propagation modeling and lte network performance in real city …1233146/... · 2018. 7. 16. · a...

UPTEC F 18045

Examensarbete 30 hpJuni 2018

Propagation Modeling and LTE Network Performance in Real City Scenarios

Sebastian Vestberg

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

Abstract

Propagation Modeling and LTE Network Performancein Real City Scenarios

Sebastian Vestberg

Maps of chosen areas in Chicago, San José, London and Shibuya, are imported from Open Street Map into matlab in order to run LTE network simulations for various scenarios. Firstly, two path loss models are compared, the empirically based WINNER model and a set of site-specific model. Secondly, low load network simulations are run separately at two different carrier frequencies, 700MHz and 2GHz, for city specific base station deployments. Simulation results show that user performance is quite unique for each city and that deployment strategies and city environments are strongly influencing path gain, SINR and throughput. In general, user performance in UL is significantly worse at 2GHz than at 700MHz, whereas DL performance is not as affected by the change in carrier frequency.

ISSN: 1401-5757, UPTEC F 18045Examinator: Tomas NybergÄmnesgranskare: Mikael SternadHandledare: Gunther Auer

i

Populärvetenskaplig sammanfattning

I detta projekt har kartdata från Open Street Map använts i en matlab-baseradnätverkssimulator med syfte att analysera och jämföra effektförluster och täckn-ing både inomhus och utomhus mellan städer från olika kontinenter och med olikastadsstruktur. Följande stadsdelar importerades och simulerades; 1000x1000m2 avChicago, 4000x4000m2 av San José, 600x600m2 av London samt 400x400m2 av Shibuya.

Eftersom effektförluster för radiovågor är större för högre frekvenser så under-söktes hur två vanliga LTE-frekvenser 700MHZ och 2GHz skiljer sig vid sammabasstationsplacering i de utvalda städerna. Dessutom undersöktes hur två olikapropageringsmodeller, en statistisk och en deterministisk, skiljer sig vad gäller beräk-nandet av effektförluster.

Den deterministiska modellen som är utvecklad hos Ericsson kräver mycket ge-ometrisk information och de importerade byggnaderna, som representeras av poly-goner, måste följa simulatorns riktlinjer till punkt och pricka. Denna modell är valid-erad mot mätdata och ger väldigt bra indikation på hur stor signaldämpningen ärmellan basstation och användare i verkligheten. Den statistiska modellen är snab-bare men ger inte ett lika pålitligt resultat.

Överlag visar beräkningarna av effektförluster att den deterministiska modellenger ett mer optimistiskt resultat än den statistiska modellen, framförallt för utomhu-sanvändare som befinner sig på gator i tätbebyggda områden såsom centrala Lon-don och Chicago. I de påföljande nätverkssimuleringarna användes enbart de beräk-nade värdena från den deterministiska modellen. Simuleringsresultaten visade attöverföringshastigheter i DL hos utomhusanvändare är lika för de båda frekvenserna.Överföring i UL försämras dock avsevärt för inomhusanvändare när frekvensen är2GHz. Städernas olika utseende och individuella placering av basstationer resulter-ade i att Chicago hade sämst inomhustäckning i UL och Shibuya hade lägst över-föringshastigheter i DL. Däremot hade Shibuya de högsta överföringshastigheternai UL av de fyra stadsdelarna.

ii

AcknowledgementsFirstly I would like to send a million thanks to Gunther Auer for being the bestsupervisor a student could ever wish for. Your knowledge, patience and positiveattitude have been extremely valuable for me throughout this thesis.

I would also like to express my sincere gratitude to Dirk Gerstenberger. Yourkindness and humbleness are striking and I am very proud to have been a part ofyour brilliant team at Ericsson.

Finally I would like to thank Mikael Sternad. Apart from giving great and in-spiring lectures at Uppsala University you are always so kind and helpful.

iii

Contents

Acknowledgements ii

1 Introduction 11.1 Historical Background of Mobile Communications . . . . . . . . . . . . 11.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Theory 32.1 Propagation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Links and Paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.1.2 Path Loss and Path Gain . . . . . . . . . . . . . . . . . . . . . . . 32.1.3 Free-space Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.4 Deterministic Models . . . . . . . . . . . . . . . . . . . . . . . . 5

Diffraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.5 Statistical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.6 WINNER Path Gain Models . . . . . . . . . . . . . . . . . . . . . 52.1.7 A Set of Site-Specific Path Gain Models . . . . . . . . . . . . . . 7

Model for propagation above terrain and buildings . . . . . . . 7Model for propagation around buildings . . . . . . . . . . . . . 8Foliage model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Outdoor-to-indoor model . . . . . . . . . . . . . . . . . . . . . . 9Additional stochastic model . . . . . . . . . . . . . . . . . . . . . 9

2.2 SNR, SINR and Channel Capacity . . . . . . . . . . . . . . . . . . . . . 92.2.1 SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.2 SINR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.3 Channel Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Long Term Evolution (LTE) . . . . . . . . . . . . . . . . . . . . . . . . . 102.3.1 LTE-framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Cyclic Prefix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11TDD and FDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Method 123.1 Map Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.1 OSM import . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.1.2 Polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Network Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2.1 Deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14San José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15London . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.2.2 System Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 18

iv

4 Results 194.1 Path Gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.1.1 Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.1.2 San José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.1.3 London . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.1.4 Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2 Network Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.2.1 Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.2.2 San José . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2.3 London . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.2.4 Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Indoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Outdoor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5 Conclusions 39

Bibliography 41

v

List of Figures

2.1 Illustration of Half Screens . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 TDD and FDD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1 Deployment in Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.2 Deployment in San José . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.3 Deployment in London . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.4 Deployment in Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.1 3D view of PG in Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2 2D view of PG in Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . 214.3 CDF of PG in Chicago . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.4 2D view of PG in San José . . . . . . . . . . . . . . . . . . . . . . . . . . 234.5 CDF of PG in San José . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.6 3D view of PG in London . . . . . . . . . . . . . . . . . . . . . . . . . . 254.7 2D view of PG in London . . . . . . . . . . . . . . . . . . . . . . . . . . 254.8 CDF of PG in London . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.9 3D view of PG in Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . 274.10 2D view of PG in Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . 284.11 CDF of PG in Shibuya . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.12 UL and DL performance for Chicago in 3D . . . . . . . . . . . . . . . . 304.13 UL and DL performance for Chicago in 2D . . . . . . . . . . . . . . . . 314.14 CDF of SINR and Throughput in Chicago . . . . . . . . . . . . . . . . . 314.15 UL and DL performance for San José in 2D . . . . . . . . . . . . . . . . 324.16 CDF of SINR and Throughput in San José . . . . . . . . . . . . . . . . . 334.17 UL and DL performance for London in 3D . . . . . . . . . . . . . . . . 344.18 UL and DL performance for London in 2D . . . . . . . . . . . . . . . . 354.19 CDF of SINR and Throughput in London . . . . . . . . . . . . . . . . . 354.20 UL and DL performance for Shibuya in 3D . . . . . . . . . . . . . . . . 374.21 UL and DL performance for Shibuya in 2D . . . . . . . . . . . . . . . . 374.22 CDF of SINR and Throughput in Shibuya . . . . . . . . . . . . . . . . . 38

vi

List of Tables

2.1 Path loss models for C2- and D1 WINNER propagation scenarios . . . 6

3.1 Parameters for two LTE systems with different carrier frequencies andbandwidth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

vii

List of Abbreviations

AGL above ground levelbps bits per secondBS base stationBW bandwidthCDF cumulative distribution functionDL downlinkFDD frequency division duplexFFT fast fourier transformFSPL free space path lossGP guard periodIFFT inverse fast fourier transformISD intersite distanceISI intersymbol interferenceITU international telecommunications unionLOS line of sightLTE long term evolutionNLOS non line of sightOFDM orthogonal frequency division multiplexingOFDMA orthogonal frequency division multiple accessOSM open street mapPG path gainPL path lossSINR signal to interference and noise ratioSNR signal to noise ratioTDD time division duplexUE user equipmentUL uplink

