information report number: ist-2000-28088-momentum-d47-pub · thomas kurner¨ (tu-bs) alexander...

Contract Number: IST-2000-28088

Project Title: Models and Simulationsfor Network Planning andControl of UMTS

Project Acronym: MOMENTUMdisseminationintegration &

characterisation

servicetraffic

estimation

Information Report Number: IST-2000-28088-MOMENTUM-D47-PUB

Date of Delivery: 2003-10-14

Report Title: Final Report on Automatic Planning andOptimisation

Editor: Thomas Kurner (TU-BS)

Authors: Andreas Eisenblatter (Atesio)Hans-Florian Geerdes (ZIB)Daniel Junglas (TUD)Thorsten Koch (ZIB)Thomas Kurner (TU-BS)Alexander Martin (TUD)

Reviewers: Erik Fledderus (TNO)Bernd Heideck (E-Plus)Thomas Winter (Siemens)

Abstract: This document summarises MOMENTUM’s approachon automatic planning of the UMTS RF network configurationof large-scale networks. The approach covers all major aspectsof automatic planning including an adaptive propagation modelapplicable for all relevant deployment scenarios, sophisticatedheuristics to reduce calculation times, and mathematical optimi-sation methods. First results generated by these methods arepresented, showing encouraging results.

Key word list: UMTS, radio interface, planning, adaptive prop-agation model, mathematical model, mathematical optimisation,heuristics, optimisation results , IST, Key Action IV, Action Line IV.4.1

Key action: IV, Essential Technologies and InfrastructuresAction Line: IV.4.1, Simulation & Visualisation

Confidentiality: MOMENTUM public

Document History

Date Version Comment Editor2003-09-02 0.1 Initial draft TK2003-09-10 0.2 Draft Path Loss Propagation TK2003-09-18 0.3 Draft Introduction & Conclusion TK2003-09-27 0.4 Draft Mathematical Toolbox DJ2003-10-08 0.5 Draft Optimisation AE2003-10-09 0.6 Revision TK2003-10-10 0.7 Draft Optimisation Results TK2003-10-13 0.8 Review Version TK2003-10-13 0.9 Layout AE2003-10-14 1.0 Final Version AE/TK

IST-2000-28088-MOMENTUM-D47-PUB MOMENTUM PUBLIC

Contents

1 Challenges in UMTS Radio Network Planning 7

2 Adaptive Propagation Models 112.1 Setting the Scene for Adaptive Propagation Models . . . . . . . . . 112.2 Transition between different DTMs . . . . . . . . . . . . . . . . . . 152.3 Switching between models for small macro cells and micro cells . . 282.4 Indoor and outdoor propagation mechanisms: Interactions . . . . . 332.5 Combining the sub-models . . . . . . . . . . . . . . . . . . . . . . 392.6 Comparison with Measurements . . . . . . . . . . . . . . . . . . . 41

3 Scope of Radio Network Optimisation 433.1 Mathematical Optimisation Models . . . . . . . . . . . . . . . . . 433.2 Input to the Optimisation . . . . . . . . . . . . . . . . . . . . . . . 43

4 Mathematical Toolbox 464.1 MIP Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.2 Heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5 Planning Results 545.1 Coverage and Capacity Shortages . . . . . . . . . . . . . . . . . . 545.2 Optimisation Results . . . . . . . . . . . . . . . . . . . . . . . . . 565.3 Assessing the Optimisation Approach . . . . . . . . . . . . . . . . 62

6 Conclusion 63

Bibliography 65

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List of Figures

1-1 Fundamental difference between GSM and UMTS Radio Planning . 81-2 Trial-and-error method in UMTS Radio Planning . . . . . . . . . . 91-3 Fully automated UMTS Radio Planning . . . . . . . . . . . . . . . 92-1 Propagation environments and configurations . . . . . . . . . . . . 122-2 Example for statistical data derived from high resolution data . . . . 172-3 DTMs with different resolution; terrain profiles between BS and MS 182-4 Land use data in 50 m resolution and building vector data . . . . . . 202-5 Average relative building height in the A2 area . . . . . . . . . . . 202-6 Macro cell prediction using the transition from A1 to A2 . . . . . . 202-7 Profile vector and data available for the transition situation BS in A2 212-8 Drawing a profile vector between BS and MS from raster data . . . 212-9 Fitting equally distributed buildings into profile parts . . . . . . . . 222-10 Distribution of buildings into profile parts . . . . . . . . . . . . . . 222-11 Solution for the “gap” problem . . . . . . . . . . . . . . . . . . . . 232-12 Example for regions, where open areas are assigned . . . . . . . . . 232-13 Prediction using M2-type model and transition for a BS located in A2 242-14 Result as in Figure 2-13 aggregated to a resolution of 50 m . . . . . 252-15 M1-model with statistical database in A2 and land use data in A1 used 252-16 Building vectors of the area at Berlin centre . . . . . . . . . . . . . 272-17 Macro cell prediction in A2 area (see Figure 2-16) using model M1 272-18 Macro cell prediction using M2 and high-resolution data . . . . . . 282-19 Relevant propagation phenomena and sub-models in macro cells . . 292-20 Relevant propagation phenomena and sub-models in micro cells . . 312-21 Results from different micro cell models . . . . . . . . . . . . . . . 322-22 Components determined by the sub-models . . . . . . . . . . . . . 342-23 Components determined by the sub-models . . . . . . . . . . . . . 352-24 Components determined by the sub-models . . . . . . . . . . . . . 352-25 Total result achieved by superposition of the path loss . . . . . . . . 362-26 Example of a prediction result for outdoor-to-indoor propagation . . 382-27 Determining the number of penetrated walls . . . . . . . . . . . . . 382-28 Example of a prediction result for indoor-to-outdoor propagation . . 392-29 Flowchart of the adaptive propagation model . . . . . . . . . . . . 402-30 Comparison measurements with predictions . . . . . . . . . . . . . 423-1 Three types of site sets . . . . . . . . . . . . . . . . . . . . . . . . 455-1 WP- simulation results for Lisbon public scenario . . . . . . . . . 555-2 WP- simulation results for Berlin public scenario . . . . . . . . . . 565-3 Scales for result maps . . . . . . . . . . . . . . . . . . . . . . . . . 565-4 Effects of configuration changes, The Hague . . . . . . . . . . . . . 575-5 Planning result for The Hague (Figure 5-3 for scales and units) . . . 585-6 Min pathloss map, Berlin Alexanderplatz . . . . . . . . . . . . . . 605-7 Planning result for Berlin . . . . . . . . . . . . . . . . . . . . . . . 616-1 Components of automatic optimisation . . . . . . . . . . . . . . . . 63

