DEGREE PROJECT, IN , SECOND LEVELCOMMUNICATION SYSTEMSSTOCKHOLM, SWEDEN 2014

Propagation Modeling andPerformance Evaluation in an AtriumBuilding

YAO LU

KTH ROYAL INSTITUTE OF TECHNOLOGY

INFORMATION AND COMMUNICATION TECHNOLOGY

Abstract

In this thesis electromagnetic wave propagation is investigated in an indoor en-vironment. The indoor environment is a furnished office building with corridors,corners and rooms. Particularly, there is an atrium through the building in thecenter. For the study there were measurements available from real building inthe 2.1 GHz frequency band. One objective is to design a propagation modelthat should be simple but reflect the trend of the propagation measurements.Furthermore, a system performance evaluation is carried out based on the im-plemented model.

The proposed 3D model is a combination of the Free Space Path Loss model,the Keenan-Motley model and the recursive diffraction model. The channelpredictions from the 2D Keenan-Motley algorithm are quite different from themeasurements. Therefore, the 3D Keenan-Motley algorithm is designed to de-pict the atrium effect and speed up the simulation at the same time. Besidesa butterfly radiation diagram is created to mimic Kathrein 80010709 antennainstalled in the building. Finally, a diffracted path is added to improve the re-ceived signal strength for the users around the atrium areas. With all the aboveprocedures, the final results from the model are in good quantitative agreementwith the measurement data.

With the implemented propagation model, a further analysis of the systemperformance on the Distributed Antenna System (DAS) is performed. A com-parison for the system capacity between the closed building and the atriumbuilding is conducted, showing that the former one benefits more when thenumber of the cells increases. The reason is the atrium cells suffer severe in-terference from neighbor cells during high traffic demand scenarios. Then somefurther cell configurations show that the number of the cells, the geometry per-formance and the balance of the user fraction should be considered to improvethe system capacity.

Index terms: Indoor propagation, 3D path loss modeling, atrium building,the Keenan-Motley model, the recursive diffraction model, cell splitting, DAS

Acknowledgment

I would like to express my gratitude to Claes Tidestav for believing me andgiving me this opportunity to do the thesis at Ericsson Research. I gratefullyacknowledge Sara Landstrom for her encouragement, guidance and support. Myspecial thanks go to my supervisor Johan Soder for his unselfish and unfailinghelp, patience and guidance from the initial to the final stage. I am indebtedto Ben Slimane, my examiner from KTH, for his comments and follow up ofthe thesis. Furthermore, I am grateful to all the colleagues at Ericsson, whohave supported and encouraged me. Finally, I would like to thank my familyfor their love and understanding in all aspects of my life, and I can never affordto thank them enough.

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 System Models 32.1 Indoor Path Loss Models . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Free Space Path Loss Model . . . . . . . . . . . . . . . . . 32.1.2 Power Law Models . . . . . . . . . . . . . . . . . . . . . . 42.1.3 Keenan-Motley Model . . . . . . . . . . . . . . . . . . . . 42.1.4 Multi-floored Path Loss Model . . . . . . . . . . . . . . . 52.1.5 The Recursive Diffraction Model . . . . . . . . . . . . . . 6

2.2 Antenna System . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.1 Fitted Pico Omni Antenna . . . . . . . . . . . . . . . . . 72.2.2 Kathrein Antenna . . . . . . . . . . . . . . . . . . . . . . 82.2.3 Distributed Antenna System . . . . . . . . . . . . . . . . 9

2.3 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.1 Shape Identification . . . . . . . . . . . . . . . . . . . . . 10

2.4 Simulator Overview . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Measurements and Propagation Model Implementation 133.1 Indoor Environment, Measurements and Simulation Setup . . . . 133.2 New atrium building setup . . . . . . . . . . . . . . . . . . . . . 153.3 Atrium Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.4 Antenna Diagram Modification . . . . . . . . . . . . . . . . . . . 203.5 The Recursive Diffraction Model . . . . . . . . . . . . . . . . . . 21

4 Algorithm Optimization 264.1 Drawbacks of the 2D Keenan-Motley algorithm . . . . . . . . . . 264.2 3D Keenan-Motley algorithm . . . . . . . . . . . . . . . . . . . . 27

5 System performance analysis 295.1 Cell Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.2 System Evaluation of the Closed Building and the Atrium Building 305.3 Cell Configuration in the Atrium Building . . . . . . . . . . . . . 32

5.3.1 Case Study 1 . . . . . . . . . . . . . . . . . . . . . . . . . 325.3.2 Case Study 2 and Case Study 3 . . . . . . . . . . . . . . . 34

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6 Conclusion and Future Work 376.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Appendix A 41A.1 Measurements Distribution . . . . . . . . . . . . . . . . . . . . . 41A.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . 41

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List of Figures

2.1 2D Keenan-Motley Model. . . . . . . . . . . . . . . . . . . . . . . 52.2 Street Diffraction. . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Recursive Diffraction Model with Three Sections . . . . . . . . . 72.4 Radiation Diagram of Fitted Pico Omni Antenna . . . . . . . . . 82.5 Radiation Diagram of Kathrein 80010709 Antenna . . . . . . . . 82.6 The Variation of ρr for Different Shapes . . . . . . . . . . . . . . 112.7 The Flow Diagram of the Simulator . . . . . . . . . . . . . . . . 12

3.1 Simplified Floor Plan . . . . . . . . . . . . . . . . . . . . . . . . . 143.2 Measurement Route and Simulation Range . . . . . . . . . . . . 153.3 Floor Plan Conversion . . . . . . . . . . . . . . . . . . . . . . . . 163.4 RSCP Distribution from the Atrium Building . . . . . . . . . . . 173.5 RSCP Simulation Results from Two Algorithms for Cell Two . . 183.6 Atrium Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.7 RSCP Comparison with 3D Keenan-Motley Algorithm . . . . . . 193.8 An Approximation of the Kathrein 80010709 . . . . . . . . . . . 203.9 RSCP Comparison with Modified Antenna Diagram . . . . . . . 213.10 Geometry Distribution Comparison with Modified Antenna Dia-

gram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.11 Diffraction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.12 Diffraction Path Influence . . . . . . . . . . . . . . . . . . . . . . 233.13 RSCP Comparison with Recursive Diffraction Model . . . . . . . 243.14 Geometry Distribution Comparison with Recursive Diffraction

Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.1 3D Keenan-Motley Algorithm. . . . . . . . . . . . . . . . . . . . 27

5.1 Cell Splitting Configurations . . . . . . . . . . . . . . . . . . . . 305.2 System Capacity for Different Configurations . . . . . . . . . . . 315.3 System Performance of the Closed and Atrium Buildings with

Conf 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.4 Cell Configuration Pair 1 . . . . . . . . . . . . . . . . . . . . . . 325.5 Cell Capacity for Configuration Pair 1 . . . . . . . . . . . . . . . 335.6 Cell Utilization and DL Geometry for Configuration Pair 1 . . . 335.7 Cell Configuration Pair 2 . . . . . . . . . . . . . . . . . . . . . . 345.8 System Performance of Cell Configuration Pair 2 . . . . . . . . . 355.10 System Performance of Cell Configuration Pair 3 . . . . . . . . . 36

A.1 Measurement Distribution from the Atrium Building . . . . . . . 41

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A.2 Simulation Results for Cell One with the 3D Keenan-Motley Al-gorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

A.3 Simulation Results for Cell Two with the 3D Keenan-Motley Al-gorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

A.4 Simulation Results for Geometry with the 3D Keenan-MotleyAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A.5 Simulation Results for Cell One with the Modified Antenna Dia-gram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A.6 Simulation Results for Cell Two with the Modified Antenna Di-agram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

A.7 Simulation Results for Geometry with the Modified Antenna Di-agram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

A.8 Simulation Results for Cell One with the Diffraction Model . . . 45A.9 Simulation Results for Cell Two with the Diffraction Model . . . 45A.10 Simulation Results for Geometry with the Diffraction Model . . . 46

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List of Tables

3.1 Attenuation of Floor and Different Types of Walls . . . . . . . . 15

4.1 Time Consumption for the 2D Keenan-Motley Algorithm, Lim-ited Number of Antennas . . . . . . . . . . . . . . . . . . . . . . 26

4.2 Time Consumption for the 3D Keenan-Motley Algorithm, Lim-ited Number of Antennas . . . . . . . . . . . . . . . . . . . . . . 28

4.3 Time Consumption for the 3D Keenan-Motley Algorithm . . . . 28

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Chapter 1

Introduction

1.1 Background

The past ten years witness the rapid growth of the wireless service. This trendwill continue according to some statistic analyzes. The mobile data traffic isexpected to increase 11 times, as well as the traffic per subscriber will increasemore than 7-fold by 2018 [1]. Although close to 80% of the traffic occurs indoor[2], the performance of the indoor system has been less explored than that ofoutdoor. Thus, understanding how electromagnetic waves propagate in indoorenvironments becomes increasingly important.