1

Chapter 1

Introduction

Today billions of smartphones are connected through our complex mobile communi-cation networks across the globe. For the new generation of users it’s almost difficultto imagine a life without these devices providing us with fast internet access. Manycould surely think of situations and locations when mobile broadband coverage isextra poor. Rural areas with base stations deployed far from each other sometimesprovide low data rates, but also dense venues such as crowded sport arenas causelarge problems in our existing networks. By combining available systems, such as2G, 3G and LTE-advanced, and optimally deploy broadband antennas for placeswith certain local traffic demands could help overcome these problems. Currentlythe world is preparing for the upcoming 5G release including many new featureslikely to enhance user experience greatly, but since older releases will remain in ournetworks for a long time it’s also important to continue analyzing possible improve-ments for these systems. This chapter will give the reader a brief background ofmobile communications followed by a description of the thesis objectives.

1.1 Historical Background of Mobile Communications

Ever since the 1G cellular telephones were standardized in the 1980s, new techniquesand features have constantly been added to earlier releases to improve and reshapewireless networks to what we currently have. Earlier mobile communication stan-dards rarely occupy frequency bands above 1GHz and when the first European GSM2G networks were released in the early 1990s, peak data rates hardly reached overa couple of hundreds of kbps. Enhancements of 2G, such as GPRS and EDGE, in-creased data rates up to almost 400 kbps by improving network architecture as wellas modulation and coding techniques.

In the early 2000s the international telecommunications union (ITU) releasedtheir standard, IMT-2000, including technical specifications for the 3G standards.3G systems have peak data rates of a few Mbps, and mainly two incompatiblestandards are dominating the market, cdma2000 and W-CDMA, both using CDMA-techniques.

3GPP is an organization working towards backward and forward compatibilitybetween different standard releases worldwide. With the 3GPP long term evolution(LTE) release 8 in December 2008, many of the 4G requirements were fulfilled. LTE-Advanced, however, is the system closest to the "true 4G". For the LTE system the3GPP requires, for instance, peak data rates of 100Mbps, increased spectral efficiencyand OFDM modulation. LTE is described in more detail in section 2.3, and is thesystem that is used in all scenarios in this thesis.

Chapter 1. Introduction 2

1.2 Objective

The main objective of this thesis work is to investigate LTE network performancein real cities with realistic building data imported from Open Street Map (OSM).Two different path gain (PG) models, the statistical WINNER model and a set ofsite-specific models, are estimating signal strengths in four city areas from variouscontinents; Chicago, San José, London and Shibuya. The, by the site-specific model,calculated PG is further used as input in the throughput simulations. Two commoncarrier frequencies for LTE systems, 700MHz and 2GHz, are simulated separately foreach city to capture understanding in how local city environments and base station(BS) deployments are affecting network performance differently.

The data from OSM is copyrighted c© OpenStreetMap contributors and is avail-able under the Open Database License. For more information, see https://www.openstreetmap.org

https://www.openstreetmap.org

https://www.openstreetmap.org

3

Chapter 2

Theory

In order to prepare the reader for the network analysis part a few fundamental con-cepts will be introduced in this chapter. The choice of propagation model is one ofthe core aspects in system simulations so theory concerning channel prediction andpath loss modeling will be covered in the first section. Also, this section includesa detailed description of the two propagation models used in this thesis work. Thesecond section in this chapter is aiming at the throughput analysis of the simulationscenarios, and therefore includes important concepts in wireless communication the-ory along with an overview of the LTE systems.

2.1 Propagation Models

Maxwell formulated his famous electromagnetic equations, in detail describing ra-dio wave propagation, already in the late 19th century. Solving these equations ana-lytically is often not possible, and approximating propagation models must in suchcases be implemented instead. Propagation models mainly estimate signal strengthat the receiver antennas in environments that can be represented either stochasticallyor deterministically. Many models are also combining stochastic models with deter-ministic models in order to increase accuracy, such as the set of site-specific modelsused in this thesis (2.1.7).

2.1.1 Links and Paths

In wireless communications, a link is the channel connecting two nodes within anetwork. A link is most often between a user equipment (UE), such as a mobilephone or tablet, and its serving base station (BS), but it could also include a relaythat simply acts as a mid-link between UE and BS that only receives and retransmitsthe radio signal towards the desired destination. A path between a UE and BS couldtherefore consist of multiple links depending on the number of relays. However,relays are not included in this work.

A signal sent from a UE to a BS has an uplink (UL) direction and a signal trans-mitted from a BS to a UE has a downlink (DL) direction.

2.1.2 Path Loss and Path Gain

Assume that the transmitted signal s(t) and the received signal r(t) are modeled as

s(t) = <{u(t)ej2π fct} (2.1)

r(t) = <{v(t)ej2π fct}+ n(t), (2.2)

Chapter 2. Theory 4

where u(t) is a baseband signal known as the complex envelope of s(t), fc is the carrierfrequency and n(t) is the noise. The real part of u(t) is called in-phase component,and the imaginary part is called quadrature component. In the received signal, v(t)is channel dependent, and in the most simplistic channel models only a complexrescaling of u(t).

If s(t) has power Pt and r(t) has power Pr, then path loss (PL) and path gain (PG)in dB are defined as

PL = 10 log10Pt

Pr(2.3)

PG = −PL. (2.4)

Thus, received signal power Pr can be calculated according to the following formulawhen Pt and PL are known:

Pr =Pt

10PL/10 . (2.5)

Signal power variation on the receiving side is in this work calculated in PG,although channel model descriptions perhaps more often refer to PL. Note also thatthe noise term is not included in PL.

If no obstacles are nearby a signal that is propagating through free space, thenthe signal is attenuated according to the free-space propagation law. In our real-world networks, however, all types of objects (houses, trees, hills etc.) attenuatethe transmitted signal on its way towards the receiver. Diffractions, reflections, wallpenetrations, shadowing effects etc. must therefore be taken into account in the PLmodels.

The simplest of all propagation models is the Free-Space Path Loss (FSPL), ex-plained in next subsection , which is assuming a line-of-sight (LOS) channel betweenthe transmitter and receiver antennas.

