Institutionen för systemteknik - DiVA...

Institutionen för systemteknikDepartment of Electrical Engineering

Examensarbete

A Study on the Impact of Antenna Downtilt on theOutdoor Users in an Urban Environment

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av

Pradeepa Ramachandra

LiTH-ISY-EX--12/4585--SE

Linköping 2012

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

A Study on the Impact of Antenna Downtilt on theOutdoor Users in an Urban Environment

Examensarbete utfört i Kommunikationssystemvid Tekniska högskolan i Linköping

av


LiTH-ISY-EX--12/4585--SE

Handledare: Mehdi AmirijooEricsson Research, Linköping, Sweden

Birgitta OlinEricsson Research, Stockholm, Sweden

Jan-Erik BergEricsson Research, Stockholm, Sweden

Chaitanya TVKisy, Linköpings universitet

Examinator: Danyo Danevisy, Linköpings universitet

Linköping, 14 June, 2012

Avdelning, InstitutionDivision, Department

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

DatumDate

2012-006-14

SpråkLanguage

� Svenska/Swedish� Engelska/English

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�

RapporttypReport category

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

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

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

ISBN—

ISRNLiTH-ISY-EX--12/4585--SE

Serietitel och serienummerTitle of series, numbering

ISSN—

TitelTitle

En Studie om Effekterna av Antenn Nedvipp för Outdoor-Användare i StadsmiljöA Study on the Impact of Antenna Downtilt on the Outdoor Users in an UrbanEnvironment

FörfattareAuthor


SammanfattningAbstract

Inter-site interference distribution acts as a basic limitation on how much perfor-mance a network service provider can achieve in an urban network scenario. Thereare many different ways of controlling this interference levels. One such method istuning the antenna downtilt depending on the network situation. Antenna down-tilt can also be seen as a powerful tool for load balancing in the network.

This thesis work involves a study of the impact of the antenna downtilt inan urban environment, involving non-uniform user distribution. A realistic dualray propagation model is used to model the path gain from the base station to aUE. Such a propagation model is used along with a directional antenna radiationpattern model to calculate the overall path gain from the base station to a UE.Under such modeling, the results of the simulations show that the antenna downtiltplays a crucial role in optimizing the network performance. The results showthat the optimal antenna downtilt angle is not very sensitive to the location ofthe hotspot in the network. The results also show that the antenna downtiltsensitivity is very much dependent on the network scenario. The coupling betweenthe antenna downtilt and the elevation half power beamwidth is also evaluated.

NyckelordKeywords Antenna downtilt, dual ray propagation model, antenna parameters

AbstractInter-site interference distribution acts as a basic limitation on how much perfor-mance a network service provider can achieve in an urban network scenario. Thereare many different ways of controlling this interference levels. One such method istuning the antenna downtilt depending on the network situation. Antenna down-tilt can also be seen as a powerful tool for load balancing in the network.

This thesis work involves a study of the impact of the antenna downtilt inan urban environment, involving non-uniform user distribution. A realistic dualray propagation model is used to model the path gain from the base station to aUE. Such a propagation model is used along with a directional antenna radiationpattern model to calculate the overall path gain from the base station to a UE.Under such modeling, the results of the simulations show that the antenna downtiltplays a crucial role in optimizing the network performance. The results showthat the optimal antenna downtilt angle is not very sensitive to the location ofthe hotspot in the network. The results also show that the antenna downtiltsensitivity is very much dependent on the network scenario. The coupling betweenthe antenna downtilt and the elevation half power beamwidth is also evaluated.

v

Acknowledgments

It has been a privilege to be guided by Mehdi Amirijoo and words fail me inconveying the admiration that I hold for him. I wish to thank him for providingthis opportunity and for the many hours that he spared. His enthusiasm andfascination have been infectious and I will certainly carry them with me forwardin life. Thanks are due to Birgitta Olin for guiding me through the simulator andalso in giving valuable inputs all along the thesis. I am grateful to Jan-Erik Bergfor his patience in answering my long list of doubts in the propagation model.Martin Johansson and Fredrik Gunnarsson have at various point of time answeredmy doubts and I wish to acknowledge their help.

I am also thankful to Chaitanya TVK for the patience and grace that he hasshown during the many times that I have approached him. Thanks are due toDanyo Danev for consenting to be the examiner. I owe immensely to the LinLabteam at Ericsson Research, Linköping, for making the workplace very friendly.And lastly, but in no small measure do I wish to acknowledge the benevolence ofthe invisible and unassuming hands of the Swedish taxpayer who made it possiblefor me to have this education.

vii

Contents

List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiList of tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

1 Introduction 11.1 Self Organizing Networks . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Self-optimization . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Antenna Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.1 Antenna Parameters . . . . . . . . . . . . . . . . . . . . . . 41.2.2 Directional Antenna Radiation Pattern . . . . . . . . . . . 5

1.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . 81.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Problem Formulation 92.1 Aim of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.1 Simulation Scenario . . . . . . . . . . . . . . . . . . . . . . 102.2.2 Observability Study . . . . . . . . . . . . . . . . . . . . . . 102.2.3 Controllability Study . . . . . . . . . . . . . . . . . . . . . . 10

2.3 Scope and Limitation . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Simulation Scenario 133.1 Basic Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.1 Network Scenarios . . . . . . . . . . . . . . . . . . . . . . . 153.2 Antenna Radiation Pattern Modeling . . . . . . . . . . . . . . . . . 18

4 Observability 214.1 Optimization Objective . . . . . . . . . . . . . . . . . . . . . . . . 21

4.1.1 5th Percentile User Throughput . . . . . . . . . . . . . . . . 214.1.2 Spectral Efficiency of the Cell . . . . . . . . . . . . . . . . . 22

4.2 Region of Observability . . . . . . . . . . . . . . . . . . . . . . . . 23

ix

x Contents

5 Controllability 275.1 Input towards Self Planning Scenario . . . . . . . . . . . . . . . . . 285.2 Impact of the Location of Hotspot on the Optimality of Downtilt

Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.4 Comparison with Okumura-Hata Propagation Model . . . . . . . . 34

5.4.1 Comparison with Urban Propagation Model Results . . . . 35

6 Impact of Elevation Beamwidth on Optimal Downtilt 396.1 Lower Elevation Beamwidth - 6.40 . . . . . . . . . . . . . . . . . . 396.2 Higher Elevation Beamwidth - 100 . . . . . . . . . . . . . . . . . . 40

7 Conclusions and Future Work 437.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.1.1 Optimal Downtilt Angle . . . . . . . . . . . . . . . . . . . . 437.1.2 Sensitivity of Downtilt for Different Hotspot Location . . . 437.1.3 Impact of Elevation Beamwidth on Optimal Downtilt Angle 44

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447.2.1 Introduction of Indoor and Vertical Plane Users . . . . . . 447.2.2 Study of the Impact of using a Real Antenna Model . . . . 447.2.3 Study of the Impact of Antenna Downtilt for Uplink Trans-

mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457.2.4 Study of the Impact of Horizontal Radiation Pattern . . . . 45

A 5th percentile user throughput for different initialization seeds andelevation beamwidth of 80 47

B 5th percentile user throughput for different initialization seeds andelevation beamwidth of 6.40 52

C 5th percentile user throughput for different initialization seeds andelevation beamwidth of 100 56

Bibliography 59

Contents xi

List of Figures1.1 Antenna coordinate system . . . . . . . . . . . . . . . . . . . . . . 41.2 Directional antenna radiation pattern . . . . . . . . . . . . . . . . 6

3.1 Network deployment with wrap around copies . . . . . . . . . . . . 153.2 Urban loading scenario . . . . . . . . . . . . . . . . . . . . . . . . . 163.3 5th percentile user throughput vs downtilt for urban case . . . . . 173.4 Spectral efficiency vs downtilt for urban case . . . . . . . . . . . . 173.5 Horizontal antenna radiation pattern of HV model. . . . . . . . . . 183.6 Vertical antenna radiation pattern of HV model. . . . . . . . . . . 19