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List of Tables

2-1 Examples for concrete realisations of generic models Mi . . . . . . 132-2 Application areas of models and required extensions . . . . . . . . 142-3 Available data layers in 5 m resolution . . . . . . . . . . . . . . . . 162-4 Statistical data available in 50 m resolution . . . . . . . . . . . . . . 162-5 Assignment of constant parameters to land use classes . . . . . . . . 192-6 Relative prediction time for different models . . . . . . . . . . . . . 272-7 Availability of Sub-models within the M2- and M3-type models . . 305-1 Running time of Set-Covering on “The Hague” . . . . . . . . . . . 59

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List of Abbreviations

2G second generation3D three-dimensional3G third generation3GPP Third Generation Partnership ProjectBPM Building Penetration ModelBS Base StationCIR Carrier-to-Interference RatioCOST Eurpoean COperation in the field of Scientific and Technical researchDTM Digitla Terrain ModelGSM Global System for Mobile CommunicationsLOS Line-of-SightMIP Mixed Integer ProgramMPM Multi Path Propagation Plane ModelMS Mobile StationNLOS None-Line-Of-SightQoS Quality of ServiceRF Radio FrequencySHO Soft HandoverTPM Transversal Propagation Plane ModelTRX Transmitter/ReceiverUMTS Universal Mobil Telecommunications SystemVPM Vertical Propagation Plane ModelWCDMA Wideband Code Division Multiple AccessWP Work PackageXML eXtendable Markup Language

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1 Challenges in UMTS Radio Network Planning

With the introduction of UMTS new challenges for radio network planning are com-ing up. These challenges are forced by two main aspects. On one hand UMTS hasbeen developed mainly to support new data services including higher and variabledata rates. This is a clear difference to the second generations (2G) systems, likeGSM, where speech traffic is still pre-dominant. Therefore the rules and algorithmsfor the dimensioning and optimisation of GSM networks are typically based on thecharacteristics of the speech service. On the other hand particular aspects of the un-derlying WCDMA radio access method impose fundamental changes in the planningmethodology. For GSM good and proper methodologies, algorithms to support theradio planning process have been established.

The whole GSM planning process can be sub-divided into three main subsequentsteps: coverage, parameter, and capacity planning. Coverage planning consists of theselection of the location and the configuration of the antennas. The coverage areaachieved by a single antenna depends mainly on the propagation conditions and isindependent from all other antennas in the network. During the following parame-ter planning process all radio parameters (frequencies, hand-over configuration andpower control parameters, etc.) are defined. Once a cell is in operation traffic mea-surements are made yielding to the prediction of required number of channels. Theincreased traffic does not affect the coverage area or the parameter settings—at leastin a reasonable good first approximation. In the case an additional TRX has to beinstalled new parameter settings for this TRX may be required. Only when an addi-tional site may be required for capacity reasons the increase of traffic has a (positive)influence on the coverage area. For GSM well-developed algorithms both for thesynthesis and analysis of networks exist and a lot of appropriate planning tools arecommercially available now.

In contrast, the situation for UMTS is much more complicated [12, 41]. Thecell range in a CDMA system does not only depend on propagation conditions butalso on the traffic load of the cell. Furthermore, the amount of interference receivedfrom other cells depends on their traffic load as well. Additionally the traffic loadof a cell is influenced by the soft hand-over areas, which are mainly defined duringthe parameter planning step. Coverage, parameter, and capacity planning are, thus,highly coupled processes yielding integrated planning of these three steps. This fun-damental difference between the planning processes in GSM and UMTS is displayedin Figure 1-1.

The analysis of UMTS networks taking into account these effects is done usingstatic Monte-Carlo simulations as it has been applied to analyse voice services in cd-maOne networks. Although in the recent past these techniques have been adapted toWCDMA [34, 42], the method is still too time-consuming to analyse large networks.In MOMENTUM work package 2 (WP-) methods to accelerate the analysis processhave been successfully developed [39]. Nevertheless these methods are still by fartoo slow, if a synthesis of a network has to be done. This means that synthesis of net-works can be done by trial and error only, when Monte-Carlo simulation is applied.A trail and error approach is already difficult in GSM networks with its independentplanning steps. These difficulties will increase dramatically, when coupled planning

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BSS Parameter

Extension TRX/location

Coverage

Traffic Forecast

GSM UMTS

Path Loss UTRAN Parameter

Traffic Forecast

Change TRX/location

Coverage

Figure 1-1: Fundamental difference between GSM and UMTS Radio Planning

processes are considered. The trial-and-error- method is explained in Figure 1-2.Based on coverage and capacity requirements a first educated guess of a reason-

able configuration has to be done. This configuration includes the site selection, thenumber of sectors, all antenna parameters (pattern, direction, height, tilt, etc.) aswell as the determination of codes, carriers and SHO parameters. Based on this con-figuration a network evaluation using Monte Carlo Simulation is done. Propagationpredictions and traffic maps are taken into account. After the evaluation an adjust-ment of parameters is done and the above described procedure starts again. Thiscycle is repeated until the required quality is achieved. It is obvious that this processis quite time-consuming. Up to now more efficient systematic synthesis methods donot exist for WCDMA. As far as automatic methods in UMTS radio network plan-ning are concerned only preliminary results to this problem based on a characteristiccoverage as defined in 2G radio networks exist [17, 23, 37, 40]. These approachestry to achieve a target coverage using as few BSs as possible from a given list ofpredefined ones. The target coverage is defined by the received power level, whichdepends on the path loss, which is insufficient as explained above.