2.1.3 Free-space Path Loss

If the signal model in previous subsection is used, the following formula is describ-ing v(t) when the transmitted signal is propagating through free space over a dis-tance d without any obstructions:

v(t) =λ√

Gle−j2πd/λ

4πdu(t), (2.6)

where λ = c/ fc is the wavelength and Gl is an antenna gain factor depending onthe radiation patterns. This channel model introduces only a phase- or amplitudeshift on the modulated baseband signal u(t) depending on λ and d and the resultingpower ratio between Pt and Pr becomes:

Pr

Pt=

[λ√

Gl

4πd

]2

. (2.7)

By using this power ratio, the free-space path loss (FSPL) can be obtained as

Chapter 2. Theory 5

FSPL = 20 log4πdλ√

Gl(2.8)

where Gl = 1 for isotropic antennas.

2.1.4 Deterministic Models

Deterministic propagation models are site-specific and therefore require geometricalinformation of, for instance, buildings, terrain and foliage. Ray-tracing methods areessentially used for urban areas where site-specific propagation modeling is neededfor performance evaluation of local areas with high densification [1]. One of themost fundamental parts of site-specific modeling is knife-edge diffraction, which isapproximating the loss in signal strength due to diffraction around buildings.

Diffraction

Diffraction occurs when a signal propagates over an edge, which is causing PL that isincreasing with the angle of the bent signal. This is often approximated in determin-istic propagation models with the Fresnel knife-edge diffraction model. If the diffractedsignal travels an additional distance ∆d relative the LOS-path component, then theFresnel-Kirchhoff diffraction parameter v is described as

v = 2√(∆d/λ). (2.9)

The PL, in this case called Fresnel-loss, can be calculated as a function of v [4].

2.1.5 Statistical Models

Although deterministic site-specific models are more accurate, they are often moretime consuming and computationally heavy compared to stochastic propagationmodels. The statistical models are based on empirical measurements to fit param-eters for certain scenarios and are often limited to specific environments, path dis-tances, frequencies etc. For instance, shadowing effects are modeled statisticallyusing log-normal distributions in both the empirically based WINNER model andthe set of site-specific models used in this thesis.

2.1.6 WINNER Path Gain Models

The WINNER, in this case WINNER II, channel model is a geometry based stochasticpropagation model that can enable both link and system level simulations [2]. Allchannel parameters, such as delay spread, angle-of-departure, angle-of-arrival andshadow fading (all large scale parameters), are stochastic and based on parameterdistributions. Channel models are defined for several different propagation sce-narios, each containing specific parameter distributions derived from real channelmeasurements.

Some propagation scenarios cover both LOS and NLOS links whereas otherscover only one of these types depending on the environment. For instance, the badurban macro-cell scenario (C3) only considers NLOS links since building heightsand building density are assumed to be very inhomogeneous. In this scenario base

Chapter 2. Theory 6

TABLE 2.1: Path loss models for C2- and D1 WINNER propagationscenarios

Path loss models for WINNER C2 and D1 scenariosScenario d LOS

orNLOS

Path Loss

C2 d > dBPC2 LOS 40 log d + 13.47 − 14 log hBS − 14 log hUE +

6 log fc5

C2 d < dBPC2 LOS 26 log d + 39 + 20 log fc5

C2 all d NLOS (44.9 − 6.55 log hBS) log d + 34.46 +

5.83 log hBS + 23 log fc5

D1 d > dBPD1 LOS 40 log d + 10.5 − 18.5 log hBS − 18.5 log hUE +

1.5 log fc5

D1 d < dBPD1 LOS 21.5 log d + 44.2 + 20 log fc5

D1 all d NLOS 25.1 log d + 55.4 − 0.13(hBS − 25) log d100 −

0.9(hUE − 1.5) + 21.3 log fc5

stations are assumed to be placed just above average building height although high-rise buildings in the area can exceed this height. For the urban macro-cell scenario(C2) the base stations are assumed to be deployed above the roof tops in a city areawith homogeneous building heights.

In this work, the propagation scenario used for the cities Chicago, London andShibuya is the urban macro (C2) scenario for WINNER PL calculations. For PL cal-culations in the chosen residential part of San José, rural macro (D1) scenario is used.Although each scenario has its unique formula for PL calculation, all models are ofthe following form:

PL = A log d + B + C logfc

5+ X, (2.10)

where d is the distance in meter between the transmitter and receiver, and fc isthe carrier frequency in GHz.

The variables A, B, C and X are scenario specific. For instance, FSPL is modeledwith A=20, B=46.4, C=20 and X=0. The term X is in many cases set to zero. Table 2.1summarizes the PL models for the two scenarios, C2- and D1, used in this thesis. Foreach scenario, LOS and NLOS links are modeled differently. Additionaly, a scenariospecific breakpoint distance dBP is determining the choice of PL model for a certainlink. Clearly, base station antenna height hBS and user equipment height hUE are twomain variables in the PL models.

The two breakpoint distances dBPC2 and dBPD1 are calculated from:

dBPC2 = 4(hBS − 1)(hUE − 1) fc/c (2.11)dBPD1 = 4hBShUE fc/c, (2.12)

where c=3×108 is the free-space propagation velocity in [m/s].In the WINNER PG calculations used in this thesis, each user has a defined xyz

position and it’s also specified if the user is located indoors or outdoors. For theindoor users, additional outdoor to indoor losses are added to the link loss defined

Chapter 2. Theory 7

in Table 2.1 as well as shadowing losses generated from log-normal distributions.These losses are together with the antenna gain determining the total link PG that isfurther used in the network simulations.

2.1.7 A Set of Site-Specific Path Gain Models

A set of site-specific path gain models that are much more computationally expen-sive than WINNER is also used in this work [1]. The total PG for each link is a sumof all components from the following models:

• Model for propagation above terrain and buildings

• Model for propagation around buildings

• Foliage model

• Outdoor-to-indoor model

• Additional stochastic model.

Model for propagation above terrain and buildings

In this set of site-specific models, the general PL model for propagation above terrainand buildings is:

PL[dB] = 20 log (4πd/λ) + D+ + D−, (2.13)

where d is the distance between transmitter and receiver and both D+ and D−

are knife-edge diffraction losses based on half-screen representations in the verti-cal plane of the building and terrain data. The D+ term is a sum of Fresnel-lossesF+(v+i ) for all half screens between the transmitting and receiving antennas. For theith half screen, the Fresnel-loss F+

i is calculated using the Fresnel-Kirchhoff diffrac-tion parameter:

v+i = 2√(s+i /λ), (2.14)

where s+i is the string distance that is defined as the difference between the sumof left and right convex hulls and the convex hull between transmitter and receiver.

The D− term is calculated as:

D− =3

∑i=1

F−(v−i ), (2.15)

where each loss term F−i (v−i ) comes from the dominating screen of all screensthat creates NLOS between transmitter and receiver. The dominating screen hasthe largest difference between the geometric distance and the distance transmitter-screen-receiver. The diffraction parameter v−i is defined as:

v−i = 2√(s−i /λ), (2.16)

Chapter 2. Theory 8

FIGURE 2.1: Illustration of the half screen model including threescreens for plus diffraction and the convex hull between transmitter

(left) and receiver (right).

where s−1 is the difference between the geometric distance and the transmitter-screen-receiver distance, s−2 is the difference between geometric distance and thetransmitter-screen distance, and s−3 is the difference between geometric distance andscreen-receiver distance. All distances in the D− term are calculated as if the dom-inating screen is isolated. Fig. 2.1 illustrates a path that includes three half screensfor plus diffraction and the convex hull between transmitter and receiver.