Indoor environment modeling is complicated due to several reasons. On onehand, various floor plans, different construction materials and great variabilityin the buildings contribute to the complexity of the propagation modeling. Thefocused building is of atrium type that is a very popular architecture designtoday. An atrium is a large open space in the building, giving a feeling of spaceand light [3]. However, the propagation in the atrium building is different sincethe path loss differs when the signal passes through the atrium instead of thefloor. Besides, different types of materials of the walls, floors, windows and ceil-ings affect the attenuation. Furthermore, the open and closed status of doorsas well as the movement of people adds the uncertainty to the transmission.On the other hand, propagation mechanisms such as diffraction, reflection andscattering occur since the wavelength is short compared to many objects in thebuildings. The combined effects of these mechanisms cause multipath, whichmakes the propagation modeling more complicated.

The goals of this thesis work are:

• To evaluate and extend the empirical path loss model (the 2D Keenan-Motley algorithm)

• To calibrate the new path loss model according to the measurements.

• To compare the system performance between the closed building and theatrium building.

• To configure a Distributed Antenna System (DAS) and summarize thelimits to the system capacity.

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1.2 Contribution

After fulfilling the goals, the contribution of this thesis is:

• Functionally import the geographic information from the floor plan to thesimulator. Design and implement a new propagation model in the atriumbuilding. Additionally, the 3D Keenan-Motley algorithm is also beneficialto reduce the computation complexity and speed up the simulation.

• Introduce the cell splitting to the system. Compare the system perfor-mance between the closed building and the atrium building when thenumber of cells increases. Further cell splitting in atrium building is ana-lyzed to summarize the essential metrics for cell reconfiguration.

1.3 Outline

This thesis report starts with a background introduction and motivation. Chap-ter 2 gives an introduction to the relevant empirical models. In Chapter 3, astep-by-step approach to the new propagation model is explained. Besides,the characteristics of the indoor environment and simulation setup are also de-scribed. Chapter 4 performs the simulation efficiency by bringing in the 3DKeenan-Motley algorithm. Based on the implemented model, a further systemperformance analysis is conducted in Chapter 5. Finally, conclusion and futurework are given at the end of the report.

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Chapter 2

System Models

This chapter covers a theoretical introduction to the relevant propagation mod-els, antenna diagrams and the principles to capture the floor plan information,as well as a description of the simulator.

2.1 Indoor Path Loss Models

In telecommunication systems, the path loss describes the attenuation of a sig-nal wave when propagating through the space. The path loss in wireless radiochannels has higher uncertainty compared to that in wired channels because thetransmission could encounter obstacles, which causes penetration loss, diffrac-tion, reflection and scattering. In this section, some relevant models are brieflydescribed.

2.1.1 Free Space Path Loss Model

Free space propagation is the ideal situation. In this model, all obstacles thatmay affect the field are disregarded, where there is only one clear line-of-sight(LoS) wave component from the transmitter to the receiver. H. T. Friis pre-sented the received signal strength in linear as [4]

Pr(d) =PtGt(θt, φt)Gr(θr, φr)λ

2

(4πd)2(2.1)

where d is the transmitter-receiver distance separation, Pt is the total emittedpower, Gt(θt, φt) and Gr(θr, φr) are the transmitter power gain at the elevationangle θt and the azimuth angle φt and the receiver gain at the elevation angleθr and the azimuth angle φr, respectively. λ is the wavelength of the electro-magnetic wave.

The Friis equation applies only to the far-field antenna, i.e., when the antennas

are separated by d > 2l2

λ , where l is the largest dimension of the antennas.

According to the definition, the Free Space Path Loss (FSPL) between theisotropic transmitter and receiver at a distance d is expressed as Equation 2.2 ,

LFS(d) = 20 log10(4πd

λ) = 20 log10(

4πdf

c) (2.2)

3

where f is the signal frequency, and c is the speed of light in vacuum (c =2.9979× 108(m/s)).

2.1.2 Power Law Models

The power law models are the empirical models to describe the logarithmic re-lationship between the transmitted power and the received power. Particularly,FSPL is a particular case of the power law models when n = 2.

LPL(d) = LFS(d0) + 10n log10(d

d0) (2.3)

where the first component LFS(d0) is the FSPL at a reference distance d0. Theparameter n is the path loss exponent, which depends on the characteristics ofthe environment.

The power law models are simple and efficient models for the indoor path lossprediction. Typically the range of n is between 1 and 2 to considering the reflec-tion, diffraction mechanisms and the characteristics of the indoor environments.The reference distance d0 is usually around 1-5 meters in typical environments[5]. However, simply attuning one parameter n is not enough to describe thereceived signal power for all the users at the same distance d, e.g., some signalssuffer additional penetration loss due to the floors or the walls. Therefore, moredetailed models should be introduced.

2.1.3 Keenan-Motley Model

The Keenan-Motley model is a common model for indoor environments. Thismodel estimates the path loss considering the attenuation from walls and floors.Specifically, the loss starts with FSPL in Equation 2.2, which is a LoS losscomponent, together with the losses due to walls and floors for non-line-of-sight(NLoS) conditions.

LKM (d) = LFS(d) + nWLW + nFLF (2.4)

where nW and nF are the numbers of the walls and the floors passed by thestraight ray between the transmitter and the receiver, besides LW and LF arethe corresponding attenuation factors per wall and floor.

One property of the Keenan-Motley model is that LW and LF are identicalin the same building. However, measurements from some multistory buildingsindicate that the LW and LF are not linearly depending on nF [6]. An exten-sion for the non-linear LW and LF is introduced in Section 2.1.4. The empiricalmethod to count nW and nF is listed as follows.

2D Keenan-Motley Algorithm The Two-Dimensional(2D) Keenan-Motleyalgorithm is widely used for computing the number of walls and floors crossedby the signal. This algorithm is usually applied to the closed floor, i.e., buildingswithout atrium design. It tries to decompose 3D propagation modeling into two2D paths. As shown in Figure 2.1, the closed building has six floors. Tx is thetransmitter on the ceiling of the fourth floor while Rx is the receiver on the

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first floor. Ft and Fr are the floor indices where the transmitter and receiverare placed, respectively. In Figure 2.1, Ft = 4 and Fr = 1. Pn (n is the floorindex) are the vertical projections of Tx on the floors between Tx and Rx. Inare the intersections of the straight ray TxRx and the floors. The algorithm todetermine if the signal passes through a certain wall CD on floor N is describedin the following steps.

Figure 2.1: 2D Keenan-Motley Model.

• The number of floors that the signal passes through is obtained, i.e., nF =|Ft − Fr|.

• Calculate the projections Pn, intersections In and the azimuth angle θbetween Tx and Rx.