The goal of MOMENTUM WP- has been to develop automatic optimisation andsynthesis methods in order to overcome the disadvantages of the above mentioned ap-proaches. These methods have to avoid the time-consuming feedback-loop betweenevaluation and configuration. This feedback loop has to be replaced by a fully auto-mated optimisation process, see Figure 1-3. In order to achieve this goal as a first stepanalytical approaches are needed to get a deeper insight into possible solutions of theoptimisation problem from a mathematical point of view. Changes of some input pa-rameters should lead to a predictable behaviour of the output parameters. Based onthis deep understanding of the problem a further challenge is to develop fast heuris-tics, taking into account coverage, cell loading and parameter settings simultaneouslyand self-consistently. With this generalised UMTS coverage model a general meritfunction, based on QoS parameters, has to be evaluated and used for the developmentof computer based optimisation algorithms. Furthermore, a very complicating factorfor modelling is the analysis and description of a typical UMTS multi-service envi-ronment, since at the beginning of the project no heuristics or analytic approaches

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Evaluation

Data/Parameters Network

Choosing/Changing

Simulation

Figure 1-2: Trial-and-error method in UMTS Radio Planning

Evaluation

Data/Parameters Network

Fullyautomated

optimization

Choosing/Changing

Figure 1-3: Fully automated UMTS Radio Planning

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exist for the description of such environment.When it comes to automatic radio planning, the optimisation process has to

rely completely on the result of the propagation prediction. The accuracy of thesepropagation prediction methods has a crucial impact on the overall quality of theplanning and optimisation results. Within the last decade a couple of single prop-agation models suitable for the frequency range of UMTS have been developed invarious projects, e. g. see COST 231 [9], COST 259 [10], ACTS/STORMS [37],RACE/CODIT [8], besides those appearing in literature, e. g. [5]. The drawbackof these models is that they typically only give reasonable agreement when appliedto specific areas. First approaches to combine several of these models have beensuccessfully applied to the case of macro-cells using either low-resolution or high-resolution data, e. g. [27]. In order to exploit such approaches for UMTS it is nec-essary to develop further criteria to switch between low- and high-resolution data, aswell as automatically selecting between micro- and macro-cell models. Such criteriaare not known up to now. However, these criteria are critical in UMTS since the cellrange depends on both service type and data rate yielding a large variation of cellranges even within the same cell. With the successful development of such criteriaan adaptive propagation model can be implemented.

In order to achieve the goal of an automatic planning of UMTS RF network con-figuration WP- has identified three major sub-problems:

• designing an adaptive wave propagation model

• identifying analytic and/or heuristic network performance measures

• and developing the algorithms for network synthesis

The approach developed in MOMENTUM is described in this document, which isstructured as follows: In Section 2 the adaptive propagation model developed in MO-MENTUM is described. Chapter 3 describes the input for the optimisation and givesa brief sketch of mathematical model. The mathematical tools applied are brieflydescribed in Chapter 4. Finally the results are presented in Chapter 5. Note thatChapters 3 and 4 contain only a short summary, since the detailed material is avail-able in other public deliverables of MOMENTUM WP- [13, 14].

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2 Adaptive Propagation Models

There are many planning tools these days for cellular planning purposes, withinwhich propagation models play an important role, for either coverage or interferenceestimation. In these tools, the radio planner still has a key part, for example when de-termining in which areas a specific propagation model should be used, since no auto-matic choice is performed by the tool. An enormous variety of different requirementsto the prediction model in the various propagation environments exists. Therefore dif-ferent propagation models within one large planning area are applied. Although thescience of propagation models is an area of intensive research a universal propaga-tion model applicable to all possible propagation situations is not available. There iseven no perspective, that such a propagation model will be available in the long term.Reasonable results in propagation modelling are achieved by finding more or less ac-curate models for the most dominant propagation phenomena observed for specificapplications. The specific application area of a propagation model is described forexample by the carrier frequency, the typical antenna heights for both base station(BS) and mobile station (MS), the distance between them and the structure of theenvironment (indoor/outdoor, build-up/open/forested, etc.) in the reception area ofthe signal. One consequence of the requirements for this specific application is theavailability of propagation models applicable only within a restricted validity range.Furthermore, these propagation models require digital terrain models (DTMs) whichmay be different either in content (e. g. land use data vs. detailed building data),granularity (e. g. different number of land use classes and/or attributes) and/or reso-lutions.

When applying automatic methods as described in this document accurate predic-tion models are required covering the possible deployment scenarios and operationalenvironments as described for example in [35]. Therefore in MOMENTUM a generalframework for a fully automatic and adaptive selection of propagation models hasbeen introduced [6, 33] addressing the integration of different models in terms of

• different deployment scenarios,

• use of different digital terrain databases and

• the identification of parameters for the selection of different models and/or thetransition between models.

The framework has been tested and applied to MOMENTUM’s German referencescenarios [35] using E-Plus specific implementation.

2.1 Setting the Scene for Adaptive Propagation Models

This section describes the basic ingredients of an adaptive propagation model andalso introduces the modules used for the specific implementation.

2.1.1 Basic Components of an adaptive propagation model

In a UMTS network the full range of cell types will be used. This covers macrocells, which are deployed in rural and suburban areas, small macro cells and micro

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cells occurring in urban areas as well as pico cells in hot spot areas like airports andexhibition halls. In the latter case indoor solutions are applied. These indoor basestations are at least a potential interfering source in the outdoor area. Since alsosignals from outdoor base stations can be received within a certain penetration lossat indoor environments a complete description of the interaction between indoor andoutdoor areas is important.

Typically low resolution data as defined in [6] is available for all environments,whereas the more expensive high resolution data is typically available for the denseurban areas only. The corresponding areas can be defined as follows:

• A1: Area where only low-resolution is available.