For a link, various types of paths are calculated; direct paths, backscatter pathsand specular reflection paths are all calculated separately using the half screen ap-proach.

Adding antenna gain from both transmitting and receiving antennas with respectto path angles gives the total "above" PG.

Maximum PG is selected for all calculated paths for a certain link.

Model for propagation around buildings

A 3D model that is combining the half-screen model with a recursive 2D model isused to trace paths and calculate PG around buildings. For cities with high-risebuildings that reach above antenna heights, diffraction around corners into streetcanyons is a very common phenomenon.

Foliage model

If a foliage height matrix is included in the map then outdoor foliage loss, such astree loss, can be added to the PG calculated for each path. However, foliage is not

Chapter 2. Theory 9

represented in the OSM maps used in this thesis. Additionally, neither oxygen lossnor rain loss is modeled.

Outdoor-to-indoor model

For indoor users, outer wall losses together with a loss per meter factor are added tothe outdoor losses.

Additional stochastic model

Stochastic shadowing and multipath components are also included to generate amore realistic propagation behavior.

2.2 SNR, SINR and Channel Capacity

Three important concepts regarding user throughput are introduced in this section.The signal to noise ratio (SNR) and signal to interference and noise ratio (SINR) areboth closely related to the maximum bitrate that the user actually can experience.This upper boundary bitrate (often measured in bits per second (bps)) is referred toas channel capacity.

2.2.1 SNR

Although noise is not included in the PG calculations, it plays a major part in thechannel capacity evaluation. If the received signal power (calculated by PG) for acertain link is Pr and the noise Power is Pn, then SNR is defined as

SNR =Pr

Pn. (2.17)

2.2.2 SINR

Since a mobile network often contains many active links, various types of signalinterference could decrease, along with noise, the channel capacity. Therefore, SINRis a good indicator of user throughput and is defined as

SINR =Pr

Pi + Pn, (2.18)

where Pi is the power of the total interference.

2.2.3 Channel Capacity

The channel capacity, is described as the maximum data rates that can be transmit-ted over a channel when the error probability approaches zero. The capacity limitsare upper bounds and can therefore not be reached. The evolution of turbo-codes,however, has made it possible for real systems to achieve data rates very close tocapacity. In particular, a single-user system with single antennas on both transmitterand receiver sides has a capacity limit that is described by the well known ShannonCapacity:

C = B log2(1 + SNR), (2.19)

Chapter 2. Theory 10

where B is the bandwidth for an AWGN-channel. Capacity, however, is in mostcases not explicitly defined.

2.3 Long Term Evolution (LTE)

This section covers fundamental principles of the system that is simulated in thisthesis, Long Term Evolution (LTE). The LTE-framework is described in order to un-derstand the setup outlined in the next chapter, and what capacity limits the LTE-systems can have.

2.3.1 LTE-framework

The core of the LTE DL transmission scheme is the Orthogonal Frequency DivisionMultiplexing (OFDM), which is a multicarrier modulation technique based on di-viding the total system bandwidth into many narrow-band subchannels that are or-thogonal to each other. The data rates on each subchannel are much lower than thetotal data rate and by choosing the subchannel bandwidth to be smaller than thecoherence bandwidth, each subchannel becomes (somewhat) flat fading which thusmitigates the effect of intersymbol interference (ISI). A drawback, however, withOFDM is that is leads to high peak-to-average power ratio (PAPR) and therefore asingle carrier OFDM technique (SC-OFDM) is used in UL.

The multiuser scheduling principle is called orthogonal frequency division mul-tiple access (OFDMA), since it’s based on OFDM. As shown in Fig. 2.2 OFDM-subcarriers are divided into blocks, known as resource blocks, each of bandwidths180kHz. Each resource block is scheduled for a specific user within one subframeduration of 1ms. The spacing between the orthogonal subcarriers is 15kHz, resultingin 12 subcarriers per resource block. LTE supports 6 different system bandwidths;1,4MHz, 3MHz, 5MHz, 10MHz, 15MHz and 20MHz, leading to a maximum num-ber of 100 user resource blocks for a bandwidth of 20MHz in each subframe. Withina subframe, either 12 or 14 OFDM-symbols is transmitted depending on the lengthof the cyclic prefix (described in 2.3.1), which is inserted into every OFDM symbolto eliminate ISI.

FIGURE 2.2: LTE DL transmission scheduling - OFDMA

Chapter 2. Theory 11

OFDM

OFDM is based on Inverse Fast Fourier Transform (IFFT) on the transmitter side andFast Fourier Transform (FFT) on the receiver side. The input bit-sequence is modu-lated into a stream of parallel complex symbols of some length N. IFFT is convertingeach symbol X[n] in frequency domain into a discrete sample x[n] in time domainby

x[n] =1√N

N−1

∑i=0

X[i]ej2πni/N . (2.20)

The full sequence x[0], ..., x[N − 1] is known as the OFDM symbol. After addingthe cyclic prefix to this OFDM symbol, the discrete time sequence is sent through aparallel to serial converter, D/A converter and then finally upconverted to carrierfrequncy f0. This signal is then transmitted into the channel. On the receiver’s side,FFT is used in order to recover the original data stream.

Cyclic Prefix

The cyclic prefix in each OFDM-symbol is a known sequence that is appended tothe beginning of the OFDM symbol in order to mitigate the ISI. If the length of thecyclic prefix is µ, then this sequence often consists of the last µ numbers in the orig-inal input sequence. The simulator used in this thesis assumes that the cyclic prefixcompletely eliminates the ISI.

TDD and FDD

All terminals in LTE are supporting two duplex schemes for UL and DL transmis-sions; frequency division duplex (FDD) and time division duplex (TDD). Fig. 2.3shows the structure in one radio frame (10 subframes) for FDD and TDD respec-tively. In FDD, UL and DL data is transmitted simultaneously on two different car-rier frequencies. In TDD, the UL and DL transmission is on the same carrier fre-quency and must therefore be separated in time. LTE supports 7 different TDD con-figurations. For TDD, guard periods (GP) are needed to ensure a non-overlappingswitch between UL and DL.

FIGURE 2.3: Radio frame allocation of UL and DL transmission inFDD and TDD

12

Chapter 3

Method

In this chapter the steps towards the simulations will be described. Firstly, importand treatment of OSM-data is presented along with an overview of the geometricalobjects that represent buildings in the simulator, polygons. Thereafter the simulationscenarios and various necessary network setup, including system parameters, aredescribed.

3.1 Map Generation

3.1.1 OSM import

Open Street Map (OSM) is an open source database founded in England that is sup-porting free editing of the world map. One of the main goals of this thesis is not onlyto successfully import maps from OSM into matlab, but also to treat and prepare thedata for error free simulations.