4.1 Changes in path loss while changing downtilt from 110 to 120 . . . 234.2 Observability region . . . . . . . . . . . . . . . . . . . . . . . . . . 244.3 Observability region . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.1 Investigated hotspot locations . . . . . . . . . . . . . . . . . . . . . 285.2 User throughput vs downtilt angle for different hotspot locations . 295.3 5th percentile user throughput vs downtilt angle for different hotspot

locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.4 Cell spectral efficiency vs downtilt angle for different hotspot locations 305.5 Particular seed results of 5th SINR values . . . . . . . . . . . . . . 315.6 Load distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.7 Particular seed results of 5th percentile user throughput values . . 325.8 Deployment to understand interference situation . . . . . . . . . . 335.9 5th percentile user throughput of selected cells. . . . . . . . . . . . 335.10 User distribution in selected cells. . . . . . . . . . . . . . . . . . . . 345.11 SINR distribution for the Okumura-Hata propagation model . . . 355.12 SINR distribution for the Urban propagation model . . . . . . . . 365.13 5th percentile user throughput vs downtilt angle for OkumuraHata

model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6.1 5th percentile user throughput as a function of downtilt angle foran elevation beamwidth of 6.40. . . . . . . . . . . . . . . . . . . . . 40

6.2 5th percentile user throughput as a function of downtilt angle foran elevation beamwidth of 100. . . . . . . . . . . . . . . . . . . . . 41

A.1 Simulations with seed#1 for an elevation beamwidth of 80. . . . . 48A.2 Simulations with seed#2 for an elevation beamwidth of 80. . . . . 48A.3 Simulations with seed#3 for an elevation beamwidth of 80. . . . . 49A.4 Simulations with seed#4 for an elevation beamwidth of 80. . . . . 49A.5 Simulations with seed#5 for an elevation beamwidth of 80. . . . . 50A.6 Simulations with seed#6 for an elevation beamwidth of 80. . . . . 50A.7 Simulations with seed#7 for an elevation beamwidth of 80. . . . . 51A.8 Simulations with seed#8 for an elevation beamwidth of 80. . . . . 51

B.1 Simulations with seed#1 for an elevation beamwidth of 6.40. . . . 53

B.2 Simulations with seed#2 for an elevation beamwidth of 6.40. . . . 53B.3 Simulations with seed#3 for an elevation beamwidth of 6.40. . . . 54B.4 Simulations with seed#4 for an elevation beamwidth of 6.40. . . . 54B.5 Simulations with seed#5 for an elevation beamwidth of 6.40. . . . 55

C.1 Simulations with seed#1 for an elevation beamwidth of 100. . . . . 57C.2 Simulations with seed#2 for an elevation beamwidth of 100. . . . . 57C.3 Simulations with seed#3 for an elevation beamwidth of 100. . . . . 58C.4 Simulations with seed#4 for an elevation beamwidth of 100. . . . . 58

List of Tables3.1 Common network parameter settings. . . . . . . . . . . . . . . . . 143.2 Urban network parameter settings. . . . . . . . . . . . . . . . . . . 16

Abbreviation3GPP Third Generation Partnership ProjectAWGN Additive White Gaussian NoiseCQI Channel Quality InformationFTP File Transfer ProtocolHV Horizontal-VerticalLOS Line Of SightLTE Long Term EvolutionOPEX OPerational EXpenditureQoE Quality of ExperienceRSRP Reference Signal Received PowerSINR Signal to Interference and Noise RatioSON Self Organizing NetworkUE User EquipmentVoIP Voice over Internet Protocol

Chapter 1

Introduction

The cellular network has seen an unprecedented growth in the last decade. Witha drastic increase in cellular traffic, it has become very challenging to meet allthe requirements from both service providers’ and users’ perspective. A largeamount of effort has been invested to make these cellular networks capable ofhandling themselves. It is in this area, an application of Self Organizing Networks(SON) plays a crucial role. There are many parameters in a network that can beused to fine tune the network performance in accordance to the service providers’requirements. Antenna downtilt is one such parameter. In this chapter, a briefintroduction to the SON is provided. Later, some of the basic antenna parametersare discussed and also some previous literature that deals with the impact ofantenna downtilt is discussed.

1.1 Self Organizing NetworksA SON can refer to any network that tries to organize itself by looking at theparameters available in its control. A SON network will try to get the best outof its available resources by continually monitoring the variations in the networkperformance. In this report, SON is used to refer to Self Organizing Networks incellular use case.

1.1.1 IntroductionWith a drastic increase in network traffic, there is a need for the service providersto minimize the human involvement in handling of radio network management.This is where the Self-Organizing Networks (SON) plays a major role. SON alsoplay a key role in reducing the Operational Expenditure (OPEX) of the networkoperators, improving the overall system performance and in faster adaptation tothe changing network conditions.

The SON functionalities can be broadly classified as,

1. Self-planning

1

2 Introduction

2. Self-configuration

3. Self-optimization

4. Self-healing

Self-planning

Self-planning involves the planning of the location of a new base station and de-ciding its radio and transport parameters. As an example, it basically specifies thenumber of sectors required, suitable antenna and power settings for these sectors.This is based on the capacity or coverage goals of the network, estimated trafficforecast and some predefined limitations on the available resources from a serviceprovider. The parameters that are set during the planning phase will be defaultparameters and they can be optimized during the self-optimization process.

Self-configuration

Self-configuration involves the procedures required for bringing up the newly plannedeNodeB. They involve hardware installation, transmission setup, node authenti-cation, automatic software download to the eNodeB and also self testing.

Self-optimization

The radio network parameters are continuously optimized based on the networkoperators’ objective. The objective can be improving the network capacity or thenetwork coverage or it can be in terms of improving the user experience. Moredetails regarding self-optimization are covered in section 1.1.2.

Self-healing

Self-healing mainly involves software updating for the system maintenance andalso cell outage detection and cell outage compensation.

1.1.2 Self-optimizationSelf-optimization involves changing the radio network parameters to enhance thenetwork performance. The network performance metric can be evaluated in termsof network capacity, network coverage or the quality of experience (QoE) as perthe users in the network. As an example for the capacity based objective, one canconsider improving the cell throughput in terms of achieved bit rate. Coveragecan be enhanced by improving the 5th percentile spectral efficiency of the usersin the cell. The objective can be power efficiency oriented, where reduction oftransmission power is considered.

The self-optimization process can be categorized as:

1. Off-line optimization

2. On-line optimization

1.2 Antenna Fundamentals 3

The following subsections will brief about the off-line and the on-line optimiza-tion process.

Off-line Optimization

Off-line optimization is an open-loop optimization process. As the optimizationprocess is open loop, the impact of the changes made during the optimization willnot be fed back. Therefore, one has to be careful while changing the parametersas it might have a large negative impact on the network performance. In orderto do so, the network needs to be observed for a long duration so that one hasconfidence on the knowledge of network working condition. By observing for alonger duration, generally one can estimate the network parameters, like loading onthe network and user density, with a smaller variance. This knowledge along withsimilar knowledge from the neighboring cells will help in choosing the parametersof the optimization algorithm. The impact of these changes is initially estimatedby using some propagation models and this will be used as reference for choosingthe right parameters for optimization. Based on the confidence in the knowledgeof the network, a large degree of freedom is provided in choosing the parameters.

On-line Optimization

On-line optimization is a closed-loop optimization process. Here, the radio param-eters are changed and its impact on the network is continuously monitored. Usingthis as the feedback, the radio parameters are retuned to optimize the networkperformance and this process will continue indefinitely, adapting to the varyingnetwork conditions or for a predefined number of retuning iterations. Here, theknowledge about the network might not be accurate enough to explore the entiredynamic range of the parameters to be tuned and therefore, a careful evaluationof the impact of changing these parameters on the network performance needs tobe considered.

The knowledge of the behavior of a particular objective function while tuninga given parameter will help in developing an efficient optimization algorithm. Inthis thesis work, the analysis with respect to the impact of antenna downtilt ina realistic scenario is considered. The antenna downtilt will in general controlthe distribution of inter-site interference in the network. By varying the antennadowntilt, one controls the boundary of the cell with the neighboring sites. Un-derstanding how the antenna downtilt will impact the users within its coveragearea and also understanding the interference caused to the neighboring cell isvery important. The results of thesis work will act as a pre-study for the on-lineoptimization of antenna parameters in a Long Term Evolution (LTE) networkscenario.