• A2: Area where also high-resolution data is available. Subdivision into:

⇒ A2a : outdoor areas⇒ A2b : indoor areas

Theoretically all possible combinations of cell types, deployment mode and DTMavailability have to be considered.

low resolution data (A1)

high resolution data (A2)

M1

M2(+M4)M3(+M4) M5

Figure 2-1: Propagation environments and configurations of practical interest

However, not all possible configurations are of practical interest. Therefore onlythe following propagation models will be used in the adaptive UMTS propagationmodel, see Figure 2-1:

• macro cell models using low-resolution data (M1),

• small macro cell models using high-resolution data (M2),

• micro cell models using high-resolution data (M3),

• outdoor-to-indoor models using high-resolution data (M4), and

• indoor-models using high-resolution data (M5) and their extension to the in-door-to-outdoor scenario.

The Mi are generic descriptions of prediction models. Concrete realisations forthese generic prediction models can be found in Table 2-1, which includes a classifi-cation of the prediction models according to [6].

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Model GenericDescription

COST231-Hata [9] M1Hata [22] M1Walfisch-Bertoni [43] M1, M2COST231-Walfisch-Ikegami [9] M1, M2, (M3)Vehicular Test Environment [1] M1, M2GENERAL model (MOMENTUM model) [11] M1Basile’s Model [2] M1, M2Berg’s recursive street micro cell model [3] M3Wiart’s model [44] M3Jakoby’s model [25] M3Pedestrian Test Environment [1] M3Goncalves Model [7, 20] M3De Jong’s Model [26] M5, M4∗

Mottley-Keynan-Model [36] M5Gahleithner-Bonek-Model [18] M5COST231-Berg Model [4] M4E-Plus hybrid prediction model for macro cells [31] M1E-Plus ray-tracing model for dense urban areas [32] M2Indoor coverage extension to E-Plus ray-tracing model [32] M4

Table 2-1: Examples for concrete realisations of generic models Mi, based on themodels given in [6]

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2.1.2 General Approach

For a complete interference calculation of the network, a matrix containing the mu-tual coupling of cells is required. This coupling matrix is computed by a superpo-sition of predictions with different models and DTM. Therefore a couple of modelextensions, transition models and switching criteria between models are required.The necessary developments can be grouped into four main tasks:

1. For those cases where the prediction area of a cell covers different DTM ap-propriate model extensions for the transition between different data sources arerequired. This includes a model extension of M1-type models in order to ex-ploit high-resolution data in some parts of the prediction area (BS in A1, MSin A2), whereas M2-type models need an extension to low-resolution data (BSin A2, MS in A1). These model extensions are described in Section 2.2 of thisdocument.

2. In dense urban areas three different cell types—small macro, micro and pico(indoor) cells—may be deployed. Therefore switching criteria between M2-,M3-, and M5-type are required. Since the decision between M2 and M3 isnot binary smooth transition functions are required. Details are given in Sec-tion 2.3.

3. The interaction between indoor and outdoor configurations (outdoor coverageby indoor base stations and indoor coverage by outdoor base stations) requiresmodel extensions (M4) to the corresponding models M1/M2/M3 (BS in A1or A2a, MS in A2b) and M5 (BS in A2b, MS in A2a) respectively. Theseextensions are explained in Section 2.4.

BS location MS locationA1 A2a A2b

A1 M1 transition of M1 M1, extensionto high-resolution to high-resolution

areas areas + building(Section 2.2.1) penetration M4

(Section 2.4.1)A2a transition of switching between M2/M3, extension

M2/M3 to high- M2 and M3 M4 for buildingresolution areas (Section 2.3) penetration(Section 2.2.2) (Section 2.4.1)

A2b extension of M5 extension of M5 M5to outdoor areas to outdoor areas(Section 2.4.2) (Section 2.4.2)

Table 2-2: Application areas of models and required extensions

Table 2-2 summarises the different combinations that are of interest for the adap-tive propagation model described in this document, and gives an indication in whichpart of the document the corresponding model extensions are described.

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2.1.3 Basic models used for the specific implementation

In the last years E-Plus has developed propagation models covering the whole rangeof path loss predictions for outdoor mounted BS in cellular mobile systems in the2 GHz frequency range [29, 31, 32]. These models will be the basis for the specificimplementation of the adaptive propagation model and will be described roughly inthis section. The hybrid propagation model for macro cells [31] combines several pre-diction models in a modular construction system. The selection process of the singlemodules is completely unsupervised. Input data are mainly terrain height and landuse information organised as raster data (resolution 50 m×50 m). Additionally streetvector data is used in order to take into account the effect of street orientation andto detect line-of-sight situations between BS and MS antennas within street canyons.For typical macro-cellular applications the prediction accuracy in urban areas is suf-ficient even with such low-resolution data. However, high-resolution building data ismore suitable for BS antennas mounted at or only a few metres above roof-top levelin dense urban areas [30], where a run-time efficient 3D propagation model [32] isapplied taking into account propagation within a vertical plane as well as multi-pathsignals according to the concept introduced in [28]. Run-time efficiency is achievedby taking advantage from the findings in [32] that for dense urban areas multi pathpropagation seems to be important only within a distance of up to 500 m to the BS.Therefore at larger distances considering propagation within a vertical plane is suffi-cient. A further improvement of the prediction accuracy is achieved by consideringvegetation effects. Indoor coverage by outdoor BS is considered by empirical andsemi-empirical extensions to the outdoor prediction model.

2.1.4 Introduction of Statistical Data

A key issue is the identification of parameters that can be used to perform an auto-matic selection without the intervention of the user of a planning tool. These parame-ters are typically derived from digital terrain data bases and depend on the availabilityof A2-areas and the properties of the data at the specific location. The properties ofthe data layers available in A2-areas with a resolution of 5 m are listed in Table 2-3.The E-Plus City model [32], applies these high-resolution raster layers.