There are two Overpass API query languages, Overpass XML and Overpass QL,used for reading OSM data from a specific region of interest and subsequently exportthis data into a specific file format. A helpful tool for quick export and display of thereturned OSM data is Overpass turbo. In Overpass turbo there is a wizard that helpsthe user building Overpass queries by simply converting a single search term, suchas "building", into a script. More information and language guides for the two querylanguages can be found in [9].

The OSM data consists of three element types; nodes, ways and relations.

• A node element is a point representation with a latitude and a longitude coor-dinate.

• A way element is a list of two or more nodes that represents linear features orareas such as building polygons or roads.

• A relation element is a list of nodes, ways or other relations, that is defining arelationship between its including elements.

Each of these three data elements includes an unlimited number of tags that describethe specific map feature. A tag consists of a unique key and a value and could con-tain any information about the element such as height, material, color, street addressetc. For instance, a building is often represented as way including the tag with key"building" and value "yes", which in many cases is the only available informationabout a building along with the nodes defining its coordinates. However, in largercities with a variety of landmark buildings height information is often available andrepresented by either key "height" or "building:levels" for which the latter is pairedwith an integer value that defines the number of building floors.

Chapter 3. Method 13

The following Overpass XML script is used in Overpass turbo for all map im-ports in this thesis:

1 < !−−This has been generated by the overpass−turbo wizard .

3 Gets back bui ld ings−−>

5 <osm−s c r i p t output=" j son " timeout=" 25 ">< !−− gather r e s u l t s −−>

7 <union><query type="way">

9 <has−kv k=" bui lding "/><bbox−query { { bbox } } / >

11 </query><query type="way">

13 <has−kv k=" b u i l d i n g : p a r t "/><bbox−query { { bbox } } / >

15 </query><query type=" r e l a t i o n ">

17 <has−kv k=" bui lding "/><bbox−query { { bbox } } / >

19 </query></union>

21 < !−− p r i n t r e s u l t s −−>< p r i n t mode=" body "/>

23 <recurse type="down"/>< p r i n t mode=" ske le ton " order=" q u a d t i l e "/>

25 </osm−s c r i p t >

This script is querying for ways including either tag "building" or "building:part"and relations with tag "building" within the bounding box bbox that is manuallyselected with the web-gui. After running the query the returned data can be down-loaded into a kml-file that is further imported into matlab for map generation.

In matlab, the kml-file is opened with fopen which generates an integer, FID, thatis used as input to fread according to the following lines:

1 [ FID msg] = fopen ( kmlFile , ’ r t ’ ) ;t x t = fread ( FID , ’ u int8=>char ’ ) ’ ;

The return variable txt is a string that includes all information from the kml-file.In order to extract and sort useful data stored in txt, the matlab function regexp canreturn a substring that matches the input requirement. For instance, finding a heightvalue in a string variable objectString that includes all information about a specificbuilding object can be done by using the following line since the height value (if itexists) is somewhere between the strings ’height"’ and ’</value>’:

height = regexp ( o b j e c t S t r i n g , ’ height " >.+? </ value > ’ , ’ match ’ ) ;

If height information, building height or building levels, is not available in object-String, then a default height value will be used for this particular object. The defaultbuilding height is a decimal value set by the user before the map generation andpreferably reflects the average building height. For urban areas, such as Shibuya orChicago, it can be more difficult to find an appropriate height average.


3.1.2 Polygons

The imported and filtered building data is stored as vectors in a matlab struct thatmust be further corrected before the simulator can use this data as geometrical infor-mation in the PG calculations. The buildings will be defined as polygons that mustfollow the same set of rules as outlined in [8].

A polygon will be represented as a number of rings that each has an orientationdefining if the surface is on the outer or inner part of the ring. A ring is a sequence ofat least 4 vertices in which the first vertex equals the last and with a direction spec-ified as the order the vertices from the first to the last vertex. A clockwise directionsets the interior of the polygon inside of the ring and vice versa for a counter clock-wise orientation. Hence, the outer ring of a polygon must have a clockwise orderof vertices and that a ring inside another ring (defining, for instance, building courtyards) must have counter clockwise orientation to not overlap the interiors.

3.2 Network Setup

This section covers the network setup, including BS and user deployments for eachcity scenario and the system specific parameters. Four cities are simulated, Chicago,San José, London and Shibuya. The deployments are scenario specific and two LTE-systems, that only differ in carrier frequency, are simulated for each city.

3.2.1 Deployment

The simulation scenarios include two layers in the network, one UE layer and onemacro BS layer. In order to implement the set of site-specific propagation modelsoutlined in 2.1.7, both UE and BS positions must be represented by xyz-coordinateswithin the boundaries of the generated map. Additionally the deployment coordi-nates must be a subset of the mapbins, defined by the binsize. The binsize is thedistance in meters between the mapbins. For each of the following four maps, UEdeployment will be uniform within a chosen user polygon. The result area is definedas the same as the user polygon.

As urban areas could look very different around the world, it is of great interestto analyze network behavior in cities from various continents. In the analysis part,Chicago, London and Shibuya have been chosen to represent typical urban environ-ments in North America, Europe and Asia respectively. Also, deployment strategiesare different in these three continents which is accounted for here.

Since residential areas with buildings that rarely reach above 2 floors mostly looksimilar all over the planet, only one city, San José, is representing this type of cityarea.

Note that all deployments in this thesis are fictional, so the base station locationsare not derived from real deployments.

Chicago

The deployment of macro BS and users in Chicago is shown in Fig. 3.1. The macrosare placed in a hexagonal pattern with an inter-site distance (ISD) of 500m. A macrothat is given a xy-coordinate will be either placed on a pole that is 20m above groundlevel (AGL) or 2m above the roof top if a building exists on this bin. One macro inthe Chicago map is manually adjusted and moved to a better location (lower-rightBS in the 2D view).


The user area is 500×500m2 and users will be deployed on every xy-bin withinthis area, but only on every 4th floor in the z-direction. Hence, users will be sampledfor heights 1.5m, 17.5m, 33.5m, etc. inside buildings.

FIGURE 3.1: The Chicago map and its deployment. Base station loca-tions are shown in red. Horizontal directions of the sector antennasare also visualized. User positions are included in the right figure and

represented as blue points. The binsize in Chicago is 5m.

San José

In the residential area of San José, almost all buildings are of height 5m (partly dueto the default building height in the map generation). The macro sites are thereforeplaced on poles 20m above ground level as seen in Fig. 3.2. In American residen-tial areas, macros are mostly deployed with 2-3km ISD. However, ISD is chosen to1900m to fit the map of size 4×4km2. In order to deploy all sites on poles with height20m, two site positions are adjusted.

The users will be deployed on every xy-bin of the map and every 4th floor (userswill therefore only be located on the 1st floor) within the 4×4km2 large user polygon.

London

In London, and Europe in general, it is most common to place macro BS on roof topsin urban areas. ISD is chosen to be 300m and the hexagonal deployment resultedin that most BS are placed on roof tops (2m above building height) and the rest onpoles 20m AGL. Fig. 3.3 shows both site deployment and user deployment (resultarea) for this particular area in London.


FIGURE 3.2: Base station and user deployment in San José.

FIGURE 3.3: Base station and user deployment in London.