• For a specific floorN , compute the horizontal distance PNIN and PN+1IN+1

as well as the distance L between wall CD and PN in the direction angleθ. The signal passes through wall CD on the floor if both PNIN≥L andPN+1IN+1 < L are fulfilled.

The algorithm has some limitations. First, it can only be applied to the buildingswith the same floor plan for each floor. Second, it lacks computational efficiency.It is a point-to-point simulation with trigonometric calculation, consuming agreat deal of time when a large number of users are examined.

2.1.4 Multi-floored Path Loss Model

In reality, when predicting path loss in a multi-floored building, the multipatheffect should be taken into consideration. Paper [6] demonstrates two reasonsto focus on the multipath effect. The first reason is that when the building issurrounded by other buildings, the reflection from the neighbor buildings maycontribute to the received signal strength. Secondly, even if the examined build-ing is alone, the signal diffracts out of the windows, propagates along the outerwalls and then diffracts into other floors of the building through the windows,which finally increases the received signal strength. Considering these two ef-fects, a non-linear extension for the floor loss and wall loss is proposed:

LEL(d) = LFS(d) + nWLW × 10−nF

2

3 + nFL(0.23+ 1

1+nF)

F (2.5)

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2.1.5 The Recursive Diffraction Model

The recursive diffraction model is designed in [7] to predict the path loss aroundcorners in the street microcell environments and it is adaptive to different streetenvironments by tuning parameter. When examining the propagation in thestreet, the diffraction path dominates. Figure 2.2 illustrates the propagationphenomenon. Here the transmitter Tx, the receiver Rx and the corners of thebuilding are modeled as nodes. The physical connection between every two con-secutive nodes is called one section. LoS propagation is assumed within sections.

Figure 2.2: Street Diffraction.

In Figure 2.2, the solid lines indicate the signal propagation between Tx andRx. Tx and Rx together with one diffraction node divide the propagation intotwo sections. The propagation loss from Tx to Rx depends on the diffractionangle θ shown in Figure 2.2.

Figure 2.3 gives a detailed illustration of the model. The white dots are Txand Rx, respectively, and the black dots represent the street corners where thesignal diffracts. The corresponding diffraction angles are θ0 and θ1. j is thenumber of sections from Tx to Rx. The solid lines sj are the physical distanceof each section. qj is the parameter which relates the path loss to the diffrac-tion angle at the street crossing. The path loss between isotropic antennas isdescribed as follows.

Ldiff = 20 log10(4πdnλ

) (2.6)

where n is the total number of sections from Tx to Rx. Therefore n = jmax. Forexample, n = 3 in Figure 2.3. dn is the virtual LoS distance equivalent to theactual NLoS distance in terms of path loss. The way to compute dn follows arecursive method described in Equation 2.7 , where kj is a temporary variablefor calculation with the initial value k0 = 1 and d0 equals 0.

dj = dj−1 + kj ·sj−1kj = kj−1 + dj−1·qj−1

(2.7)

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Figure 2.3: Recursive Diffraction Model with Three Sections

The way that the diffraction angle influences the path loss depends on qj .

qj = q90×(θj90

)v (2.8)

where θj is the physical diffraction angle, parameter v is an attenuation shapedependent component and q90 is a constant used to adjust the recursive diffrac-tion model. The parameters v and q90 are chosen based on the street width.

The recursive diffraction model is introduced to model the propagation in bentstreets with linear segments. The adaptation to the indoor atrium environmentis presented in Section 3.5.

2.2 Antenna System

In radio networks, an antenna is always connected to a radio transmitter orradio receiver. It is an electric device that transforms the wired-bound energyinto wireless energy or vice versa. The antenna radiation diagram describes therelative field strength of the emitted radio waves in terms of angle. Typically,the antenna diagram is represented by a 3D graph. For simplicity, the 3Dantenna diagram is replaced by the polar plots of the horizontal and verticalcross sections under assumptions. In this section, two types of antennas arepresented together with a short description of the Distributed Antenna System.

2.2.1 Fitted Pico Omni Antenna

A plot of the radiation diagram of the Fitted Pico Omni antenna [8] is displayedin Figure 2.4. This antenna is designed to mimic a half-lambda dipole antennain the simulator. Figure 2.4a shows the horizontal radiation pattern of theantenna. As can clearly be seen, the antenna has an omni constant gain withthe value 2 dB as the angel ϕ varies, and no distinction in the radiation patternis discernible. Figure 2.4b illustrates that radiation power in the vertical planeis symmetric. It is a bi-directional radiation pattern with the intensity power

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going into two directions, θ = 71◦ and θ = 292◦. The nulls occur at θ = 0◦ and180◦. θ = 0◦ is the angle pointing vertically downwards when the antenna ismounted on the ceiling.

0.5 1

1.5 2

30

210

60

240

90 270

120

300

150

330

180

0

Amplitude[dB]

φ [deg]

(a) Horizontal-Plane

−15 −10

−5 0

30

210

60

240

90 270

120

300

150

330

180

0

θ [deg]

(b) Vertical-Plane

Figure 2.4: Radiation Diagram of Fitted Pico Omni Antenna

2.2.2 Kathrein Antenna

The Kathrein 80010709 [9] is an indoor multi-band antenna, which has an omni-directional gain horizontally. The polarization differs from different operatingband. It is a compact design and easily installed. Usually it is attached to theceiling or other mounting surfaces in the indoor environment. It is the antennasthat are used in the examined atrium building.

(a) Horizontal-Plane (b) Vertical-Plane

Figure 2.5: Radiation Diagram of Kathrein 80010709 Antenna

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The antennas are working at 2.1 GHz in the examined building. The corre-sponding radiation diagram is shown in Figure 2.5. In this frequency band,Kathrein 80010709 is vertically polarized. The gain for the horizontal planeachieves its maximum value when ϕ = 322◦ and drops to the bottom when ϕ =180◦. In general, the gain varies little. For simplicity, the gain of the horizontalplane is treated as an omni value, with a constant of 0 dB. Figure 2.5b showsthat the vertical radiation is in the shape of a butterfly. It has the maximumgain when θ = 50.5◦ and the nulls occur at θ = 0◦ and 180◦. The data sheetshows that the Half Power Beam Width (HPBW) is 50.25◦.

2.2.3 Distributed Antenna System

The distributed antenna system (DAS) is an excellent choice for densely popu-lated indoor zones such as office buildings and media centers. It gives a scalablenetwork solution to coverage and capacity challenges. In the head-end Equip-ment Room of the DAS, the signal is digitalized by the optical conversion unitand then transported by hybriflex cable to the remote access units. The remoteaccess units are a network of spatially separated antenna nodes that share acommon transport medium. These relatively small antennas serve as repeaters.The total power of the transport media is split into the antenna nodes. Thebenefits of the indoor nodes are that less power is wasted in overcoming pene-tration and shadowing losses. The nodes can also convert the signal back to theanalog signal and amplify the signal.

The transmitter system in the examined atrium building is a passive DAS. It isdimensioned for voice coverage rather than data capacity. Coax cable is used todistribute the signal to each floor of the building. The passive DAS consists ofsome passive components such as couplers and power splitters. It is capable tosupport multiple carrier frequency. It is less complex than typical active DASnetworks. However, the signal quality degrades according to the distance fromthe source.

2.3 Image Processing

Floor plans play an essential role in indoor wireless studies. In architecture en-gineering, a floor plan is an image showing the geographical placement of rooms,corridors, walls and other physical features. The number of the walls and thefloors between the transmitter and the receiver greatly impact the received signalstrength. To estimate the received signal strength in an indoor environment, theinformation from the floor plan should be converted as an input to the simulator.