1.2 Antenna FundamentalsAn antenna is used to convert the guided waves in a feeder cable or a transmissionline into a radiating wave traveling in free space. The pattern in which the radi-

4 Introduction

�

�

�

��

�

�

Figure 1.1. Coordinate system for antenna calculations.

ating wave travels in the free space can be controlled by using different antennaparameters.

1.2.1 Antenna ParametersSome of the terminology related to the antennas is used repeatedly throughoutthis thesis. A brief explanation of these parameters is given in this sub-section.The diagram indicating the coordinate system for the antenna calculations is givenin the Figure 1.1.

Antenna radiation pattern is the plot of radiated power from an antenna perunit solid angle. In other words, it is a plot of the power radiated from an antennaper radiation intensity of the antenna, represented by U. This radiation intensityof the antenna is given by [1],

U = r2(

12EθH

∗φr̂

)= Z0

2

(kI(0)L

4π

)sin2θ (1.1)

where:

• U - radiation intensity of the antenna,

• r - radius of the sphere on which the measurement is done,

• Eθ - electric field component along the Poynting direction,

• Hφ - magnetic field component along the Poynting direction,

• r̂ - unit vector in the Poynting direction,

1.2 Antenna Fundamentals 5

• Z0 - wave impedance = 120π,

• k - wave number = 2πλ ,

• I(0) - input current to the antenna,

• L - length of the wire,

• θ - polar angle.

The antenna parameters that define the radiation pattern are explained verybriefly below.

• Poynting vector describes the magnitude and direction of the power flowcarried by the wave per square meter of area, and is measured in watts persquare meter.

• Directivity of an antenna is the ratio of radiation intensity of the antennain a particular direction to the radiation intensity of an isotropic antennaradiating with same total power.

• Side lobe level is the amplitude of the biggest side lobe expressed in decibelsrelative to the peak of the main lobe.

• Front-to-back ratio is the ratio of the peak amplitudes of the main lobe andback lobes expressed in decibels.

• Antenna downtilt is the direction of the main lobe in the vertical directionwith positive values for the down side tilting of the main lobe. The down-tilting of the antenna radiation pattern can be done either by mechanicaldowntilting or by electrical downtilting. In case of mechanical downtilting,with changes in the downtilt values, there will be a variation in the horizon-tal radiation pattern of the antenna. In electrical downtilting, only verticalantenna radiation pattern is affected. In this report, downtilt and electricaldowntilt terms are used interchangeably.

• Antenna azimuth orientation is the direction of the main lobe in the hor-izontal direction with positive values for the clockwise measurements fromthe horizontal axis.

• Half power beamwidth is the angle subtended by the half power points ofthe main lobe.

1.2.2 Directional Antenna Radiation PatternIn order to reuse the available spectrum efficiently and also to improve the pathgain in a particular direction in comparison with other directions, modern radionetworks use directional antennas. A directional antenna radiation pattern canbe obtained by using antenna arrays. By increasing the number of elements inan antenna array, one can achieve highly directional radiation patterns. As an

6 Introduction

Figure 1.2. An example radiation pattern indicating some of the antenna parameters.

example, the antenna radiation pattern obtained by a seven element linear antennaarray is shown in Figure 1.2.

Ideally, the total instantaneous radiated power at a point is found by inte-grating the instantaneous Poynting vector over the spherical surface. In order tominimize the involved calculations, a simplified antenna radiation pattern modelis used as suggested by 3GPP model [8] and is given by:

A(θ, φ) = −min [− (AH(φ) +AV (θ)) , Am] , (1.2)

where, AH(φ) and AV (θ) are the horizontal and the vertical antenna radiationpatterns respectively. The vertical and the horizontal antenna radiation patternsare given by:

AV (θ) = −min[

12(θ − θtiltθ3dB

)2, SLAv

], (1.3)

AH(φ) = −min[

12(φ− φoriφ3dB

)2, Am

]. (1.4)

where:

• θ - elevation angle from the user’s current location to the cell,

• φ - azimuth angle from the user’s current location to the cell,

• θtilt - antenna downtilt at the base station cell,

• φori - azimuth orientation at the base station cell,

1.3 Literature Review 7

• θ3dB - vertical half power beamwidth,

• φ3dB - horizontal half power beamwidth,

• SLAv - side lobe attenuation = 20dB,

• Am - front to back ratio = 25.

This approximation of the antenna radiation pattern will help in reducing themathematical operations while taking antenna directivity losses into account.

1.3 Literature ReviewFair amount of effort has been invested in optimizing the antenna parameters, an-tenna downtilt in particular, to suit the changing network demands. Brief overviewof some of these existing literature is provided in this section.

A detailed analysis of the vertical antenna downtilt optimization for LTE basestations is carried out by Eckhardt et al. [2]. In this work, the authors discuss themulti-cell optimization technique for a three-sector LTE network by consideringspectral efficiency as the optimization objective. Here, the cell-edge users aregiven higher priority in order to improve the coverage of the cell. Up to 16 cellsare considered for optimization at a given time instance. The authors illustratethe effect of optimization with respect to the utility per sector and also its impactduring a cell outage. The results show that the average spectral efficiency increasesby 10% and the spectral efficiency at the cell edge (5th percentile) will increase byup to 100% after the optimization.

Impact of the combined optimization of antenna downtilt and horizontal andvertical half power beamwidths is given by Yilmaz et al. [3]. In this work, the au-thors discuss the multi-variable optimization for cell coverage and capacity underuniform user distribution for full buffer traffic. The behavior of these parameters isconsidered for two different inter-site distance scenarios, namely 500m and 1732m.The results show that the optimal downtilt angle is independent of the horizontalhalf power beamwidth in both the cases. The results also show that, in an interfer-ence limited scenario, a wider horizontal beamwidth and a greater downtilt anglewill provide a better coverage and capacity performances. In the noise limitedscenario, the downtilt angle and horizontal beamwidth will not have much impactto a large range of values. The results also show that the optimal parameters willvary for noise limited and interference limited scenarios. All results are given infurther detail in [4].

A detailed study of the antenna parameter optimization involving the antennadowntilt and the elevation beamwidth is carried out by Athley et al. [5] . In thiswork, the authors propose a default antenna downtilt setting depending on theinter-site distances and the antenna elevation beamwidth. The results also showthe coupling between the optimal antenna downtilt and the antenna elevationbeamwidth.

8 Introduction

1.4 Thesis ContributionIn all the existing literatures that addresses the antenna downtilt related issues, thepropagation model used do not consider the direct and the reflected rays separately.In these literatures, a Okumura-Hata like propagation model is considered. In areal network scenario, the propagation will be more complex with many buildingsin an urban scenario. There will be some locations experiencing Line Pf Sight(LOS) connection with a base station and also some regions that experience deepshadowing. Presence of the buildings will also impact the path between the basestation and a User Equipment (UE) and there by changing the understanding ofthe optimal downtilt angle. In this thesis work, a more realistic urban dual raypropagation model is considered for studying the impact of antenna downtilt onthe network performance. The ideas behind this propagation model are explainedin [6] and [7].

1.5 Thesis OrganizationThe report is divided into further siz different chapters. In the chapter 2, the prob-lem that is being addressed in the thesis is discussed and the approach adoptedduring the thesis is presented. Chapter 3 covers the modeling of the antenna radia-tion pattern and also discusses many other parameter settings. Chapter 4 explainsthe method used to understand the impact of the antenna downtilt changes on thenetwork performance is presented. In the chapter 5, the results of changing theantenna downtilt are presented. In the chapter 6, the relation of the antenna down-tilt to the antenna elevation beamwidth is discussed. In the concluding chapter 7,the conclusions that are drawn from the thesis and the possible future works inthis field are discussed.

Chapter 2

Problem Formulation

In a densely loaded urban network, antenna downtilt can act as a crucial parameterin improving the network performance. This section provides the aim of the studyand also the methodology involved in deriving the results.

2.1 Aim of the StudyThe aim of this study can be listed as follows:

1. Incorporate relevant changes in the existing propagation model in the simu-lator that suits the urban environment and also the ones which help in un-derstanding the impact of antenna parameters on the network performance.