Shibuya

Shibuya is a well known part of Tokyo, and is here chosen to represent a dense urbanarea in Asia. In this region of the world, macro BS are deployed with a shorter ISDthan in both North America and Europe. Therefore, ISD is set as 200m as shown inFig. 3.4. Since no manual adjustments are made, two of the sites are placed on rooftops; one at height 41m and one at height 22m AGL. The other five macros are placedon poles 20m AGL. Users are located within a 200×200m2 area (users are marked byblue dots in Fig. 3.4 (right)) and sampled for every xy bin, and for every 4th floor inz-direction just as in the other scenarios.

FIGURE 3.4: Base station and user deployment in Shibuya.


TABLE 3.1: Parameters for two LTE systems with different carrier fre-quencies and bandwidth

System and Propagation Parameters

Bandwidth for fc = 700MHz 20MHzBandwidth for fc = 2GHz 20MHz

Common Parameters for Both Systems

Macro BS LayerDuplex Scheme FDDHighest Modulation 64QAMDL Maxpower 40WUL Noise Figure 2.3No. DL TX Antennas 2No. UL RX Antennas 2Electrical Down Tilt 0◦

Mechanical Down Tilt 9◦

UE LayerDuplex Scheme FDDHighest Modulation 64QAMUL Maxpower 0.2WNo. DL RX Antennas 2No. UL TX Antennas 1DL Noise Figure 9Noise Floor -204dBAntenna Model isotropic

3.2.2 System Parameters

Along with the BS and UE deployment setup, general system parameters must bedefined before simulations can run. In particular, each layer has its own param-eters for each system to be simulated. Since only two LTE systems with differentcarrier frequencies are simulated, each layer will define parameters for these sys-tems separately. However, most parameters are common for both systems. Table 3.1summarizes the most important parameters for both layers.

19

Chapter 4

Results

4.1 Path Gain

This section is presenting results of PG for all links in the systems specified in chap-ter 3. A chosen WINNER scenario (see 2.1.6) and the set of site-specific propaga-tion models (see 2.1.7) are applied to each system separately. The set of site-specificmodels will in this chapter be referred to as "the site-specific model" to simplify thereading.

The realistic scenarios for the city areas in Chicago, San José, London and Shibuyashowcase on differences between the models for various scenarios and environ-ments. PG results for each city are shown in 3D views, 2D views and cdfs that arebased on the PG distribution. The 3D views include all indoor users whereas the 2Dviews only include the 1st floor users.

4.1.1 Chicago

The PG results for Chicago are presented in Fig. 4.1, Fig. 4.2 and Fig. 4.3.

Indoor

WINNER PG at 700MHz is likely between -110dB and -130dB for the lower floorusers and for users deployed higher up in sky scrapers PG is likely as low as -140dB.The site-specific model is more dependent on the local environment and althoughPG is also likely between -110dB and -130dB at 700MHz, this model generates alarger spread in PG and is overall more optimistic.

Link PG is worsened at 2GHz, and the site-specific model is also at this carrierfrequency slightly more positive than WINNER, although both models predict thatindoor PG is likely between -120dB and -140dB.

Outdoor

Outdoor PG is, obviously, overall higher than indoor user PG. The site-specific modelis significantly more optimistic than WINNER for outdoor users. WINNER PG at700MHz is likely between -60dB and -80dB only for users with LOS links near theBS antennas. The site-specific model, however, is predicting that PG at 700MHz islikely between -60dB and -80dB for users that not necessarily are close to a BS butare located in street canyons. For the most faded links with lowest PG at 700MHz,the site-specific model in fact predicts PG to be as low as -150dB, whereas no link PGis lower than just below -120dB with WINNER.

The site-specific model is also at 2GHz significantly more optimistic than theWINNER model. PG for users located in street canyons is with the site-specific

Chapter 4. Results 20

model very likely between -70dB and -90dB, whereas the WINNER model predictsPG for these users to be more likely between -80dB and -100dB. Also at this fre-quency the site-specific model is more pessimistic for the most attenuated links, andsome links can according to the site-specific model have PG as low as -160dB. Lowestoutdoor WINNER PG is about -130dB.

FIGURE 4.1: 3D view of PG in Chicago.


FIGURE 4.2: 2D view of PG in Chicago.

FIGURE 4.3: CDF of PG in Chicago.


4.1.2 San José

PG results for San José are presented in Fig. 4.4 and Fig. 4.5. Only 1st floor indoorusers and outdoor users are included in the calculations, since the residential area ofSan José is homogeneous in low height (about 5m) and doesn’t have any high risebuildings. Rural macro scenario D2 (see Table 2.1) is here used in the WINNER PGcalculations.

Indoor

Due to the large result area and the small building buildings, indoor user results areonly understood from the cdfs in Fig. 4.5. The site-specific model is clearly more op-timistic and at 700MHz it has more than 10dB higher mean and smaller spread thanPG from the WINNER model. Indoor PG from the site-specific model at 700MHzis most likely between -90dB and -110dB, whereas WINNER PG is likely between -90dB and -130dB. At 2GHz the site-specific PG is mostly between -90dB and -120dB,whereas WINNER PG is more spread out and predicts that indoor PG is likely be-tween -90dB and -140dB.

Outdoor

Also for the outdoor user links the site-specific model is more optimistic than theWINNER model. PG at 700MHz is according to the site-specific model most likelybetween -70dB and -100dB. Again, the WINNER model predicts PG to be morespread out than the site-specific and is likely between -70dB and -110dB. Note thatthe WINNER model is more pessimistic for LOS links and PG falls off faster withdistance than the site-specific PG. Also NLOS links are modeled more pessimisticwith WINNER.

Predicted PG is overall about 10dB lower at 2GHz than 700MHz for the site-specific model. Hence, site-specific PG is mostly in the interval from -80dB and-110dB. WINNER PG is also lower at 2GHz than at 700MHz and is most likely be-tween -80dB and -120dB.


FIGURE 4.4: 2D view of PG in San José.

FIGURE 4.5: CDF of PG in San José.


4.1.3 London

Fig. 4.6, Fig. 4.7 and Fig. 4.8 show the PG results for London. Since the buildingheight in this area of central London is homogeneous, apart from a few taller build-ings, PG for mostly 1st floor and 4th floor indoor users are calculated and presentedalong with outdoor users.

Indoor

The WINNER model and the set of site-specific model are generating more similarindoor results for London than San José. The 2D views in Fig. 4.7 show that PG at700MHz for users located on the 1st floor is very likely between -90dB and -110dBfor both models. Fig. 4.8 shows, however, that the site-specific PG at 700MHz ishaving about 5dB higher mean and generates somewhat more optimistic results.The indoor links with the highest PG can be found for the 4th floor users, and thesecan experience PG up to -70dB.

At 2GHz, WINNER and the site-specific model have even more similar PG re-sults. 1st floor PG are likely between -100dB and -140dB for both models and 4thfloor users are likely having PG between -80dB and -100dB.