Rectangular and circular wall configurations account for a large proportion ofhouse floor plans worldwide. The procedure to characterize the rectangularwalls and circular walls is different. Representing rectangular walls is simpleand can be done by identifying their start points and end points. However,the procedure for circular walls is a little more complicated. Usually polygonalapproximation is chosen to describe a circle. Therefore, floor plan conversionshould start with shape identity; that is to distinguish between rectangular wallsand circular walls.

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2.3.1 Shape Identification

The shape identification method used in the thesis bases on the central moment.According to Ming-Kuei Hu’s Visual Pattern Recognition by Moment Invariants[10], the variation of ρ, which is the distance from the examined point to thecenter of gravity, differs for different shapes. Here the ρ variation rate (ρr) isdefined as the ratio between ρ and ρmax, where ρmax is the maximum valueamong all ρ. Obviously, ρr is a set of vector ranging from 0 to 1.

ρr =ρ

ρmax(2.9)

As shown in Figure 2.6a, ρr is always 1 when examining a circle, since OA= OB. In a rectangular, ρmax is the length of OB shown in Figure 2.6b. ρrvaries significantly, and the minimum values of ρ are less than 0.5. For allthe rectangles, the maximum value of the minimum ρr appears for squares asshown in Figure 2.6c. The corresponding value is ρr = 1√

2≈ 0.7071. Thus,

by evaluating if the minimum ρr is above a threshold, e.g., 0.6, it is possible todistinguish the rectangle from the circle.

2.4 Simulator Overview

Generally speaking, radio network simulations is divided into two categories,time dynamic methodology and time static methodology. The former one canmodel traffic variation over time like users entering and leaving the system orretransmission. However, the latter one is attractive from a simulation execu-tion time perspective. Since we only focus on network metrics like the receivedsignal strength and cell capacity, time static methodology is selected for theMATLAB radio network simulator.

The simulator provides an efficient simulation environment for multi-standardssuch as LTE, WiFi and HSPA. To start with, the studied environment and thenetwork deployment are generated, including the map information, the channelconditions and the antenna locations as well as a whole new set of user data.Then the system applies the propagation modeling to obtain the received sig-nal strength of all the users, based on which cell or server is selected. Trafficis allocated to each user and the initial cell utilization (ρc1) is assumed. Af-ter the traffic is assigned, SINR of each user is calculated. SINR calculation isbased on the received signal strengths and the utilization of the interfering cells.Moreover, the user achievable data rate is calculated from SINR. The systemschedules the users based on their data rate and traffic demand to obtain theuser utilization (ρu). When all the ρu are calculated, the new cell utilization(ρc2) is the summation of ρu within the cell. The system keeps iterative pro-cessing until the ρc2 converges to ρc1. After accomplishing the above steps, thesimulation results are obtained.

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(a) Circle

(b) Rectangle

(c) Square

Figure 2.6: The Variation of ρr for Different Shapes

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Figure 2.7: The Flow Diagram of the Simulator

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Chapter 3

Measurements andPropagation ModelImplementation

This chapter starts with a description of the indoor environment where themeasurements were taken as well as the simulator setup. Then it continueswith a detailed introduction of the new propagation model designed in thethesis. The new model is derived and calibrated to have a better match tothe measurement data. Together, a comparison between the measurements andsimulation results is presented.

3.1 Indoor Environment, Measurements and Sim-ulation Setup

Measurements provide a better way to understand the wireless channel. Themeasurements were conducted in an eight-floor office building in Kista, Sweden.Each floor is 55×55m2 and 3m high. It is a fully furnished building with an openoffice design. The Floor Three to the Floor Seven have an identical floor planwhile the Floor One, the Floor Two and the Floor Eight differ. Thus, attentionis paid to the measurements taken from the Floor Three to the Floor Seven.Figure 3.1 is a simplified floor plan for the Floor Three to the Floor Seven re-gardless of the stairwell, doors and the furniture inside rooms. The solid blacklines represent the walls except that the semi-circle in the center characterizesthe atrium. The atrium is shaft from the entrance floor all the way up to theeighth floor.

Currently, a passive DAS WCDMA system is installed in the building. TheFloor Three to the Floor Seven have the same antenna installation. Each floorhas 13 antennas. Three of the 13 antennas are around the atrium and the restof them are uniformly placed around the corridor. The position of the antennasis known and saved as input to the simulator. The antennas are the Kathrein80010677 described in Section 2.2.2. The frequency of the transmitted signal iscentered at 2.1 GHz with a bandwidth of 5 MHz. The power for the DAS is 43

13

Figure 3.1: Simplified Floor Plan

dBm. The total pilot power accounts for 10% of the total power, which is 33dBm. Since the transmission attenuation depends on the length of the coax ca-ble, the pilot power varies from floor to floor and from the antenna to antenna,which adds difficulty to the simulation. The mean pilot power of antenna onthe Floor Four and the Floor Five is around 5.5 dBm, which is around 3 dB to5 dB lower than that of the other floors. When considering the pilot power onthe same floor, the highest pilot power belongs to the antennas near the atrium.The offset of the values within one floor is around 3 dB. There are two cells inthe building: the first three floors constitute the first cell and the rest of thefloors belong to the second cell. In this thesis, the target floor is defined as thefloors that the transmitters desire to serve, i.e., the target floors for Cell Oneare the first three floors while the target floors for Cell Two are from the FloorFour to the Floor Eight. Thus, the higher floors, which are the Floor Four andabove, are the non-target floors for the Cell One. The non-target floors for theCell Two are the first three floors.

The measurements were taken along the walk route on each floor, which isthe dashed line in Figure 3.2a. The average received power is saved togetherwith the positions. During testing, measurements are based on speech call only,no data connection. The user equipment (UE) is Sony Ericsson W995. The UEalways chooses the strongest received signal as the pilot signal, and the powerof resolvable signal should be within 25 dB offset from that of the pilot power.

Parameters settings of the simulator are configured according to the indoor en-vironment and measurements. Since the measurements were taken at WCDMA2100, WCMDA is chosen in the simulator. For simplicity, the simulation pilotpower for the Floor Four and the Floor Five is set as 6 dBm per antenna head,and the values for the rest of the floors are set to be 9 dBm. User samples areuniformly generated in the building with a density of one user/m2. When con-sidering the dynamic range of the measurements, the gray areas in Figure 3.2bare chosen as an approximation. Therefore, the measurements can be dividedinto two categories according to the relative distance to the atrium. That is:the users close to the atrium are named as atrium users while the users in thecorridors are called corridor users. When comparing with the measurements,

14

(a) Measurement Route (b) Simulation Range

Figure 3.2: Measurement Route and Simulation Range

Type Attenuation(dB)Floor 22Inner Wall 7Atrium 0

Table 3.1: Attenuation of Floor and Different Types of Walls

only the users in the dynamic range of the measurements are considered. Theantenna locations from the floor plan are used as input to the simulator. Fur-thermore the floor and wall loss are chosen according to Table 3.1, considering[12].

3.2 New atrium building setup

As described in Section 3.1, the examined building is an atrium building withthe floor plan shown in Figure 3.1. The geographic information in the floor planshould be digitalized to set up the building in the simulator. According to thetheory from Section 2.3, the first step is carried out the shape identification.Figure 3.1 is a simplified floor plan for the atrium building regardless of thedoors, the stairwell and the decoration of the rooms. The threshold for ρr isset to 0.707 to distinguish circles from rectangles. With shape identification,the semi-circular wall in the center stands out from all the rectangular walls.The way to depict rectangular walls and circular walls differs. Featuring arectangular wall is simply to detect the start point and end point and save theinformation as a vector: [

Ps Pe]

Ps stands for the start point of the wall and Pe represents the end point. Forthe semi-circular wall, the polygonal approximation is used in this thesis. Eight

15

points are sampled, creating a vector:P1s P2e

P2s P2e

......

P8s P8e

After applying the procedure described above, the result of the converted floorplan is displayed in Figure 3.3. It is obvious that the converted floor plancontains most of the information in Figure 3.1. The building created in thesimulator is displayed in Figure 3.5a.