2. Study the impact of different locations of hotspot in the network on theoptimal antenna downtilt angle.

3. Study the impact of the elevation HPBW on the network performance andalso study how it affects the optimal downtilt angle.

4. Derive the conclusions from the study that can be used for the self planningand self optimization processes of a SON network.

The above mentioned tasks need to be carried out in a systematic way forwhich the following methodology is adapted.

2.2 MethodologyAntenna downtilt plays an important role in shaping the inter-site interference dis-tribution. As the traffic load varies continuously with time and with geographicallocation, it is important to understand how the antenna downtilt can be tunedaccordingly. A systematic approach for studying the impact of antenna downtiltinvolves several key steps. They are briefly explained in the following sub-sections.

9

10 Problem Formulation

2.2.1 Simulation ScenarioThe first step in carrying out this study is to establish a simulation set up thatdepicts an urban environment. After that, parameters related to an urban networklike loading on the network and default antenna parameter settings of all the cellsin the network also needs to be derived. By the end of this study, one should beable to simulate an urban environment that can be used for antenna parameterrelated studies.

2.2.2 Observability StudyThe operators will try to optimize the performance of the network by trying to finetune the parameters available at their disposal. One such tunable parameter isantenna downtilt. From an operator’s point of view, it is important to decide whatneeds to be improved or optimized in the network. Once the optimization objec-tive is decided then its relation to the antenna downtilt needs to be understood.Optimization objectives can be cell throughput based (as an example, improvingthe average spectral efficiency in the cell) or cell coverage based (as an example,improving the 5th percentile user spectral efficiency in the cell) or it can just beimproving the user experience (as an example, Signal to Interference and NoiseRatio (SINR) improvement).

There is also a need to establish a region of observation in which one can observethe changes in the network performance while tuning the antenna parameters.This will help in understanding the extent of the impact of changing the antennaparameter. Considering a too small region might show large improvement ordegradation during the modification of the antenna downtilt value but it mightnot cover the impact on the overall network. Whereas, considering a large regionmight not help in observing the impact of changes as it might average out theresults and therefore, acts as a poor feedback. Therefore, a region of observationneeds to be decided before trying to understand how antenna downtilt is affectingthe network performance.

2.2.3 Controllability StudyThis study will involve understanding of the impact of vertical radiation patternof the antenna on the network performance. This study can be split into followingsub studies.

1. Study the impact of different locations of the hotspot in a cell on its optimaldowntilt angle. Study the relation between the position of the hotspot andthe optimal downtilt angle.

2. Study the impact of smaller and larger elevation HPBW on the optimaldowntilt angle.

3. Draw conclusions from the above mentioned studies. This study will alsolist all the observations that can be seen in the results and how can they beused in the future works in this field.

2.3 Scope and Limitation 11

2.3 Scope and LimitationNot all the network scenarios are considered for evaluation in this thesis. A veryspecific set of simulation parameters is used for analysis. The parameters arechosen carefully to depict a realistic network scenario. Some of the scope andlimitations of the thesis work are,

• Only downlink direction of transmission is considered for evaluation. Theoptimal downtilt value can be different for downlink and uplink direction oftransmission.

• Users considered in the simulator are static in nature i.e. the users will arriveat a particular location and they continue to remain at the same locationuntil the fulfillment of their request.

• Users in the network are always assumed to be outdoor users. No indoorrelated losses are added in the simulations. Also, as all the users in thenetwork are outdoor users, they are assumed to be at the ground level.

Some of the limitations imposed here will help in reducing the variances inthe measured values which can potentially make it difficult to see the impact ofantenna downtilt. Such reduction in variances will help in easier understanding ofthe impact of the antenna parameters.

Chapter 3

Simulation Scenario

A Matlab based simulator is used to carry out the simulations during the thesiswork. The simulator depicts the entire network in a time-dynamic fashion, i.e., ittries to mimic a real network environment for every specified interval of time.

Different network parameters can have different effects on the optimality ofthe antenna downtilt. For example, if we consider intersite distance as a networkparameter, then the optimal antenna downtilt value decreases with the increasein the intersite distance. This is due to the larger coverage requirement per cellwith the increase in the intersite distance. Also, mean height of the buildings in acity will also have an impact on the optimal downtilt angle. Here, a larger meanbuilding height will force a smaller downtilt angle to be the optimal downtilt value.Therefore, it is important to declare the simulation settings that are used in thesimulations to study the impact of the antenna downtilt.

3.1 Basic Simulation SettingsSome of the common network settings that are used for all the network scenariosstudied during this thesis work are given in Table 3.1.

The wrap around functionality will ensure that the region that is far away fromthe central cell will also experience the effects of changing the antenna parameterin the center cell. A plot indicating the network deployment with part of itswrap around copies is shown in the Figure 3.1. The blue colored cells are thecorresponding center cells of each of the wrap around copies. The gray colored cellsbelong to the center network region that is being investigated. In the remainingparts of the report, only central region shown in gray color is used for illustration,but the simulations are carried out with the wrap arounds.

All the base station antennas are initialized with the default antenna parametersettings given in Table 3.1. Only downlink is considered for the study during thethesis work. All the users arriving in the network will be expecting a downloadof 80,000 bits of data in the file transfer mode. The propagation model used isexplained in [6] and [7].

13

14 Simulation Scenario

Table 3.1. Common network parameter settings.

Number of macro sites 7Number of sectors per macro site 3Number of wrap around copies 7

Network bandwidth 10 MHzTraffic type FTP - small packets (80000 bits)Scheduling Round-robin

Transmission power of base station 20 WPropagation model Urban modelTransmission mode Downlink

Base station antenna height 28.5 mUE antenna height 1.5 m

Number of transmit antennas 2Number of receive antennas 2Default azimuth orientation 00

Default horizontal half power beamwidth 650

Default vertical half power beamwidth 80

Side lobe level 17 dBFront to back ratio 25

The parameters given in Table 3.1 can be used to derive different network per-formance parameters. For example, one can use Table 3.1 to calculate the numberof bits transmitted by the base station to a particular UE during a given timeinstant. This will depend upon the SINR experienced by the UE in the previoustime instant and this information will be fed back to the base station throughcontrol signaling (Channel Quality Information (CQI) information). Using theseparameters, the number of bits transmitted by the base station to a particular UEis given by,

NumBits = NumBitsperRE ∗NumREperSubband ∗NumSubband ∗ (1− ControlOverhead) (3.1)

where,

• ControlOverHead= 0.15, is the different control signaling overhead involvedin terms of bandwidth utilization,

• NumSubband = 50, for a 10 MHz bandwidth channel, each of size 180 KHz,

3.1 Basic Simulation Settings 15

Figure 3.1. Network deployment with parts of the wrap around copies being shown.

• NumREperSubband= 14*12 = 168, where 14 is the number of time slotsand 12 is the number of sub carriers.

The value of control overhead is pre calculated and its value is used during allcalculations although the actual control signaling is not simulated.

3.1.1 Network ScenariosBefore starting the antenna parameter tuning related study, the network scenariounder which the simulations are carried out needs to be defined. In the followingsub-sections we define the set of network scenarios that are investigated duringthe thesis work.

In an urban network, there will be high traffic density because of the presence oflarge number of users in the network. This will demand for a denser deployment ofthe base stations and therefore the inter-site distance in an urban network is small.The user distribution in the network will not be uniform and it is necessary to havehotspots while simulating an urban network. As the antenna parameter variationsin only one cell is considered for the analysis, the hotspots in the network aresimulated to be present in the cell which has the reconfigurable antenna setting.The density of arrival of users in the hotspot will determine how crowded a hotspotis in comparison with the neighboring area.

When an antenna parameter is changed in the cell, the network will continue


to have an impact because of the previous antenna parameter settings for a briefspan of time. This time span is called as queue settling time. Queue settling timeshould not be considered for performance evaluation of the new antenna parametersettings. The value of queue settling time is calculated by observing the time ittakes for the resource block utilization in a cell to reach a steady state value whenthe downtilt is changed from one value to another value.