Apart from the pure selection process, some of the models mentioned in Table 2-1can also make use of a statistical database, which is derived from the layers of a high-resolution database. A prominent example of a model requiring such data is Basile’smodel [2], which will be the basis for the transition model described in Section 2.2.1.The statistical database, which has been derived from the data layers in Table 2-3 andis used for the specific implementation of the adaptive propagation model is describedin Table 2-4. An example for the relation between the high-resolution and the (low-resolution) statistical database is shown in Figure 2-2 for the layers Hb, µH , ω and µS .

2.2 Transition between different DTMs

For a complete interference calculation the coupling between cells located in differentapplication areas Ai has to be quantified. In practical network planning the two types

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Data Description of dataht(xn, yn) terrain height: absolute terrain height (without buildings) above

sea levelV (xn, yn) vegetation occupation: pixel containing vegetationB(xn, yn) building occupation: pixel containing a buildingO(xn, yn) open areas/street occupation: pixel containing neither vegetation

nor buildingsHb(xn, yn) building height: absolute building height above sea-level; rel-

ative building height is calculated as the difference betweenHb(xn, yn)andht(xn, yn)

w(xn, yn) street width; calculated for each raster pixelr(xn, yn) street orientation; calculated for each raster pixel

Table 2-3: Available data layers in 5 m resolution

Data Description Data GenerationµH average relative building heights weighted average over all relative

building heights within a windowof 200 m×200 m; available as pre-processed layer

σh standard deviation of averageabsolute building height

standard deviation of the absolutebuilding height calculated along aprofile or within a specified predictionarea; not available as pre-processedlayer; calculated during predictionrun-time

µS average street width averaged over all street width valueswithin 50 m×50 m; available as pre-processed layer

IV vegetation index percentage of pixels with pseudo clut-ter value 2 within 50 m×50 m; avail-able as pre-processed layer

IB building index percentage of pixels with pseudo clut-ter value 1 within 50 m×50 m; avail-able as pre-processed layer

Table 2-4: Statistical data available in 50 m resolution

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build

ing

heig

hts

stre

et w

idth

5m reso lu tion 50m reso lu tion

Figure 2-2: Example for statistical data (50 m resolution) derived from high resolu-tion data (5 m resolution): absolute building heights Hb (upper left), average relativebuilding heights µH (upper right), street width w (lower left) and average street widthµs (lower right) ; c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH& Co. KG

of data (A1 and A2, see Section 2.1) are used.Therefore both directions of transitions have to be considered. The transitions

have to be taken into account when the terrain profiles between the BS and MS aredrawn, see Figure 2-3. In both cases it is important that the resolution and the gran-ularity applied to the terrain profile is homogeneous for the whole profile. Otherwiseit will not be possible to apply fast profiling and line-of-sight-checking algorithms,which typically require most of the computing time of a predictor. The solid parts ofthe profile lines correspond to those parts where the original data resolution can beused. The dashed lines represent those parts where the data resolution changes. Forthose parts of the profile transition models are required. Based on this assumption themain task of the transition models is to transform the DTM information available atand near to the MS into the resolution and granularity of the DTM available at the BS.Section 2.2.1 describes the models which can be applied to situations represented bythe terrain profile BS1 to MS1, whereas Section 2.2.2 introduces the model exten-sions required for the profile between BS2 and MS2. Furthermore, in Section 2.2.3 apossibility to speed-up M2-type models based on the transition models is presented.

2.2.1 Transition from low-resolution to high-resolution areas

This transition problem is the less complex one since the data at the mobile endcontains more details than the propagation model can process. This means that theprecise information (detailed building information) has to be generalised to a lower

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A1

A2

A1

BS2

MS2 BS1

MS1

Figure 2-3: DTMs with different resolution and granularity; terrain profiles betweenBS and MS

degree of complexity. On the other hand the data should contain still more informa-tion as the low-resolution land use data in order to be able to increase the predictionaccuracy. Macro cell models exploit the digital terrain height and land use data. Theterrain height described is used to determine the large-scale terrain obstacles due tohills, mountains, etc. The variation of this ground height is large compared to theresolution in both DTMs. Therefore the re-sampling of the digital terrain height istrivial and does not require any changes of the prediction algorithms. This is differentwhen land use data is considered. Typically land use data allows only a rough statisti-cal description of the terrain morphology. Land use data is typically exploited by themacro-cellular prediction models by applying clutter loss correction factors [9, 11].Clutter loss factors are usually determined by a calibration from measurements.

A more advanced method is the assignment of geometrical and/or electrical tothe land use classes parameters. For more details see [2, 29, 31]. The same set ofparameters is assigned to all pixels carrying the same land use class. The advantageof assigning parameters to land use classes is that the clutter loss is not a constantbut has an inherent dependency on the specific height of MS and BS as well as thedistance between BS and MS. Furthermore, these types of prediction models are ableto process also data from a statistical database as described for example in [2, 30].In [2, 30] the improvements of prediction accuracy achievable by a statistical databaseare shown. Based on this information the general approach of the transition modelcan be formulated as follows:

1. In the A2-areas a statistical database is derived in the resolution of the databaseavailable in A1 based on the high-resolution information available in A2. Thedifferent layers of the statistical database should be picked from Table 2-4based on the availability of the data layers in the A2 area and also based onthe possible input parameters the macro-cell can process.

2. When evaluating the terrain profile the parameters from the data layers of thestatistical database are used in the A2-areas (dashed line in Figure 2-3) insteadof the land use data.

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3. The prediction model uses this data for the path loss calculation. It shouldbe noted that even prediction models, that apply only clutter loss correctionfactors can make use of a statistical database. An example how clutter losscorrection factors can be derived from geometrical parameters is given in [24].