Figure 3.3: Floor Plan Conversion

3.3 Atrium Effect

From this section till the end through this chapter, a step-by-step approach todesigning the indoor propagation model is explained. The received signal codepower (RSCP) and geometry, which is the signal to interference ratio in the fullload scenario, are the primary metrics considered.

To start with, the characteristics of the measurements are investigated. Be-cause of the confidential issue, the RSCP value for every measurement pointis not provided. Instead, the RSCP distribution figures are displayed togetherwith an RSCP description. The RSCP measurements from Cell One show thatthe users on the first three floor receive a good quality of the signal with a meanvalue around -60 dBm. The RSCP distribution for the first three floor is quitesimilar as shown in Figure 3.4a. Taking the Floor Three as an example, thehighest RSCP belongs to the atrium users. When considering the Floor Four tothe Floor Eight, the mean RSCP values experience a roughly 20 dB loss. TheRSCP distribution for the high floors is also similar. The atrium users sufferhigher interference from Cell One, with the highest RSCP measurements. How-ever, collected measurement points from the higher floors are much less thanthat from the first three floors. Most of the RSCP values for the corridor usersare impossible to be detected, which is due to the UE resolvable requirement.

16

Deep into one floor, the atrium users get the most severe inference from theCell One. The RSCP for the atrium users is around 10 dB higher than thatof the corridor users on the same floor. The same phenomenon occurs in themeasurements from Cell Two. The RSCP on the target floor (the Floor Four tothe Floor Eight) is pretty good with the mean values around 60 dB. However,measurement results for the first three floor are around 10 dB lower than thatof the target floors. The atrium users on the first three floors are also highlyinterfered by the antennas in Cell Two. This power leakage through the atriumto the non-target floors is called atrium effect in this thesis. The atrium effectis the most interesting point in the atrium building, reflecting the multipatheffect.

(a) Cell One (b) Cell Two

Figure 3.4: RSCP Distribution from the Atrium Building

After After showing the measurements, the simulation results from the 2DKeenan-Motley algorithm for Cell Two is presented in Figure 3.5a. However,the results do not reflect the atrium effect. The simulation RSCP for the targetfloor is approximately -60 dBm while the values for the non-target floor is con-tinuous without some maxima values. The atrium users do not have a higherRSCP than that of the corridor users.

The problem with applying the 2D Keenan-Motley algorithm in the atriumbuilding occurs when the signal passes through the atrium. In reality, when thesignal passes through the atrium instead of the floor, there is no extra floor loss.Therefore, the total path loss is reduced. To solve this problem, 3D Keenan-Motley algorithm is introduced. One improvement for the 3D Keenan-Motleyalgorithm is that it checks if the signal goes through the atrium, when calculat-ing nF in Section 2.1.3. The detailed description of the new algorithm is givenlater in Chapter 4.

Figure 3.6 shows visualized results after applying the new algorithm. The onlyFitted Pico Omni antenna is placed adjacent to the atrium on the third floor,labeled as the red circle. The users are uniformly distributed throughout thebuilding. The results show that the atrium users on the third floor receive ahigh signal power. Besides, there is power leakage from the third floor to thefirst two floors. The atrium users enjoy an approximate 20 dB higher RSCPthan that of the corridor users, which is the atrium effect.

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(a) 2D Keenan-Motley Algorithm (b) 3D Keenan-Motley Algorithm

Figure 3.5: RSCP Simulation Results from Two Algorithms for Cell Two

(a) Antenna Position(b) RSCP Simulation Results for a SingleAntenna

Figure 3.6: Atrium Effect

After applying the referenced DAS deployment described in Section 3.1, thesimulation results of Cell Two for both the 2D Keenan-Motley algorithm andthe 3D Keenan-Motley algorithm are presented in Figure 3.5. The RSCP on thetarget floors, from the Floor Four to the Floor Eight, is similar for the two dif-ferent algorithms while the values for the non-target floors are different. In the

18

Figure 3.5b, the simulation results on non-target floors of the first three floorsfor the 3D Keenan-Motley algorithm are continuous except for some maximavalues for the atrium users, which are plotted as the color of orange. Thesemaxima values are approximately 20 dB higher than that of the 2D Keenan-Motley algorithm, which is the atrium effect that we expect to see.

(a) Measurement Distribution of Cell One (b) Simulation Distribution of Cell One

(c) Measurement Distribution of Cell Two (d) Simulation Distribution of Cell Two

Figure 3.7: RSCP Comparison with 3D Keenan-Motley Algorithm

Figure 3.7 shows the RSCP comparison between the simulation results andmeasurements after applying 3D Keenan-Motley Algorithm. Generally speak-ing, the simulation distribution for Cell One is similar to the measurements.For the target floor, the mean RSCP values are around -60 dBm similar to themeasurements. When considering the simulation on the Floor Four, the meanRSCP experiences a 20 dB loss. The mismatch is that the curves for non-targetfloors are separated wider than the measurements. However, the simulation re-sults for Cell Two do not fully match the measurements. The simulation resultsfor the target floors are in good agreement with the measurements. The prob-lem appears when we focus the simulation results for the non-target floors. Themeasurements indicate that the RSCP differences are only around 10 dB for allthe floors while the simulation results show a 20 dB difference. This differenceis related to the antenna diagram.

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3.4 Antenna Diagram Modification

In the existing simulator, a simple antenna model to start with is the FittedPico Omni antenna. As shown in Figure 2.4, the antenna has an omni constantgain in the horizontal plane and has two symmetric lobes in the vertical plane.However, the vertical diagram of the Fitted Pico Omni antenna is quite differentfrom that of Kathrein 80010709 antenna installed in the atrium building. Asdepicted in Section 2.2.2, the vertical plane pattern of the Kathrein antenna isin the shape of a butterfly. Therefore, a new antenna diagram is designed inthe thesis to mimic the Kathrein antenna. In the horizontal plan, the antennahas an omni gain with a constant value of 2 dB. As shown in Figure 3.8b, ithas a butterfly shape in the elevation plane pattern. 0◦ is the angle pointingvertically downwards when the antenna is mounted on the ceiling.

(a) Horizontal-Plane

−15 −10 −5 0

30

210

60

240

90 270

120

300

150

330

180

0

(b) Vertical-Plane

Figure 3.8: An Approximation of the Kathrein 80010709

The simulation results based on the modified antenna pattern are presentedin Figure 3.9. For Cell Two, the RSCP distribution results show significantimprovements with the new antenna diagram. The RSCP on the target floorsis similar to the measurements with a mean RSCP around -65 dBm. For thenon-target floors, the RSCP is increased fitting the measurements. The meanRSCP for the Floor Three is only 10 dB lower than that of the Floor Four,which is similar to the RSCP difference of the measurements.

However, there are still some differences between measurements and simula-tion results for Cell One. The maximum value of the measurements of the FloorFour in Figure 3.9b, which belongs to the atrium users, is about -60 dB whilethat of the simulation RSCP is around -70 dBm. The RSCP differences lead toa significant difference in geometry results in Figure 3.10 as well. Focused onthe Floor Four to the Floor Eight, the measurements results originate around10 dB lower than that of the simulation values. To improve the interferenceperformance and to recreate lower geometry, the recursive diffraction model isintroduced.