The duration of observation of the network that captures all the impact of achange in the antenna parameter is called sampling time. This is the observedduration of the network for a given antenna parameter setting. This durationshould be long enough to average out any event that causes instantaneous largevariation of the network performance. Such events can be related to the sequenceof user arrival or the location of user arrival. Some of these specific networksettings used with respect to the urban scenario are given in Table 3.2.

Table 3.2. Urban network parameter settings.

Inter-site distance 500 mRadius of circular hotspot 40 m

Hotspot density 10Number of hotspots in center cell 1

Queue settling time 10 sSampling time 90 s

Load on the network 145 Mbits/s/km2

Default downtilt angle 100

0 50 100 150 2000

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Res

ourc

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RB utilization vs Load

Figure 3.2. Resource block utilization as a function of the load on the network, measuredin bits/s/km2, for the urban scenario.

3.1 Basic Simulation Settings 17

0 2 4 6 8 10 12 14 16 18 200

1

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6

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5th

perc

entil

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in b

its/s

Figure 3.3. 5th percentile user throughput as a function of common downtilt angle ofall the cells in the urban network scenario.

0 2 4 6 8 10 12 14 16 18 200.5

1

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Downtilt angle in degrees

Cel

l spe

ctra

l effi

cien

cy in

bits

/s/H

z

Figure 3.4. Spectral efficiency as a function of the downtilt angle value of all cells inthe urban network scenario.

Load on the network is set by considering the percentage of resource blockutilization while sweeping the load in small steps. In an urban network scenario, asmore traffic is expected, the load value which gives 50% resource block utilizationis considered for evaluation. This is obtained from the Figure 3.2. The load valuethat corresponds to 50% resource block utilization is 145 Mbits/s/km2.

The value for the default downtilt angle in all the cells is calculated by sweep-ing the downtilt angle from 00 to 200 for all the cells simultaneously in steps of10. The 5th percentile user throughput is used as a performance metric. Theobtained results are plotted as shown in the Figure 3.3. The results show thata downtilt of 100 can be chosen as the common downtilt in the network. As a


further conformance, the spectral efficiency of all the cells is plotted against thecommon downtilt angle in the Figure 3.4. The result from this figure will alsopoint towards having 100 as the default downtilt angle.

3.2 Antenna Radiation Pattern ModelingDifferent antenna models are supported in the simulator including the 3GPP model[8] and the Omni-directional model. The antenna model considered in this thesiswork is Horizontal-Vertical model (HV model). This model’s antenna gain val-ues in horizontal and vertical directions are given in Figure 3.5 and Figure 3.6respectively.

0 50 100 150 200 250 300 350 400−10

−5

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Horizontal angle in degrees

Hor

izon

tal a

nten

na d

irect

ivity

gai

n

Horizontal antenna directivity gain

Figure 3.5. Horizontal antenna radiation pattern of HV model.

3.2 Antenna Radiation Pattern Modeling 19

0 50 100 150 200 250 300 350 400−45

−40

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Vertical angle in degrees

Ver

tical

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enna

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ain

Vertical Antenna Directivity

Figure 3.6. Vertical antenna radiation pattern of HV model.

Chapter 4

Observability

Network service providers will continuously try to get the best out of the resourcesavailable at their disposal. A service provider initially needs to decide what to op-timize in the network. This optimization objective might be cell capacity centeredor cell coverage centered or it even might deal with improving the user experi-ence in the network. Therefore, it is important to initially define the objective ofoptimization. Once the objective of optimization is decided, the optimization algo-rithm can be initialized. In order to provide efficient feedback for this optimizationalgorithm, it is important to know the region of network that will get impactedwhile changing the parameters of a given cell. These two important aspects arediscussed in the subsequent sub-sections.

4.1 Optimization ObjectiveThe performance of a network can be measured in many different ways. In thisthesis work, two different optimization objectives are considered to analyze theimpact of changes in antenna downtilt on the performance of the network. Theyare:

1. 5th percentile user throughput

2. Spectral efficiency of the cell

The 5th percentile user throughput is used as the primary criterion for evalua-tion and when the results need more analysis, spectral efficiency is also considered.

4.1.1 5th Percentile User ThroughputThe path gain from the base station to the UE will depend on many factors. Bylooking at obtained path gain values from each cell, the serving cell is chosen tobe the one with the highest path gain to the UE (transmission power for all cellsis same as mentioned in Table 3.1). The SINR value as experienced by the UE is

21

22 Observability

the ratio of received power from the serving cell to the interference caused by thepower levels from all the other cells. It is given by,

SINRUE = PtGsN +

∑i 6=s(PtGi)

(4.1)

where,

• SINRUE - SINR as experienced by the UE,

• Pt - transmission power of the base station as given in Table 3.1,

• N - thermal noise power,

• Gs - total path gain from the serving cell to the UE,

• Gi -total path gain from the non-serving ith cell to the UE.

The total path gain used in the above calculations is the sum of the path gainfrom the ith cell to the UE (PGi) and the antenna directivity gain (ADi) and isgiven by,

Gi = (PGi +ADi) dB (4.2)

The SINR value so obtained is fed back to the base station not directly but inthe form of Channel Quality Information (CQI). In the simulations, SINR value isdirectly used to calculate the number of bits that can be sent through the channelin the next scheduled time slot for the UE. Depending on the total path gain asobserved by the user, the user throughput will vary. The user throughput is theratio of the number of bits requested by the user for downloading and the timetaken by the base station to full fill the user’s request. It is given by,

UserThroughput = Number of bits requested

LeavingT ime−ArrivalT ime(4.3)

In this equation, the LeavingT ime is the time instant when the last part ofthe download was fulfilled and ArrivalT ime is the time instant when the userrequested for the download.

4.1.2 Spectral Efficiency of the CellSpectral efficiency is the number of bits transmitted per second per unit bandwidthand is measured in bits/s/Hz. As the total path gain to the UEs gets worse, oneneeds more and more resource blocks to transmit the same amount of data andthis will bring down the overall spectral efficiency of the cell. Therefore, one canuse this as a measure of the performance of the network as well.

4.2 Region of Observability 23

−800 −600 −400 −200 0 200 400 600 800

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Figure 4.1. Bin positions that experience a change in the SINR value when the downtiltangle of the center cell is changed from 110 to 120 in an urban network scenario.

4.2 Region of ObservabilityChanging the value of the antenna downtilt in a given cell will not only affectits own performance but also the performance in the neighboring cells. This canbe illustrated with the help of the Figure 4.1. The brown and blue colored dotsin the figure indicate the bin positions that experienced a change in SINR valuewhen the downtilt of the center cell is changed from 110 to 120. The blue coloreddots indicate the bin positions that experienced an improvement in the SINR andthe brown colored bin positions indicate the degradation in SINR. This change isbecause of the variation of antenna directivity gain to those bin positions. The binpositions on the left side of the Figure 4.1 are affected because of the wrap aroundphenomenon. There are also bin positions very close to the central base stationbut in the opposite direction of its antenna propagation direction, and these areaffected by the back lobe of the vertical antenna radiation pattern as shown in theFigure 3.6. Therefore, it is necessary to establish a region in the network to lookfor changes while changing the parameters in the center cell.

There are many different ways in which one can choose the region of observ-ability. Selecting a large number of cells in the network will ensure that all theeffects of changing the parameters in the center cell are captured but it comes ata cost. When we include a large region for evaluation, we include the locationswhich will hardly see any change in their path gain values but they still continue tobe included in the evaluation process. These values might overshadow the actualimpacted region. Therefore, considering a large area will not help in having aneffective feedback mechanism for the optimization process. A very small regionwill not be sufficient to capture the significant changes that might take place in

24 Observability

Figure 4.2. Region of Observability - All the colored cells are considered for evaluation.

different regions of the network.In order to choose the region of observability, a study of the impact of antenna

downtilt of the center cell to all the cells is carried out individually. The 5thpercentile user throughput in each cell is considered as the performance metric.Using this data, the region of observability is decided and is shown in the Figure4.2. In the figure, the wrap-around cells are also considered. In order to simulatethe observability region using a single copy of the network, different set of cells (buthaving the same effect after wrap around) is considered. The region of observabilityfor the single copy of the network is shown in the Figure 4.3.