This general approach is now applied to the E-Plus macro cell model [31]. Insteadof using clutter loss correction factor for urban areas the E-Plus model uses set ofparameters for the mean relative building height µH , the average street width µS andthe average building separation µB is assigned for each of the four different urbanland use classes in order to compute the basic path loss in urban areas. The modeldistinguishes four types of urban land use classes. The assignment of parameters tothe four urban type land use classes is shown in Table 2-5. These values are used,

Land use class µH [m] µB [m] µS[m]

Dense urban 10,1 58,5 16,8Urban 8,9 59,9 22,5

Suburban 8,0 50,6 18,2Industrial 6,3 43,4 18,2

Table 2-5: Assignment of constant parameters to land use classes in the E-Plus macrocell model [31]

if the mobile is located in A1. Otherwise the concrete values from the statisticaldatabase are used instead. The average building separation µB is derived from thebuilding occupation B.

In order to demonstrate the transition model it is applied at the boundary of avail-ability of high-resolution data at Berlin. Figure 2-4 shows the land use data togetherwith building vectors and the areas A1 and A2. The average mean building heightwithin the A2 area is displayed in Figure 2-5. A prediction is done for a BS locatedin A1 using an omni-directional antenna located 23 m above ground height (meanbuilding height at BS position is 21 m), see Figure 2-6.

It can be observed, that the prediction result looks more homogeneous in theA1 area when compared to the rapid variations observed in the A2 area. This is notsurprising since the ground terrain height at Berlin is more or less constant. Thereforethe path loss is mainly influenced by the land use. By a comparison of the land useinformation in Figure 2-4 with the prediction it is obvious that the predicted signalschange mainly when the land use class changes. On the other hand heavy signalvariations in the A2 area can be observed, where rapid changes of the mean buildingheight occur.

2.2.2 Transition from high-resolution to low-resolution areas

M2-type predictions require high-resolution building data as input. If a BS and MSis located in A2 and A1, respectively, the profile vector drawn from the BS to theMS has to process two types of data in the different parts of the profile vector, seeFigure 2-7.

A general approach how this problem can be tackled by M2-type models is de-scribed in this section followed by the specific implementation at E-Plus.

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A2 A1

Figure 2-4: Land use data in 50 m resolution and building vector data; A1 and A2-areas; c© Clutter Data (2002), E-Plus Mobilfunk GmbH & Co. KG; c© Digital Build-ing Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

Figure 2-5: Average relative building height in the A2 area; c© Digital BuildingModel Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

Figure 2-6: Macro cell prediction using the transition from A1 to A2 for a basestation located in A1; c© Digital Building Model Berlin (2002), E-Plus MobilfunkGmbH & Co. KG

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A2 A1

detailed building data land use data

profile points

Figure 2-7: Profile vector and data available for the transition situation BS in A2 andMS in A1

2.2.2.1 General approach. For the general understanding of following problemsit is important to know how a profile vector is defined between a BS and MS us-ing raster data, see Figure 2-8. In order to accelerate the profiling process standardmethods from computer graphics are used, see for example [16]. In these methodsthe values from the raster pixels located along the direct profile line are assigned toprofile points, which are generated by projection of the pixel centres. Therefore thedistance between the profile points is not equidistant. The profile points along theprofile line together with their attributes (height, land use class, etc.) are called theprofile vector.

BS, MS

profile

center of pixel

Figure 2-8: Drawing a profile vector between BS and MS from raster data usingstandard methods from computer Graphics

In order to be able to apply M2-type models in A1-areas a distinction has to bemade according to the land use type for the terrain profile drawn from 5 m resolution:

Land use type urban. Generic buildings are fitted into the terrain profile at thepoints of land use type urban. The corresponding parameters—building height h,street width w, building separation b (compare Table 2-5)—have to be assigned tothe different land use classes. The fitting process always starts at MS-end of theterrain profile assuming that the MS is located in the middle of the street. Hencethe distance from the MS to the first building seen towards to the BS is w/2, seeFigure 2-9.

The parameter dlanduse in Figure 2-9 denotes the length of the path along subse-

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W2w

2w

W

b

dlanduse

profile points

building

dbuilding

Figure 2-9: Fitting equally distributed buildings into profile parts of clutter typeurban

quent pixels of the same land use class. Note that in the general case the distance ofthe profile point representing the MS and the next profile point representing a build-ing is not w/2. In those cases the MS position is moved towards the BS to the nextprofile point which has a distance of w/2. This guarantees consistency with M1-typemodels like [31], where the mobile is assumed to be in the centre of the street. A con-sequence of that rule may be a slight inaccuracy in the distance dependent path lossterm. However, this can be neglected at the relatively large distances to the BS wherethis transition model is applied. In order to guarantee a smooth transition betweenareas of different land use classes also buildings on the very left-hand side of this landuse class (see land use class “dense urban area” in Figure 2-10) should have ideallyalso a distance of w/2 to the beginning of the next land use class or the beginning ofthe detailed building data.

open area urban area dense urban area

2w

W

Figure 2-10: Distribution of buildings into profile parts with different land useclasses; possible occurrence of gaps

Note that w is constant for all pixels of the same land use class. Hence thisprocedure of “producing” buildings out of land use data is applied as long as thecondition

d ≤ w (2.1)

is fulfilled, where

d = dlanduse − nb (2.2)

n = [dlanduse/b] (2.3)

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If the relation in equation 2.1 is not fulfilled large open areas (“gaps”) withinthe urban areas may occur at the boundary between two different land use classesresulting in an underestimation of path loss in those regions due to the large streetwidth assumed, see Figure 2-11. This problem is resolved by introducing a smallerbuilding of the width

dbuilding∗ = d∗ − w (2.4)

An example showing the solution of the “gap” problem is shown in Figure 2-11.

Land use type forest. The profile points from pixels carrying the land use typeforest are marked as pixels with vegetation occupation.

W2w

2w

W

d*

dlanduse

dbuilding*

Figure 2-11: Solution for the “gap” problem

land use type open

land use type urban

open area assignment

Figure 2-12: Example for regions, where open areas are assigned although the landuse type is urban

Land use type open. The profile points from pixels carrying the land use type openare marked as pixels with open area occupation. In order to have a smooth transitionbetween open and urban area land use classes profile points within an urban land useclass located between pixels an open area land use class and the first building areassigned to open area occupation as well, see Figure 2-12.