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(a) Measurement Distribution of Cell One (b) Simulation Distribution of Cell One

(c) Measurement Distribution of Cell Two (d) Simulation Distribution of Cell Two

(e) Simulation Results of Cell One (f) Simulation Results of Cell Two

Figure 3.9: RSCP Comparison with Modified Antenna Diagram

3.5 The Recursive Diffraction Model

The recursive diffraction model is introduced for a better description of thewireless channel in the atrium building. The recursive diffraction propagationis modeled as three-section diffraction in the thesis. Taking Figure 3.11 as anexample, the diffraction propagation follows the route along the atrium. Tx is

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(a) Measurement Distribution (b) Simulation Distribution

Figure 3.10: Geometry Distribution Comparison with Modified Antenna Dia-gram

the antenna installed on the ceiling of the Floor Six and Rx is the receiver onthe Floor Two. D1 and D2 are the diffraction points in the atrium on the samefloor as Rx and Tx respectively. After originating from Tx, the signal diffractsat D1 first, propagates along the atrium all the way to D2, diffracts and finallyreaches Rx. The received signal strength from both the diffracted path and thestraight path are compared, and we pick the larger value as the received RSCP.

Figure 3.11: Diffraction Model

Figure 3.12 shows the impact of the recursive diffraction model. The diffractionparameters q90 = 0.5 and v = 1.1 are selected to best fit the measurement data.The users are uniformly distributed on the second floor (4m) with a density ofone user/m2. The red dots represent the antennas which are placed on the firstfloor (2.9m) in Figure 3.12a and on the third floor (8.9m) in Figure 3.12b, re-spectively. The solid black circles are the users whose diffracted path is strongerthan the straight path. From the simulation results, when the users are above

22

the transmitter, a higher percentage of the users select the RSCP from thediffracted path prediction rather than the straight path as compared to whenthe users are below the transmitter.

(a) Upwards (b) Downwards

Figure 3.12: Diffraction Path Influence

After implementing the recursive diffraction model, the simulation results aredisplayed in Figure 3.13. When considering the RSCP distribution in Cell One,the RSCP of the atrium users for the non-target floor is increased and betterfits to the measurements distribution. The maximum RSCP for the Floor Fouris -60 dB, which is the same as the measurements. Besides, the curves for thenon-target floors become more compact to each other, which is the similar tothe measurements. The results for Cell Two are also in good agreement withthe measurement.

Another improvement from the recursive diffraction model is the geometry sim-ulation results. As shown in Figure 3.14, the worst geometry belongs to theFloor Three and the best geometry performance belongs to the Floor Eight.The worst geometry for the Floor Four to the Floor Eight has lowered down,better matching the measurements. Considering the comparison for both RSCPand geometry, the designed propagation model shows good agreement with themeasurements.

23

(a) Measurement Distribution of Cell One (b) Simulation Distribution for Cell One

(c) Measurement Distribution of Cell Two (d) Simulation Distribution for Cell Two

(e) RSCP Simulation for Cell One (f) RSCP Simulation for Cell Two

Figure 3.13: RSCP Comparison with Recursive Diffraction Model

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(a) Measurement Distribution (b) Geometry Simulation Distribution

Figure 3.14: Geometry Distribution Comparison with Recursive DiffractionModel

25

Chapter 4

Algorithm Optimization

In this chapter, a new algorithm to calculate nF and nW is proposed. The maingoals are to fix the problems of the 2D Keenan-Motley algorithm and acceleratethe computation speed at the same time. Since a more accurate propagationdepiction by introducing the new algorithm has been shown in Figure 3.5, thischapter focuses on the benefit in the time consumption.

4.1 Drawbacks of the 2D Keenan-Motley algo-rithm

The original 2D Keenan-Motley algorithm described in Section 2.1.3 have somelimitations. First, it is unable to depict the atrium effect since the algorithmdoes not consider the case that the signal passes through the atrium. Second,it only suits the building with an identical floor plan. If the floor plan varies,it returns the incorrect values of nF and nW , resulting in an inaccurate predic-tion for the path loss. Furthermore, it is inefficient from a computation pointof view. The 2D Keenan-Motley algorithm tries to calculate nF and nW byapplying trigonometric method. For each loop only one transmitter and onereceiver are considered. Thus, in an MATLAB based simulator environment,the program consumes a great deal of time when a large number of users andantennas are examined. Table 4.1 shows the time consumption in seconds inthe scenario with 13 antennas (only considering the antennas on the first floor)and user density one user/m2 in the eight-floor atrium building.

Function Time consumption (s)Runit 23893Scenario Setup 33Path Loss Generation 23825The 2D Keenan-Motley algorithm 21442

Table 4.1: Time Consumption for the 2D Keenan-Motley Algorithm, LimitedNumber of Antennas

26

The Runit is the main function for the simulation. It calls the Path LossGeneration responsible to estimate the path loss which in term calls the 2DKeenan-Motley algorithm. As shown in the above results, it is obvious that the2D Keenan-Motley algorithm is the bottleneck for the simulation. It consumes21442÷ 23893 ≈ 90% of the total time consumption. Furthermore, the simula-tion is just for the antennas on the first floor. If setting up the simulation forthe whole the DAS system, i.e., 104 antennas, it is expected to take more than53 hours.

4.2 3D Keenan-Motley algorithm

Therefore, the 3D Keenan-Motley algorithm is designed. As shown in Figure 4.1,Tx is the transmitter on the ceiling of the fourth floor while Rx is the receiveron the first floor, i.e., Ft = 4 and Fr = 1. In (where n is the floor index andI1 = Rx) are the intersections of the straight ray TxRx and the floors. Pn arethe projections of In+1 and P4 is the projection of Tx on the fourth floor. ThusInPn is the projection of the straight ray on the corresponding floor n. Thealgorithm is described as follows.

Figure 4.1: 3D Keenan-Motley Algorithm.

• The floor separation in height between Tx and Rx is calculated. TakeFigure 4.1 as an example, the floor separation is three.

• Compute the intersections In. Count In that are in the center atrium area.In Figure 4.1, only I3 is in the atrium. Therefore, the total number of floorsthe signal passes through is the difference between the floor separation andthe total number of In within the atrium area. Specifically nF equals to2 in Figure 4.1.

• The projections Pn are calculated.

• The final step is to check if there are any walls between In and Pn foreach floor. Assume there is a certain wall CD lying in the building. Thecriterion for the signal traversed by the wall CD is to satisfy the followingmathematical equations.

(−−→CIn×

−−→CD)·(

−−→CPn×

−−→CD) < 0

(−−→InC×

−−−→InPn)·(

−−→InD×

−−−→InPn) < 0

(4.1)

27

Function Time consumption (s)Runit 124Scenario Setup 38Path Loss Generation 45The 3D Keenan-Motley algorithm 33

Table 4.2: Time Consumption for the 3D Keenan-Motley Algorithm, LimitedNumber of Antennas

Function Time consumption(s)Runit 810Scenario Setup 31Path Loss Generation 729The 3D Keenan-Motley algorithm 188

Table 4.3: Time Consumption for the 3D Keenan-Motley Algorithm

After applying the above procedures, the time consumption for the 3D Keenan-Motley algorithm is logged. To be able to compare with Table 4.1, the simulationscenario setup is kept the same, only considering the antennas on the first floor(13 antennas ) and user density one user/m2 in the building.

Table 4.2 shows that the time consumed when applying the 3D Keenan-Motleyalgorithm. When compared with Table 4.1, the 3D Keenan-Motley algorithmsaves up to 99.5% of the time consumed by the 2D Keenan-Motley algorithm,which is a significant improvement. As a result, the total simulation duration isreduced dramatically. Besides, it also shows that the time consumption for theScenario Setup stays stable.

Table 4.3 shows the simulation takes around 809 seconds for the whole DAS(104 antennas) and user density one user/m2 in the eight-floor atrium building.The 3D Keenan-Motley algorithm only takes 188 seconds. These results showthe simulation has been improved significantly by involving the 3D Keenan-Motley algorithm.

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Chapter 5

System performanceanalysis

Chapter 3 gives a detailed explanation of the designed propagation model forthe examined building. Based on this model, a system performance analysis isperformed in this chapter. To evaluate the performance from the future perspec-tive, LTE release 8 is chosen with a bandwidth of 20MHz. The traffic service isfile download and upload.