The center cell is denoted by orange color and the antenna downtilt of this cellis changed during evaluation. The cells that share immediate geographical borderwith this cell are shown in green color. The cells that are part of observabilityregion and share geographical boundaries with green colored cells are shown inyellow color. The cells that are part of observability region and share geographicalboundaries with yellow colored cells are shown in gray color. For yellow and graycolored cells, one has to look at the network with wrap around in mind (for wraparound, refer to the Figure 3.1).

Some of the key observations in this region of observability study are:

• There will be variations in the 5th percentile user throughput in all (orange,green, yellow and gray colored) cells belonging to observability region whilechanging the downtilt of center cell in the range 00 to 80.

• The impact of variations in the 5th percentile user throughput is limitedonly to orange, green and yellow cells while varying the downtilt values inthe range 80 to 140.

4.2 Region of Observability 25

Figure 4.3. Region of Observability - All the cells that are colored other than blue colorare considered for evaluation.

• Only orange and green cell’s 5th percentile user throughput will get impactedwhile changing the downtilt values in the range 140 to 200.

These observations are based on the changes in the 5th percentile user through-put values of each individual cell while sweeping the downtilt from 00 to 200 insteps of 10. When a cell continues to have the same 5th percentile user throughputvalue even while changing the downtilt of the center cell, then one can say thatthe particular cell has become insensitive to the downtilt of the center cell. Thereasoning behind this observation is that, the vertical antenna radiation patternwill hit the flooring value (referring to Figure 3.6) for distant users. This will makethese users insensitive to higher downtilt values of the center cell.

The region of observability as shown in the Figure 4.3 will take care of theentire downtilt range of 00 to 200. This region can be reduced based on theabove observations while having a smaller range of downtilt values of the centercell. In other words, one can choose the region of observability based on thedynamic downtilt range that is available during optimization based on the aboveobservations.

Chapter 5

Controllability

With the details of the simulation settings and the observable region already beingestablished, in this chapter, a detailed study of the impact of antenna downtilton the performance of the network and also the sensitivity analysis of antennadowntilt is carried out. The range in which the measured value is within 95%of its optimum value is calculated. The width of this interval can be used as ameasure of sensitivity under the given network scenario. In this study, all theantenna parameters of the center cell other than the antenna downtilt are keptconstant as per Table 3.1. The downtilt of the center cell is swept from 00 to 200

in steps of 10.The optimization objective considered for evaluation is the 5th percentile user

throughput. The Figure 5.1 indicates the different hotspot location scenarios thatare investigated. Three subplots along the first row indicate the scenarios where in,the hotspot is located close to the center cell’s deployment position. Three subplotsalong the second row indicate the scenarios where in the hotspot is located at adistance of 150 to 200 m away from the center cell deployment location. Thesubplots along the final row indicate the scenarios where in hotspot is located atthe cell edge.

The result of sweeping the downtilt angle of the center cell is shown in theFigure 5.2. Along with the 5th percentile user throughput, 50th and 95th percentileuser throughput are also plotted in the Figure 5.2. These results are in sync withrespect to the position of hotspots as shown in the Figure 5.1. More results withdifferent seeds are given in the Appendix A. Here, the seed refers to the seedused by the Matlab random generator. By running multiple simulations withdifferent seeds and having all other settings the same, one can ensure that theresults are not influenced by the instantaneous values (for example, user arrivalinto the network, location of arrival of new users, and assignment of path gainbased on the propagation model calculations).

In all the subplots of the Figure 5.2, the 95th percentile user throughput willalways be the same and also for all downtilt angles. This is due to the type oftraffic that is being used in the simulations and the quantization effect due to theallocation of number of bits to per resource element depending on the observed

27

28 Controllability

Figure 5.1. Investigated hotspot locations for the urban network scenario.

channel quality.As the 5th percentile user throughput is used as the network performance

measuring criterion, it is separately plotted in the Figure 5.2. Here, the bluecolored curves indicate the value of 5th percentile user throughput for each of thesimulation seeds and the red curve indicates the average value of 5th percentile userthroughput. This red colored curve can be taken as an input for the self-planningscenarios. The green colored horizontal line indicates the sensitivity region for thegiven hotspot scenario. It is the five percent below the highest 5th percentile userthroughput value. The individual values for each of the blue curves can be foundin the Appendix A.

In order to cross verify the results, the spectral efficiency of the cells that area part of the region of observability is shown in the Figure 5.4.

Some of the observations from the above results are mentioned in the followingsubsections.

5.1 Input towards Self Planning ScenarioIn the self-planning case, it is a safe bet to initialize the default downtilt angle fora new base station to 100 in an urban scenario that has the network parametersas mentioned in Table 3.1 and Table 3.2. The optimal downtilt value is always inthe range 90 to 130 (including the simulations with different seeds as mentionedin Appendix A) with 100 being within the 95th percentile value of the highest 5thpercentile user throughput.

The main reason behind this observation is that the optimal channel quality

5.1 Input towards Self Planning Scenario 29

0 10 200

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5th percentile

50th percentile95th percentile

Figure 5.2. User throughput as a function of downtilt angle for different hotspot sce-narios (the results are shown in the same order as the Figure 5.1 with respect to thelocations of hotspots).

Figure 5.3. Only 5th percentile user throughputs as a function of downtilt angle.

and the optimal load balancing will happen at different downtilt angles. In ahighly loaded network, it is important not to further overload any of the cells.

In the Figure 5.5, the 5th percentile of the SINR is shown as a function of thedowntilt of the center cell. In the Figure 5.6, the load distribution in terms ofnumber of users served per sampling time by each cell is shown as a function ofthe downtilt of the center cell. In the Figure 5.7, the corresponding 5th percentileuser throughput is shown as a function of the downtilt of the center cell. As can

30 Controllability

0 10 202

2.5

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Cel

l spe

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l effi

cien

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Figure 5.4. Spectral efficiency of cells for different hotspot scenarios.

be seen, not all the time we observe the optimal SINR to be at 100. During someof the cases, when the optimal downtilt is less than 100 (80 for the 8th subplot),the corresponding load distribution shows that the center cell will be overloaded.This will pull back the user throughput as users will start to spend more time inthe queues. Therefore, the optimal 5th percentile user throughput will result inbetween the optimal downtilt for the SINR values and the one that correspondsto the optimal load distribution in the network.

5.2 Impact of the Location of Hotspot on the Op-timality of Downtilt Angle

The location of hotspot will have a very small impact on the optimal downtilt valueas smaller downtilt angles (90 to110) are suitable for hotspots at the cell edge andlarger downtilt angles (100 to 130) are suitable for hotspots that are close to basestation.

5.3 Sensitivity AnalysisThe sensitivity of downtilt value is highly dependent on the particular scenarioof path gain values that the hotspot users are experiencing. From the results ofFigure 5.3 (and also from the Appendix A), it is clear that the sensitivity leveldiffers from one scenario to the other (it is not just the location of the hotspot thatmatters but the path loss distribution to the center cell and the highest interferesfor the hotspot users). The observations with respect to sensitivity of antennadowntilt are:

5.3 Sensitivity Analysis 31

0 10 200

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perc

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INR

in d

B

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Figure 5.5. 5th percentile SINR values in dB as a function of the downtilt of the centrecell.

Figure 5.6. Load distribution in terms of number of users served per sampling durationas a function of downtilt of the centre cell.

32 Controllability

0 10 200

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0

5x 10

6

Figure 5.7. 5th percentile user throughput as a function of the downtilt of the centrecell.

1. When the hotspot is located very close to the base station (within 100 m),the downtilt is not very sensitive to higher downtilt angles.