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2.2.2.2 Specific implementation using E-Plus models. The general approach isapplied to the E-Plus ray-tracing model [32]. Some adjustments are made in orderto achieve consistency in the transition region between the M1- and M2-type mod-els [31, 32]. The following rules are introduced in detail:

• At pixels carrying the land use type “open” path loss is calculated according tothe path loss calculation for open areas in the macro cell model [31].

• At pixels carrying the land use type “forest” the lateral wave approach usedin [31, 29] is used, if the path length through vegetation is larger then 300 mand the distance between the beginning of the vegetation area and the MS isat least 400 m. In all other cases the vegetation sub model from [32] is used.Note that the extension to lateral wave propagation is now also applied withinA2-areas.

• In those situations within the ray-tracing model, where the COST-231-Wal-fisch-Ikegami-Model is selected and the multi path sub model is not applied,the path loss is reduced by 8.7 dB as a heuristic derived from the findingsin [21].

• At distances larger than 2 km the method described in Section 2.2.3 is applied.

2.2.2.3 Results. Figures 2-13 through 2-15 show the prediction results for a basestation (antenna height 23 m above ground level, omni-directional antenna) locatedin the A2-area. The results produced by an M2-type model and a 5 m×5 m resolutionare depicted in Figure 2-13. The same prediction result aggregated to a resolutionof 50 m×50 m is displayed in Figure 2-14. Both predictions reveal a shadowingeffect into 45◦ north direction which is caused by a single high building (see arrow inFigure 2-13 to Figure 2-15). For a comparison Figure 2-15 shows the result achievedby the macro cell model (M1-type) and the statistical database, where this shadowingcan not be predicted.

BS

Figure 2-13: Prediction using M2-type model and transition for a BS located in A2(prediction resolution 5 m); area taken as zoom from Figure 2-4. The arrow pointsto a building higher than the BS; c© Digital Building Model Berlin (2002), E-PlusMobilfunk GmbH & Co. KG

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BS

Figure 2-14: Same prediction result as in Figure 2-13 aggregated to a resolution of50 m; c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

BS

Figure 2-15: Prediction for same BS as used in Figure 2-14; M1-model with statis-tical database in A2 and land use data in A1 used; c© Digital Building Model Berlin(2002), E-Plus Mobilfunk GmbH & Co. KG

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2.2.3 Application of the transition model to speed up M2-type applications

Although the initial idea of developing the transition model has been to predict pathloss prediction inside A2 areas, when the BS is located in A1—areas, the modelcan be applied to scenarios where the BS is in A2 areas as well. This approach hasthe potential to reduce calculation times for large areas dramatically. In [32] it isshown that multi-path propagation in urban areas delivers a significant contributiononly within a circle of about 500 m around the base station. At larger distances therelevant part of the signal energy propagates within a so-called vertical plane. Onthe other hand for large distances path loss is influenced by the large-scale variationof building height in conjunction with the building height and street width at the MSposition rather than by the detailed height of each individual building. Both the large-scale behaviour and the mean building height and street width can be retrieved from astatistical database. Therefore the following rule can be applied in order to speed-upprediction times:

• M2-type prediction model is used within a circle of 2 km around the base sta-tion

• Transition model A1→A2 (see Section 2.2.1) is applied for distances largerthan 2 km.

M2-type prediction models typically provide predictions with a resolution of5 m×5 m, whereas the transition model provides results in a 50 m×50 m grid. De-pending on the specific task of further processing and the capabilities of the radioplanning tool in terms of processing mixed grid resolutions an adaptation of the reso-lution is necessary. This means that either the 5 m×5 m results have to be aggregatedto a 50 m x 50 m resolution (averaging over 100 pixels) or the 50 m x 50 m pixels haveto be split into 5 m×5 m (assigning identical values to each pixel of a 5 m×5 m grid).Results for the area (total size 8 km ×10 km) shown in Figure 2-16 are depicted inFigure 2-17 and Figure 2-18. The prediction based on the statistical database is onlypresented in Figure 2-17, whereas the combination according to the above mentionedrule is shown in Figure 2-18. The dashed circles in these two figures represent anarea of 2 km around the BS. Although the prediction model used for calculating theresult in Figure 2-18 changes at a distance of 2 km to the BS a “hard” transition is notobserved.

The gain in terms of calculation time using this approach is demonstrated byTable 2-6. The reference calculation time of 3500 sec, which corresponds to 100%,has been determined using the E-Plus ray-tracing model producing a path loss grid of5 m×5 m resolution and a prediction radius of 2 km. It can be seen that the computingtime for the transition model can be neglected compared to the computing time of theray-tracing model.

The multi path sub-model is applied only for distances less than 500 m. Since therelative increase in computing time from 200 m to 500 m is larger compared to theincrease observed between 500 m and 2 km it is obvious that the multi path sub-modelrequires most of the computing time.

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prediction radius [m] Relative prediction time [%]M2-type model M2-type model +

transition model transition modelA1 → A2 A1 → A2

100 4 4 0.1200 14 14 0.1500 86 86 0.1

1000 88 88 0.12000 100 100 0.15000 514 100 0.2

20000 — 101 0.4

Table 2-6: Relative prediction time for different models; reference M2-type modelwith prediction radius 2000 m: 3500 s measured at a SUN workstation (Ultra60,360 MHz, 1 GB RAM)

Figure 2-16: Building vectors of the area at Berlin centre, co-ordinate grid size res-olution 1 km ×1 km; c© Digital Building Model Berlin (2002), E-Plus MobilfunkGmbH & Co. KG

BS

Figure 2-17: Macro cell prediction in A2 area (see Figure 2-16) using model M1 andthe statistical database. Dashed circles indicate a radius of 2 km

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BS

Figure 2-18: Macro cell prediction using M2 and high-resolution data for distancesless than 2 km (results aggregated to 50 m grid); same area as used in Figure 2-17

2.3 Switching between models for small macro cells and micro cells

In dense urban areas the base station antenna may be deployed either above or be-low roof-top levels. Depending on the height of the base station antenna relative tothe height of the surrounding buildings the cell is classified as being a small macro(antenna mounted above average rooftop level) or micro cell (antenna mounted be-low average rooftop level), see e. g. [9]. A number of prediction models exist in theliterature, that can handle these two deployment scenarios, see [6] for a detailed listof these models. However, most of these models have been developed for only oneof these two deployment scenarios. In this section it will be shown, how the corre-sponding models can be combined into a more general model covering both scenariosfocusing on the definition of automatic criteria for the selection of the correspondingcomponents.