To start with the concept of cell splitting is introduced. Then a system per-formance comparison between the closed building and the atrium building isperformed based on different cell splitting configurations. The chapter ends upwith a discussion of cell splitting principles in the examined building.

5.1 Cell Splitting

The early outdoor mobile systems were designed to ensure coverage. The radiusof the cells was up to several miles. However, the traffic demand has increasedsignificantly over the decades which cannot be satisfied by the old outdoor cellconfiguration. Therefore, cell splitting is introduced. In cell splitting, a parentcell is divided into two or more daughter cells. Instead of using a single, high-power transmitter in the center, a large number of the low power transmittersare placed in the daughter cells. These new transmitters work simultaneously,which increases the system capacity.

Cell splitting has been applied to indoor environments. The total transmitterpower is split to each antenna. With configurations, the antennas are groupedinto cells. Each new cell covers a smaller area than before. Thus, the throughputis increased, and the whole frequency is reused.

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5.2 System Evaluation of the Closed Buildingand the Atrium Building

As mentioned above, cell splitting brings in higher system capacity. In thissection the splitting gain for the closed and atrium buildings is quantified sepa-rately. The atrium building are described in Section 3.1 while the closed buildinghas the same the layout as the atrium building except for the atrium. Fig-ure 5.1 shows four interesting cell configurations. The reference configuration(Reference) is the current cell configuration in the atrium building described inSection 3.1. In Configuration 3 (Conf 3), there are four cells; every two floorsbelong to one cell. In Configuration 4 (Conf 4) each floor is one single cell. Tomeet high traffic demands in the future, Configuration 5 (Conf 5) is examined,where every floor has five cells leading to a total number of 40 cells.

Figure 5.1: Cell Splitting Configurations

The simulation results for four cell configurations in the closed building and theatrium building are displayed in Figure 5.2. In the left figure the solid lines showthe simulation results for the closed building while the dashed lines representthe results for the atrium building. The X-axis shows the traffic generated perfloor and the Y-axis represents the 5th percentile user throughput. For example,the x value 50Mbps means that the floor generates one 10 Mbits packet every0.2s and the throughput 100 Mbps in the Y-axis means it takes 0.1s to deliverthe packet. It is clear that there is a significant growth in system throughputwhen the splitting factor increases for both the closed and the atrium buildings.The closed building always has a higher system capacity than that of the atriumbuilding for the same cell configuration. The right bar graph illustrates the pro-vided traffic services when the 5th percentile user throughput is 10 Mbps. Theright Y-axis gives the corresponding traffic volume (TU ) that can be providedto one user per month. The assumptions are that the working time is 10 busyhours per day (TD) and 20 days per month (TM ) and that the user density (DU )is one user/10m2. As mentioned, the length of the floor (FL) is 55m and thewidth of the floor (FW ) is 55m. Thus, there are 55 × 55 ÷ 10 = 302.5 usersper floor. Assume the traffic demand per floor (TF ) is 40 Mbps, the average

30

traffic per user per second is the quotient between traffic demand per floor andthe number of users per floor, which is 0.132 Mbps. Thus, each user can enjoy4.76 Gbits per working day, equaling to 11.9 GB per month. Furthermore, thefigure also shows that the splitting gain for the closed building increases moresignificantly than that for the atrium building.

TU =TF

FL × FW /DU× 3600× TD × TM (5.1)

Figure 5.2: System Capacity for Different Configurations

(a) Cell Utilization for Conf 5 (b) DL Geometry for Conf 5

Figure 5.3: System Performance of the Closed and Atrium Buildings with Conf5

Deep into the results, Conf 5 is examined. As shown in Figure 5.3a, when thetraffic load is 93Mbps per floor which equals to 28 GB per user per month, thecell utilization for the closed building is even around 0.5. However, the atriumcells in the atrium building are overloaded. The reason is shown in Figure 5.3b,

31

the downlink geometry of the atrium cells is about 10 dB worse than that ofthe center cells in the closed floor. Therefore, during the high traffic demandscenarios, the interference from the neighboring atrium cells deteriorates the cellcapacity of the atrium building.

5.3 Cell Configuration in the Atrium Building

In this section, three different cell configuration pairs are studied as a baselineto discover the essential network metrics for cell configuration.

5.3.1 Case Study 1

The conclusion from the previous section is that the interference through theatrium limits the system capacity. Thus the question arises that keeping thenumber of cells as 2, compared with the reference configuration, ould it benefi-cial to group all the atrium users as a cell and the corridor users as the othercell. The two configurations are displayed in Figure 5.4, respectively.

Figure 5.4: Cell Configuration Pair 1

As shown in Figure 5.5, the reference configuration provides a higher cell capac-ity. For lower traffic load, the results for the two configurations are overlappedwhile the reference configuration stands out when the traffic per floor require-ment is above 1Mbps.

Figure 5.6 shows that the unbalanced load limits throughput performance forConf 2. The users in the second cell in Conf 2, which are the corridor users, takesup 84% of the total users while the user fraction of the atrium cell is only 16%.As a result, the cell utilization of the second cell in Conf 2 goes immediatelyto 1 while the atrium cell remains under-utilized. The reference configurationkeeps a better balance for user fraction between the two cells, 36% and 64%respectively. To sum up, the first configuration pair shows that keeping theuser fraction balanced among cells contributes to a higher system capacity.

32

Figure 5.5: Cell Capacity for Configuration Pair 1

Figure 5.6: Cell Utilization and DL Geometry for Configuration Pair 1

33

5.3.2 Case Study 2 and Case Study 3

During the high traffic demand scenario, increasing the number of cells pavesa way to achieve higher system capacity. To study further cell configuration inthe atrium building, the atrium areas should be paid special attention to. Onemay wonder if there is a rule to deal with the atrium areas, e.g., whether itis better to keep the atrium area configured the same as the rest of the floorsor it is better to have an atrium cell on top of equally split floors? To answerthis question, the second configuration pair and the third configuration pair isprovided.

Case Study 2 As shown Figure 5.7, every two-floor belongs to one cell inConf 3, while Conf 3b has four two-floor cells as well as an entire atrium cell ontop.

Figure 5.7: Cell Configuration Pair 2

The simulation results in Figure 5.8a show that the Conf 3b provides a highersystem capacity. The results for the two configurations are similar during lowtraffic load scenarios, while the performance of the Conf 3b increases more sig-nificant when the traffic demand per floor is above 5Mbps. Deeper into this,Figure 5.8b shows that the user fraction for both cell configurations is balanced.Since there are more cells in Conf 3b, the user fraction per cell is reduced.Figure 5.8d indicates that the geometry distribution is similar for both the con-figurations. Thus the system capacity is improved in Conf 3b showing thathaving an atrium cell on top of equally split floors gives higher system capacity.

Case Study 3 Finally, a third configuration pair is analyzed for the hightraffic demand scenario. As shown in Figure 5.3.2, every floor has five cells(totally 40 cells) in Conf 5 while corridor areas have 4 cells per floor as well asan extra cell covering the atrium areas of all floors (totally 33 cells) in Conf 5b.

According to Figure 5.10a, it is clear that Conf 5 has better system capacity

34

(a) Cell Capacity (b) User fraction

(c) Cell Utilization (d) DL Geometry

Figure 5.8: System Performance of Cell Configuration Pair 2

(a) Cell Configuration Pair 3 (b) Cell Configuration Pair 3

performance than that of Conf 5b in the high traffic demand scenario. Althoughthe system capacity for Conf 5 starts with a lower level, Conf 5 exceeds Conf5b when the traffic per floor is over 10Mbps. The system capacity differencesbetween Conf 5 and Conf 5b keep growing when traffic demand increases. Thereason is the user fraction for Conf 5 differs while the user fraction for Conf 5b

35

(a) Cell Capacity (b) User fraction

(c) Cell Utilization (d) DL Geometry

Figure 5.10: System Performance of Cell Configuration Pair 3

is quite unbalanced. The atrium cell serves over 20% of the total users while therest of the cells only take care of 2.5% in Conf 5. The unbalanced traffic loadhas a significant impact on the maximum cell utilization in Figure 5.10c. Theatrium cell is overloaded when the traffic demand is only 30 Mbps per floor.Although the geometry of Conf 5b is 2 dB better than that of Conf 5, the over-loaded atrium cell limits the system capacity. The study shows that a singleatrium cell can be problematic as it is more difficult to predict and balance theload of this cell.