Outdoor

The site-specific model is generating more positive outdoor user PG results thanWINNER at 700MHz in London also. Just as in Chicago, the site-specific model ispredicting significantly higher PG for users located in street-canyons. If BS is neara street canyon, then PG is very likely between -60dB and -80dB in a large part ofthat street according to the site-specific model. PG generated with the WINNERmodel fall off more rapidly with distance from BS, and only LOS links for usersless than about 100m from BS are likely to have PG between -60dB and -80dB. Notethat although the site-specific model is overall more optimistic than WINNER foroutdoor users, it is also more pessimistic for the most attenuated links.

Outdoor PG is in general lower at 2GHz. Still, open areas and street canyons nearBS provide highest PG with the site-specific model, where PG can be between -80dB-90dB. Otherwise, outdoor PG is likely as low as -120dB. With WINNER, only LOSlinks nearest BS can achieve -80dB and -90dB, otherwise PG is more likely between-90dB and -110dB. Although the site-specific model is more optimistic at 2GHz forthe best 50th percentile users, WINNER is is more optimistic for the worst 50th per-centile users.


FIGURE 4.6: 3D view of PG in London.

FIGURE 4.7: 2D view of PG in London.


FIGURE 4.8: CDF of PG in London.


4.1.4 Shibuya

The PG results for the 400x400m area in Shibuya are shown in Fig. 4.9, Fig. 4.10 andFig. 4.11.

Indoor

The site-specific model is generating overall higher indoor user PG than the C2 WIN-NER scenario. The cdfs show that WINNER PG has mean of about -100dB andsite-specific PG has mean of about -90dB for 700MHz. For 2GHz, WINNER PG hasmean just over -110dB and site-specific PG has decreased its mean to about -100dB.The WINNER model also generates overall larger spread for both frequencies thanthe site-specific models.

Outdoor

Due to the short ISD, outdoor users in Shibuya are very likely to have high PG.WINNER is again more pessimistic than the site-specific model at both frequencies.Whereas WINNER has a mean of about -80dB at 700MHz, the mean of the site-specific PG is near -70dB. Users located in the open areas experience highest PG,and the cdfs show that PG at 700MHz can be over -60dB for the site-specific models.

At 2GHz, LOS links in the open areas are more likely to have PG between -70dBand -80dB for the site-specific models, and between -70dB and -90dB for WINNER.The means have now dropped to about -90dB for WINNER and just over -80dB forthe site-specific model.

FIGURE 4.9: 3D view of PG in Shibuya.


FIGURE 4.10: 2D view of PG in Shibuya.

FIGURE 4.11: CDF of PG in Shibuya.


4.2 Network Performance

The simulator is using all predetermined system parameters, together with PG, inorder to generate bitrates, SINR and interference for each link in both UL and DLdirection. It is important to stress that the traffic load in these simulations is nearzero. Hence, each user is allocated the full available spectrum (BW).

Since the network consists of two layers, performance is only evaluated for linksbetween the user layer and macro layer. Since BW is 20MHz for every scenario, 100RB is allocated to the users. In DL direction, the total BS power of 40W is dividedequally for each RB. Thus, power per resource block is 0.4W in DL.

The DL and UL performance evaluations follow similar procedure, and thus al-most the same algorithm is used. PG results from the site-specific model serves asinput to the simulator. The path with the strongest PG is determining the servingBS-UE link, but other paths with lower PG are also used in interference calculationsin DL. The received signal strength is together with all modeled interference andnoise determining the link SINR. Since the BS transmit power is much larger thanthe UE transmit power, the received signal power will be much larger in DL than inUL.

Besides carrier frequency, the choice of highest modulation is an important vari-able in how many bits per RB that can be received for each direction. As describedin Table 3.1, both DL and UL are using FDD and 64QAM as highest modulation,but the DL transmission has a higher average number of bits per RB than the ULdirection since MIMO is used in DL.

The final bitrate for each link is the number of assigned resource blocks (100 forDL and 96 for UL) multiplied with the bitrate per RB.

4.2.1 Chicago

Throughput and SINR results for Chicago are presented in Fig. 4.12, Fig. 4.13 andFig. 4.14. Both UL and DL results are presented at fc=700MHz and fc=2GHz respec-tively.

Indoor

The 3D views shown in Fig. 4.12 illustrate the indoor user bitrate results from thetwo simulations. Fig. 4.13 shows throughput for 1st floor users. As illustrated in thefigures, throughput can vary between everything from below minimum bitrates upto near peak rates inside of a single building.

In DL at 700MHz, 1st floor users located near the outer wall closest to the servingsector antenna are likely experiencing bitrates over 50mbps. On this floor, through-put rarely drops below 10mbps. For users located on top floors in the high risebuildings, bitrates are likely between 10mbps and 50mbps. In UL, 1st floor userslocated near outer walls can experience rates between 40mbps and 50mbps. How-ever, bitrates are also likely to drop below 10mbps for user positions on the side ofthe building furthest away from nearest BS. On top floors in high rise buildings, ULthroughput is almost always below 10mbps.

At 2GHz, areas where bitrates can exceed 50mbps are slightly smaller than at700MHz. The most significant differences compared to 700MHz can be found forthe worst percentile users. Much larger indoor areas have DL bitrates lower than30mbps for the 1st floor users, and almost all positions for top floor users in highrise buildings provide bitrates under 30mbps. In UL, the decrease in indoor user


throughput at 2GHz compared to 700MHz is perhaps even more prominent. Most1st floor users have UL bitrates under 10mbps and higher up in sky scrapers, usersare likely out of coverage.

The cdfs of SINR in UL are less smooth than those of DL. This is due to UL powercontrol for a minimum SINR of -3dB and target SINR of 10dB. Hence, it’s much morelikely for indoor links at 2GHz to have SINR below -3dB. Note that this is reflectedin much worse indoor throughput at 2GHz.

Outdoor

It it obvious that SINR and throughput in UL is much more affected by the change incarrier frequency than DL. In fact, DL performances are nearly identical for both car-rier frequencies. Keep in mind, though, that PG results are not identical for 700MHzand 2GHz.

In UL, LOS links in open areas and street canyons are likely experiencing ratesover 50mbps for both frequencies. For more attenuated links, however, rates aremuch lower at 2GHz. Rates can likely be below 30mbps at 2GHz, but not at 700MHz.Note that this is a consequence of that SINR falls below target SINR.

FIGURE 4.12: UL and DL performance for Chicago in 3D.


FIGURE 4.13: UL and DL performance for Chicago in 2D.

FIGURE 4.14: CDF of SINR and Throughput in Chicago.


4.2.2 San José

Fig. 4.15 and Fig. 4.16 show the SINR and throughput in UL and DL for San José.

Indoor

Indoor user results are presented in cdfs. DL SINR and throughput results are al-most identical for both carrier frequencies. In UL, however, SINR and subsequentlythroughput results are more pessimistic at 2GHz than 700MHz. The mean of ULSINR at 700MHz is about 13 and almost all links have SINR over target SINR. Sincemean of SINR at 2GHz is just above 10, many links have SINR under target SINR.As a consequence, throughput at 2GHz becomes much worse for those links.