2. If the hotspot users have multiple base stations as large interferers then afterthe handover of hotspot users (at a particular downtilt) to one of these basestations, the overall 5th percentile user throughput will degrade drastically.The performance degradation will continue to happen even when the downtiltis large enough to have very small interference to the hotspot users. Thedegradation of 5th percentile user throughput is due to the fact that thenew cell has a different base station as its main interferer and changing thedowntilt value of the center cell will not have much impact on these usersSINR value. Such a result can be better explained using Figure 5.8, Figure5.9 and Figure 5.10.Figure 5.8 illustrates the network situation when the hotspot users (shown ina light brown square) are connected to the center cell (orange color). Theseusers are experiencing a large interference from the neighboring cells, 21stcell (green color) and 17th cell (blue color). When the downtilt of the centercell is increased, the hotspot users will get handed over to 21st cell. Figure5.9 shows the 5th percentile of the user throughput of each cell as a functionof the downtilt angle of the center cell. As can be seen in the figure, from 100

downtilt onwards, the 5th percentile user throughput of the 21st cell will getdegraded because of the handover of the hotspot users. There is also somedegradation in the 5th percentile of user throughput of 17th cell. Also, thenumber of users served by each cell during the sampling duration is given inFigure . It clearly shows that the degradation in quality is due to the largeadditional load that the 21st cell is serving. The cell acting as its largestinterferer is 17th cell.When handing over a large set of users from the center cell to the neighboring

5.3 Sensitivity Analysis 33

Figure 5.8. Network deployment showing the interferers to the hotspot users. Orangecolored cell is the center cell. Hotspot users will get handed over to green cell and thenblue cell will act as the largest interferer.

Figure 5.9. 5th percentile user throughput of selected cells.

cells, it is important to see the loading condition in the neighboring cell.By handing over a large set of users to a loaded cell, the probability of

34 Controllability

Figure 5.10. Number of users served by individual cells as a function of downtilt of thecenter cell.

degradation in the network performance increases.

5.4 Comparison with Okumura-Hata PropagationModel

A study with a different propagation model will help in comparing the impactof the antenna downtilt on the network performance. Therefore, an Okumura-Hata like model is taken as the reference propagation model to see how it impactsthe results. This Okumura-Hata like model is based on the free space path losscalculations and log normal fading losses associated with the UE. The total pathloss model used here is,

Total path loss = min(Distance loss+ Lognormal fading loss+ k,

Free space loss) (5.1)

where,

• k - a constant which depends on the network scenario (rural/urban),

• Distance loss - is log10(dα), with d representing the distance of UE from thebase station and α representing the path loss exponent,

• Lognormal loss - is Sigma ∗ ((√Ra ∗ (randn)) + (randn ∗ (

√1−Ra))), with

Sigma being the standard deviation and Ra is a constant.

5.4 Comparison with Okumura-Hata Propagation Model 35

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−6

−4

−2

0

2

4

6

8

10

Figure 5.11. Spatial SINR distribution (in dB scale) for Okumura-Hata propagationmodel.

• Free space loss - is −20log10

(4πfc

)− 10log10

(d2 + (hBS − hUE)2

), with f

being the operating frequency, c being the speed of light in vacuum, hBSand hUE being the height of base station and UE respectively.

From the equation, it is clear that two UE’s located next to one another are moreprobable to have similar path gain values towards a particular base station.

5.4.1 Comparison with Urban Propagation Model ResultsA plot of SINR values as experienced by the bin positions is given in the Figure 5.11and the Figure 5.12 for the Okumura-Hata model and Urban propagation modelrespectively. The downtilt angle of the center cell in both the cases is 100. Forthe Okumura-Hata model, as shown in 5.11, there is always a smooth variation ofSINR in the spatial domain. For the Urban propagation model, as shown in 5.12,there are some clear boundaries, especially visible in the hotspot region, wherethe smoothness of variation of SINR in the spatial domain is impacted. It is clearfrom this plot that the SINR distribution is different from Okumura-Hata modeland therefore previous study results cannot be directly compared with the currentresults.

With all the parameters set similar to the values given in Table 3.1 and Table3.2, and only changing the propagation model to Okumura-Hata, the downtilt wasswept from 00 to 200 in steps of 10 and the changes in the 5th percentile userthroughput is plotted in the Figure 5.13.

In case of Okumura-Hata model, diffraction is not taken into account. There-fore, in an ideal scenario, the optimal downtilt angle in the Urban propagation

36 Controllability

−500 0 500

−600

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20

21

Distance in meters

Dis

tanc

e in

met

ers

−6

−4

−2

0

2

4

6

8

10

Figure 5.12. Spatial SINR distribution (in dB scale) for Urban propagation model.

0 10 205

6

7

8x 10

6

5 10 15 205

6

7

8x 10

6

5 10 15 205

6

7

8x 10

6

0 10 205

6

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6

7

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0 10 205

6

7

8x 10

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5th

perc

entil

e us

er th

roug

hput

Figure 5.13. 5th percentile user throughput while sweeping the downtilt from 00 to 200

in steps of 10 in Okumura-Hata model.

5.4 Comparison with Okumura-Hata Propagation Model 37

model would have been smaller as the direct ray will have a smaller optimal anglein comparison to the Okumura-Hata model. Although smaller downtilt angle pro-vides a better path gain to the UE’s located close to the central base station, italso introduces a large interference in the network and also the center cell will becovering a large region in the network. Therefore, smaller downtilt angles will re-sult in a worse performance. As this explanation is true for every cell, the overallperformance will be lesser in Urban propagation model compared to Okumura-Hata model. This can be seen in the results as the optimal downtilt angle forOkumura-Hata model is in the range 6 to 8 Mbits/s. Whereas, similar value inUrban propagation model will be in the range 3 to 4 Mbits/s.

Chapter 6

Impact of ElevationBeamwidth on OptimalDowntilt

As per the previously carried out studies by Athley et.al. [5] and Yilmaz et.al.[4], elevation beamwidth plays an important role in the resulting optimal downtiltvalue in an urban scenario. Therefore, it is relevant to see how a realistic prop-agation model impacts the previously observed results. Ideally, a large value ofelevation beamwidth, will introduce higher interference to the neighboring cells.A smaller elevation beamwidth is suitable for pointing towards a particular regionlike hotspot. In this chapter, all the simulations are carried out as per the networksettings in Table 3.1 and Table 3.2.

6.1 Lower Elevation Beamwidth - 6.40

The default elevation HPBW of all the cells is changed from 80 to 6.40. This willhave an impact on the coverage area of the cell. When the elevation HPBW isdecreased, the beam will get narrower and the region where the vertical antennaradiation pattern (as shown in the Figure 3.6) gets the flooring value will be closerto the main beam direction.

With all the settings similar to the ones mentioned in Table 3.1 and Table 3.2(except for elevation HPBW which is set to 6.40), the downtilt of the center cell isswept from 00 to 200 in steps of 10. It should be observed that the antenna gainis increased from 18 dBi to 18.6 dBi to have the same input power. The resultsare plotted in the Figure 6.1. The individual results for each seed are given in theAppendix B.

As can be seen in the Figure 6.1, all the figures seem to be a shifted versionof the Figure 5.3. This is in sync with the observations from the study of Athleyet al. [5]. In that study, a relation between elevation HPBW and the antennadowntilt was derived and it was observed that a smaller elevation HPBW will

39

40 Impact of Elevation Beamwidth on Optimal Downtilt

Figure 6.1. 5th percentile user throughput as a function of downtilt angle for an eleva-tion beamwidth of 6.40.

have a smaller optimal downtilt angle. This observation is in sync with the resultsshowed in the Figure 6.1. Here, a value of 90 can be used as the optimal downtiltangle independent of the location of the hotspot. Also, the sensitivity of thedowntilt is similar to that of the 80 elevation beamwidth case in terms of thelocation of the hotspot.

The absolute values of the 5th percentile user throughput in this case are largerthan the ones observed for the 80 elevation HPBW in Figure 5.3. One possiblereason for this behaviour is that in smaller elevation HPBW case, the amount ofinterference caused by the neighbouring cells is lesser compared to a larger HPBWcase.

6.2 Higher Elevation Beamwidth - 100

In this study, the elevation HPBW value is changed from 80 to 100 and all theother parameters are set according to the Table 3.1 and Table 3.2. The simulationswere carried out by sweeping the antenna downtilt from 00 to 200 in steps of 10.It should be observed that the antenna gain is decreased from 18 dBi to 17.2 dBito have the same input power. The results are as shown in the Figure 6.2. Theindividual results for each seed are given in the Appendix C.