All in all, it is difficult to summarize a rule for configuring the atrium cellin the building. However, when doing cell splitting, it always beneficial to con-sider the effects of three aspects, the total number of the cells, the geometryperformance and the balance of the user fraction.

36

Chapter 6

Conclusion and FutureWork

6.1 Conclusion

In this thesis, a new propagation model in an atrium building is developed.Empirical indoor modeling is applied and the new model is calibrated to themeasurements taken centering 2.1 GHz. The evaluated model is a combinationof the Free Space Path Loss model, the Keenan-Motley model and the recur-sive diffraction model. The simulation results show that the 2D Keenan-Motleyalgorithm is not a good model for the atrium building. Therefore, the new 3DKeenan-Motley algorithm is created in the thesis to depict the atrium effect.Besides, it is shown that the right antenna diagram is beneficial to the simu-lation results. The designed butterfly radiation diagram is a better model tomimic Kathrein 80010709 antenna than the Fitted Pico Omni antenna. Withthe parameters chosen as q90 = 0.5 and v = 1.1, the recursive diffraction modelimproves the simulation RSCP of the atrium users to fit the measurements bet-ter. The final simulation results from the model show good quantitative agree-ment with the measured data. Additionally, the 3D Keenan-Motley algorithmis designed in the thesis to reduce the computation complexity and improve theefficiency.

Based on the implemented propagation model, a further analysis of the sys-tem performance on the DAS system is performed. The simulation results showthat the number of the cells, the geometry performance and the balance of theuser fraction affect the system capacity. To cope with the increasing trafficdemand, the cell splitting is employed. Cell splitting gives more gain to theclosed building than the atrium building since the atrium cells suffer from poorgeometry due to the interference. For the examined atrium building, keepingthe atrium as one cell and other areas as the other cell provides lower capacitycompared to the referenced setting because of the unbalanced load. The resultsfor the further cell splitting strategies also indicates while reconfiguring the cel-lular network, both geometry and load balance should be considered when thenumber of the cells is fixed.

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6.2 Future Work

The following can be considered as some interesting aspects for the future work.While generating the user location, a uniform distribution is assumed. There-fore, the traffic demand, which is proportion to the user density, is also uniformlydistributed. However, in reality, it tends to be more users in the office area thanthe atrium area during office hours and there are more users in the atriumarea during fika and lunch break. It is interesting to see what strategies shouldbe taken according to the users behavior. Anther topic could be consideringthe outdoor macro cell impact. If the building is closed to an outdoor macrocell, there are chances that the corridor users prefer macro cell to the indoorcell, which also influences the user distribution. Furthermore, instead of ap-plying DAS, the unlicensed Wi-Fi could be another interesting technology tostudy. Since it is contention-based, the network throughput during high trafficdemand scenario depends on the back-off scheme.

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Bibliography

[1] VNI Mobile Forecast Highlights, 2013-2018. http://www.cisco.com/

assets/sol/sp/vni/forecast_highlights_mobile/index.html. [On-line].

[2] Mobile Europe AprMay 2011. http://viewer.zmags.com/publication/

9576e079#/9576e079/1. [Online].

[3] Atrium (architecture) - Wikipedia, the free encyclopedia. http://en.

wikipedia.org/wiki/Atrium_(architecture). [Online].

[4] Lars Ahlin, Zander, Jens, and Slimane, Ben. Principles of Wireless Com-munications. Studentlitteratur AB, Lund, August 2006.

[5] J. Medbo and J. E Berg. Simple and Accurate Path Loss Modeling at5 GHz in Indoor Environments with Corridors. In Vehicular TechnologyConference, 2000. IEEE-VTS Fall VTC 2000. 52nd, volume 1, pages 30–36vol.1, 2000.

[6] Christer Tornevik, J. E Berg, F. Lotse, and M. Madfors. PropagationModels, Cell Planning and Channel Allocation for Indoor Applications ofCellular Systems. In Vehicular Technology Conference, 1993., 43rd IEEE,pages 867–870, May 1993.

[7] J. E Berg. A Recursive Method for Street Microcell Path Loss Calculations.In Personal, Indoor and Mobile Radio Communications, 1995. PIMRC’95.Wireless: Merging onto the Information Superhighway., Sixth IEEE Inter-national Symposium on, volume 1, pages 140–143 vol.1, Sep 1995.

[8] Fredrik Gunnarsson, Martin N Johansson, Anders Furuskar, Magnus Lun-devall, Arne Simonsson, Claes Tidestav, and Mats Blomgren. DowntiltedBase Station Antennas- A Simulation Model Proposal and Impact on HSPAand LTE Performance. In Vehicular Technology Conference, 2008. VTC2008-Fall. IEEE 68th, pages 1–5. IEEE, 2008.

[9] 80010709 Kathrein Scala Division. http://www.kathrein-scala.com/

catalog/80010709.pdf. [Online].

[10] Ming-Kuei Hu. Visual Pattern Recognition by Moment Invariants. Infor-mation Theory, IRE Transactions on, 8(2):179–187, Feb 1962.

[11] I Siomina, A Furuskar, and G. Fodor. A Mathematical Framework for Sta-tistical QoS and Capacity Studies in OFDM Networks. In 2009 IEEE 20th

39

International Symposium on Personal, Indoor and Mobile Radio Commu-nications, pages 2772–2776, September 2009.

[12] Y.-P. Zhang. Indoor Radiated-Mode Leaky Feeder Propagation at 2.0 GHz.Vehicular Technology, IEEE Transactions on, 50(2):536–545, Mar 2001.

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Appendix A

A.1 Measurements Distribution

In this section, the distribution of the measurements which were taken in theexamined atrium building are presented.

(a) Measurements Distribution for CellOne

(b) Measurements Distribution for CellTwo

(c) Geometry Measurements Distribution

Figure A.1: Measurement Distribution from the Atrium Building

A.2 Simulation Results

In this section the simulation results are presented following the procedure men-tioned in Chapter 4.

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(a) Simulation Results (b) Simulation Results Distribution

Figure A.2: Simulation Results for Cell One with the 3D Keenan-Motley Algo-rithm

(a) Simulation Results (b) Simulation Results Distribution

Figure A.3: Simulation Results for Cell Two with the 3D Keenan-Motley Algo-rithm

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(a) Simulation Results (b) Simulation Results Distribution

Figure A.4: Simulation Results for Geometry with the 3D Keenan-Motley Al-gorithm

(a) Simulation Results (b) Simulation Results Distribution

Figure A.5: Simulation Results for Cell One with the Modified Antenna Diagram

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(a) Simulation Results (b) Simulation Results Distribution

Figure A.6: Simulation Results for Cell Two with the Modified Antenna Dia-gram

(a) Simulation Results (b) Simulation Results Distribution

Figure A.7: Simulation Results for Geometry with the Modified Antenna Dia-gram

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(a) Simulation Results (b) Simulation Results Distribution

Figure A.8: Simulation Results for Cell One with the Diffraction Model

(a) Simulation Results (b) Simulation Results Distribution

Figure A.9: Simulation Results for Cell Two with the Diffraction Model

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(a) Simulation Results (b) Simulation Results Distribution

Figure A.10: Simulation Results for Geometry with the Diffraction Model

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