Outdoor

Also for outdoor users DL results are close to identical for both frequencies. Out-door user SINR and throughput in UL direction, though, is clearly worse at 2GHz.Although SINR is overall lower at 2GHz, almost all links have SINR over 10. ULthroughput is therefore not as prominently decreased at 2GHz as in, for instance,Chicago. Spread is very low in SINR at both frequencies, and the throughput cdfsare therefore also having a low variances.

FIGURE 4.15: UL and DL performance for San José in 2D.


FIGURE 4.16: CDF of SINR and Throughput in San José.


4.2.3 London

Indoor and outdoor user performance results in London are presented in Fig. 4.17,Fig. 4.18 and Fig. 4.19.

Indoor

Best indoor DL throughput rates are provided to 4th floor users, and Fig. 4.17 showsthat bitrate results on this floor are similar for 700MHz and 2GHz. For 1st floor users,2GHz generates lower DL bitrates than 700MHz, as shown in Fig. 4.18. Althoughspread in throughput is large at both frequencies, the cdfs tell that bitrates at 2GHzis spread out between 0mbps and 100mbps whereas DL throughput at 700MHz ismostly between 20mbps and 100mbps.

In UL, both 4th and 1st floor users are experiencing worse performance at 2GHz.The cdfs show that SINR at 2GHz is more likely to be below target SINR, which againaffects throughput negatively. Throughput decrease is therefore most prominent forindoor user UL transmissions.

Outdoor

DL throughput for outdoor users are almost identical for both frequencies. In fact,some parts of London provide higher bitrates at 2GHz than at 700MHz. In UL,however, throughput is also in London worse at 2GHz. The cdfs show that thespread of SINR at 700MHz is between 10 and 20 for almost all users. 2GHz haslower SINR mean, resulting in that SINR are more likely to fall below target SINRand UL throughput is subsequently decreased significantly for the worst percentileusers.

FIGURE 4.17: UL and DL performance for London in 3D.


FIGURE 4.18: UL and DL performance for London in 2D.

FIGURE 4.19: CDF of SINR and Throughput in London.


4.2.4 Shibuya

The throughput rates for DL and UL transmissions in Shibuya are displayed inFig. 4.20 and Fig. 4.21. The cdfs of SINR and throughput are presented in Fig. 4.22.

Indoor

DL indoor user performance is nearly identical for 700MHz and 2GHz. Bitrates inthe DL direction are mostly between 20mbps and 100mbps as a result of that SINRis mostly between 5 and 25.

In UL, however, the change in carrier frequency affects SINR and subsequentlyalso throughput. Mean of SINR is decreasing from about 14 at 700MHz to about 12 at2GHz, and the number of users with SINR below target SINR is therefore increasing.Fig. 4.20 and Fig. 4.21 show that indoor links that experience rates between 30mbpsand 40mbps at 700MHz can have rates as low as 5mbps at 2GHz.

Outdoor

DL bitrates are also for outdoor users not much affected by the change in carrierfrequency. Some parts of the more open areas, in fact, have slightly higher rates at2GHz than at 700MHz. Note that the outdoor DL performance is much worse inShibuya than in any of the other cities, although PG is high. This is because CRSinterference is increased, since the ISD is shorter than in the other cities.

Shibuya, however, sees a significant increase in UL throughput compared to theother cities. Mean of UL SINR at 700MHz is about 18, which is even higher than themean of DL SINR for outdoor users. Although mean of UL SINR is lower at 2GHz,about 16, almost all users have SINR over 10. The distributions in SINR results inthat UL throughput at 700MHz is mostly from 50mbps up to the peak rates of about66mbps whereas bitrates at 2GHz mostly are from about 40mbps up to the UL peakrates.


FIGURE 4.20: UL and DL performance for Shibuya in 3D.

FIGURE 4.21: UL and DL performance for Shibuya in 2D.


FIGURE 4.22: CDF of SINR and Throughput in Shibuya.

39

Chapter 5

Conclusions

In this thesis it has been shown that OSM maps can successfully be imported andused in the matlab based simulator. Buildings are often not as detailed as in pro-fessional maps, but the data import can be further developed in order to use moreavailable information and by that make the scenarios more realistic.

The separate PG analyses of the four chosen cities are giving us guidelines aboutwhen the WINNER model and the site-specific model can provide quite differentresults. A general observation for both indoor and outdoor users is that the site-specific model is more optimistic for the top percentile users. Street canyons arecommon outdoor user positions in urban cities, and the site-specific model is muchmore optimistic than WINNER for such areas. Also, LOS link PG is likely to behigher for the site-specific model.

For low percentile users, the site-specific model can even be more pessimisticthan WINNER. For a very nonuniform dense urban city like Chicago, the spreadof PG is larger for the site-specific model. On the other hand, the spread of PG issmaller for the site-specific model in both rural cities with very uniform buildingheights and in urban city areas with large open areas such as this part of Shibuya.

The capacity analyses tell us that DL and UL transmissions are responding dif-ferently on the change in carrier frequency. UL bitrates are in general much morenegatively affected by a change from 700MHz to 2GHz than DL, since UL transmis-sions are more affected by the larger pathlosses. Largest differences in SINR andcapacity in UL between 2GHz and 700MHz are for indoor users in Chicago, sincea majority of the users have SINR around or below minimum SINR at 2GHz. Forthe other cities UL SINR for indoor users is in general higher, but since SINR is morelikely to be below target SINR at 2GHz than 700MHz, also the indoor UL throughputis becoming much worse at 2GHz for all four cities. Although UL SINR for outdoorusers is worse at 2GHz than 700MHz, the number of users that fall below targetSINR is low, even for Chicago and London, and therefore the throughput becomesonly slightly worse.

DL throughput is in general much less affected by the change in carrier frequencythan UL throughput. The most significant difference in DL bitrates between 700MHzand 2GHz is for indoor users in Chicago. A lower mean and larger spread of SINR at2GHz leads to much worse throughput at this frequency. DL bitrates are also slightlyworse at 2GHz than at 700MHz for indoor users in London. However, indoor usersin San José and Shibuya are experiencing almost identical bitrates at both frequen-cies. Outdoor users in all four cities, however, are not experiencing any differencein neither SINR nor throughput when carrier frequency is changed from 700MHz to2GHz.

The higher inter-cell interference in Shibuya, due to the short ISD, is worseningSINR in the DL direction. Although PG is higher in Shibuya than in the other cities,outdoor users here are experiencing the worst DL throughput. Another observation

Chapter 5. Conclusions 40

is that this is the only city in which indoor user performance exceeds outdoor userperformance.

41

Bibliography

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[2] Pekka Kysti and et al. IST-4-027756 Winner II. In WINNER II Channel Models,2007.

[3] A. Goldsmith, Wireless Communications. Cambridge University Press, 2005.

[4] Recommendation ITU-R P.526-13 Propagation by diffraction (11/2013).

[5] D. Astély, E. Dahlman, A. Furuskär, Y. Jading, M. Lindström, and S. Parkvall,Ericsson Research, “LTE: The Evolution of Mobile Broadband”, IEEE Communi-cations Magazine, April 2009.

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[7] URL: https://ieeexplore.ieee.org/document/6782076/authors

[8] URL: https://www.esri.com/library/whitepapers/pdfs/shapefile.pdf

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http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7152831

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