:01:25

2MB 17 93,60 3,46 103,75 98,53 00:59:00

2MB 17 96,32 3,60 109,60 101,02 00:57:41

2MB 34 93,60 3,53 109,60 99,78 00:58:20 00:02:41

Appendix B

Poor Individual

The following network parameters where used in measurements of chapter 5.1, 5.2and 5.4.

Parameter P1 P2 P3 P4Value 14 9900 4000 10

Table B.1. Values of other parameters.

57

58 Poor Individual

0 1 2 3 4 5 6 70 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-51 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-52 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-53 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-54 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-55 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-56 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-57 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-58 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-59 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-510 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-511 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-512 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-513 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-514 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-515 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-516 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-517 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-518 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-519 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-520 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-521 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-522 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-523 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-5 MCS-524 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-625 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-6 MCS-626 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-727 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-7 MCS-728 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-829 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-8 MCS-830 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-931 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9 MCS-9

Table B.2. Table for 8-PSK in the downlink with IAN and no PAN.

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