From the plots, it is clear that one can use 110 as the optimal downtilt an-gle. This is again in sync with the results obtained by Athley et al.[5]. Themajor difference here is that the antenna downtilt becomes very sensitive. This ismainly because of the larger interference introduced by each of the cells to theirneighbours. Also, whenever a handover happens, the handed over users will con-

6.2 Higher Elevation Beamwidth - 100 41

Figure 6.2. 5th percentile user throughput as a function of downtilt angle for an eleva-tion beamwidth of 100.

tinue to experience large interference from the centre cell and thus, will experiencepoor throughput. This will result in poor 5th percentile user throughput in thesurrounding cells that became the serving cells for the handed over users.

The absolute values of the 5th percentile user throughput is much lesser com-pared to the other two cases. This is again mainly due to the larger interferencelevels from the neighbouring cells.

Chapter 7

Conclusions and FutureWork

This chapter summarizes both the results and also possible extensions to the studythat is carried out in the thesis.

7.1 ConclusionThe antenna downtilt clearly impacts the network performance to a large extent.The conclusions drawn in the controllability study of antenna downtilt can besummarized in three parts:

1. Optimal downtilt angle in the presence of a hotspot.

2. Sensitivity of downtilt depending on the location of hotspot.

3. Impact of elevation beamwidth on the optimal downtilt angle.

7.1.1 Optimal Downtilt AngleIndependent of the location of the hotspot in the center cell, the optimal downtiltangle lies in the range of 90 to 130. If the hotspot is located close to the cellboundary, then the optimal downtilt angle will be in the range of 90 to 100. If thehotspot is located at the center of the cell then the optimal downtilt will be in therange of 100 to 120. If the hotspot is located very close to the base station thenthe optimal downtilt is in the range of 110 to 130.

7.1.2 Sensitivity of Downtilt for Different Hotspot LocationThe sensitivity of downtilt largely depends on the type of connection that thehotspot users are having with the center cell and also to some of its larger inter-fering cells. Some conclusions regarding the sensitivity analysis are:

43

44 Conclusions and Future Work

• When the hotspot is located very close (with in 50 m radius) to the basestation, then the downtilt is less sensitive to higher downtilt angles. Thisprovides a very large optimal or close to optimal region to work with.

• When the hotspot users are handed over from the center cell to the neigh-boring cell, it is important to see how the interference is distributed to theneighboring cells. If there are two or more cells having similar path gain val-ues to the UE’s in the hotspot and the center cell is about to handover theseUE’s to the neighboring cells, then it is safe not to handover them. In orderwords, the peak performance is already reached and any further downtiltingwill result in the degradation of network performance. The interference lev-els from the neighboring cells can bee estimated from the RSRP (ReferenceSignal Received Power) measurements by the UE. If one of the neighboringcell is already heavily loaded then it is safe not to handover more hotspotusers to that cell.

7.1.3 Impact of Elevation Beamwidth on Optimal DowntiltAngle

When the elevation beamwidth of the base station antenna is reduced from 80 to6.40, the optimal downtilt angle value will reduce in comparison with the largerelevation beamwidth. This behavior clearly shows a mutual coupling between theelevation beamwidth and the optimal downtilt angle. Therefore, the coupling asobserved by the previous studies using simpler propagation model is still validunder Urban propagation model.

7.2 Future WorkThere are many different scenarios when the impact of antenna downtilt can bedifferent from the observed results. Also, other antenna parameters will also im-pact the network performance. So, the possible further studies in this field aregiven in the subsequent sections.

7.2.1 Introduction of Indoor and Vertical Plane UsersIn the current propagation model, only outdoor users are assumed to be part ofthe network. By introducing the indoor users, who experience more path lossbecause of the wall penetration losses, the path loss distribution in the spatialdomain will change and makes it an interesting study. Also, by adding users atdifferent vertical planes (different floors of a building), there will be a situationwith different floors experiencing different path loss towards the base station.

7.2.2 Study of the Impact of using a Real Antenna ModelIn the HV-antenna model, the side lobe flooring value is modeled very high inorder to compensate for the scattering affect that is experienced in the near field

7.2 Future Work 45

of the UE. The changes in the side lobe flooring level will have an impact onthe interference distribution with respect to other cells. Also, the interpolationmethod used for combining the horizontal and vertical radiation pattern is notvery realistic. Therefore, a study that considers a more detailed three dimensionalantenna radiation pattern can be considered for analysis.

7.2.3 Study of the Impact of Antenna Downtilt for UplinkTransmission

In the study carried out during the thesis, only downlink direction of transmissionis considered. It would be interesting to see how the optimal downtilt angle isimpacted if the uplink transmission is considered.

7.2.4 Study of the Impact of Horizontal Radiation PatternA study for the horizontal radiation pattern can be carried out in order to under-stand the impact of azimuth orientation on intra-site interference. Also, one canfurther look into the mutual coupling between azimuth orientation and horizontalbeamwidth. It might also be interesting to see how coordinated changes in all thesectors of a site will act in addressing the network performance objectives.

Appendix A

5th percentile userthroughput for differentinitialization seeds andelevation beamwidth of 80

The measured 5th percentile user throughput can be influenced by many parame-ters that were part of the simulation. Some of theam are listed below.

1. Location of users in the network.

2. Sequence of arrival of new users in the network.

3. Randomness involved in the calculations of the path gain in the Urban prop-agation model. For example, street orientation is initialized randomly duringthe calculations. This will have an impact on the LOS probability for a UEalong that street.

In order to have more confidence on the obtained results, it is important torun more simulations with different seed for the random generator in the Matlab.The results with different seeds are given beolw.

47

48 5th percentile user throughput for different initialization seeds andelevation beamwidth of 80

0 10 200

5x 10

6

0 10 200

5x 10

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in b

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0

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6

First tier cellsObservability region

Figure A.1. Simulations with seed#1 for an elevation beamwidth of 80.

0 10 200

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49

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First tierObservability region



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51

0 10 200

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First tier only

Observability region


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First tier only



Appendix B

5th percentile userthroughput for differentinitialization seeds andelevation beamwidth of 6.40

Similar to the study carried out in Appendix A, the simulations were carried outfor multiple seeds with an elevation beamwidth of 6.40.

52

53

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First tier only


Figure B.1. Simulations with seed#1 for an elevation beamwidth of 6.40.

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54 5th percentile user throughput for different initialization seeds andelevation beamwidth of 6.40

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55

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Appendix C

5th percentile userthroughput for differentinitialization seeds andelevation beamwidth of 100

Similar to the study carried out in Appendix A, the simulations were carried outfor multiple seeds with an elevation beamwidth of 100.

56

57

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Figure C.1. Simulations with seed#1 for an elevation beamwidth of 100.

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Bibliography

[1] Simon R. Saunders, Alejandro Aragon-Zavala, "Antennas and propagation forwireless communication systems", second edition, John Wiley & Sons Ltd,2007.

[2] Harald Eckhardt, Siegfried Klein, and Markus Gruber, "Vertical antenna tiltoptimization for LTE base stations", Vehicular Technology Conference, Spring,2011.

[3] Osman N.C. Yilmaz, Seppo Hämäläinen and Jyri Hämäläinen, "Analysis ofantenna parameter optimization space for 3GPP LTE", Vehicular TechnologyConference, Fall, 2009.

[4] Osman Nuri Can Yilmaz, "Self-optimization of coverage and capacity in LTEusing adaptive antenna systems", Master Thesis, Aalto University, February2010.

[5] Fredrik Athley and Martin Johansson, "Base station antenna parameter op-timization for LTE", Ericsson internal report, EAB-08:087722 Uen, January2009.

[6] Jan-Erik Berg, "GRAYT version 27 September 2011", GRAYT sep28 2011Up-dated.ppt, Ericsson internal presentation, September 2011.

[7] Peter Nysten, "Radio wave propagation from LOS to street-level in an ur-ban area", Master of Science Thesis, Ericsson Research and Royal Institute ofTechnology, T/U 02:063, March 2002.

[8] 3rd Generation Partnership Project; Technical Specification Group Radio Ac-cess Network; Further Advancements for EUTRA, Physical Layer Aspects (Re-lease 9), 3GPP TR 36.814 v0.4.1(2009-02).

[9] Lars Ahlin, Jens Zander, Ben Slimane, "Principles of wireless communications",Studentlitteratur, 2006.

[10] Chip Reinhardt, "Taxi cab geometry: history and applications", TMME, Vol2,no.1,December 2003.

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