2.3.1 Propagation mechanisms and sub-models

The dominant propagation phenomena for small macro and micro cells have to beidentified. Based on this identification the sub-models can be defined. In this contexta sub-model is a part of an M2- or M3-type model. This allows a classification of themodels from Table 2-1 in terms of available sub-models. With such a classificationit will be possible to compose the general model by combination of different sub-models.

2.3.1.1 Propagation phenomena. Following the approach in [28] the propaga-tion in dense urban areas can be sub-divided mainly into three different parts:

1. Propagation in a Vertical Plane, characterising the energy transported along aprofile between the BS and MS covering diffraction over roof-tops in none-line-of-sight (NLOS) cases as well as reflection processes and free-space prop-agation in line-of-sight (LOS) cases.

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2. Multipath Signals, characterising signals that propagate between BS and MSvia single or multiple scattering or reflection processes occurring on buildingwalls.

3. Propagation in a Transversal Plane, characterising the energy transported in apropagation plane perpendicular to the vertical plane covering mainly diffrac-tion around the building corners.

It has to be noted that in some models listed in Table 2-1 the sub-models arenot mutually exclusive. For example, some semi-empirical micro cell models exist,e. g. [44], which takes into account contributions from both scattering (multi-pathsignals) and diffraction around building corners (propagation in a transversal plane).

Theoretically a general prediction model can consider signals from all three com-ponents, whatever the location of BS and MS are. However the required computa-tional effort for the determination of the multi-path signals and the possible propa-gation paths in the transversal plane does not allow such a “brute force” procedure.In order to achieve reasonable calculation times, i. e., acceptable for practical ap-plication the consideration of propagation phenomena has to be restricted to thosescenarios where a significant contribution to the complete signal can be expected.

Based on the propagation phenomena described above three basic sub-modelscan be defined:

• Vertical Propagation Plane Model (VPM)

• Transversal Propagation Plane Model (TPM)

• Multi Path Propagation Model (MPM)

As a supplement to these basic sub-models also models for the consideration ofvegetation (VM) as defined in [32] or for the penetration through buildings (buildingpenetration model BPM) as introduced by de Jong [26] can be applied.

BS

MS

Multipath Model (MPM)

Vertical Plane Model(VPM)

Figure 2-19: Relevant propagation phenomena and sub-models in macro cells

The distribution of the models applicable in dense urban areas (M2- and M3-type)is set up in Table 2-7 according to their sub-model classification. Since some of the

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Model VPM VPMN MPM TPM BPM VMLOS NLOS

Walfisch-Bertoni [43] x xCOST231-Walfisch-Ikegami [9]

x x

Vehicular Test Environ-ment [1]

x x

Basile’s Model [2] x xBerg’s recursive street mi-cro cell model [3]

x x

Wiart’s model [44] x xJakoby’s model [25] x x xPedestrian Test Environ-ment [1]

x x

Goncalves Model [20, 7] x x xE-Plus ray-tracing modelfor dense urban areas [32]

x x x x

De Jong’s model [26] x

Table 2-7: Availability of Sub-models within the M2- and M3-type models fromTable 2-1

models presented there consider the vertical plane for LOS only the VPM sub-modelsare distinguished also by their capability to handle LOS and NLOS situations. TheVPM is selected for all types of scenarios. For small macro cells, see Figure 2-19,the path loss by diffraction around the corners is much higher than diffraction overroof-tops, which is quite obvious when the number of diffraction points and the corre-sponding diffraction angles are considered. Therefore the TPM is omitted for macrocell scenarios. However, the MPM is important, if the distance between MS and BSis below 500 m, see [32].

In micro cell scenarios, see Figure 2-20, also propagation in the transversal planehas to be considered.

2.3.2 Composing the General Model

The composition of a general model reduces to two sub-problems. The first sub-problem consists of finding the criteria for the selection of the appropriate sub-mo-dels. The second sub-problem is to define the rules for the superposition of thesub-models. Solutions to both sub-problems depend heavily on their characteristicspicked from the list in Table 2-7. In this paper the discussion will be restricted to arough description of the specific implementation only. A more detailed overview isgiven in [33].

2.3.2.1 Sub-models used in the specific implementation. The specific imple-mentation uses the E-Plus ray-tracing model, which already contains the VPM andMPM. This model is completed by a TPM model from the list in Table 2-7 contain-

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Tranversal Plane Model (TPM)

BS

MS

Multipath Model (MPM)

Vertical Plane Model(VPM)

Figure 2-20: Relevant propagation phenomena and sub-models in micro cells

ing five different models usable as a TPM. Note, that the Berg-model is included asTPM in the Pedestrian Test Environment model as well. Therefore only the modelsfrom Berg, Wiart, Jakoby and Goncalves have been considered further. Predictionresults achieved by these four models for a typical micro cell scenario (BS antenna5 m above ground level, omni-directional antenna) are shown in Figure 2-21. Themodels from Jakoby and Goncalves include a VPM as well, whereas the other twomodels are pure TPMs. The results from Berg’s and Wiart’s show at which partssignificant TPM contributions are expected since the terrain processing is the samefor all four models. Goncalves’ model did not show TPM contributions at all parts,where Wiart and Berg do, which is mainly due to the fact, that Goncalves mainlypredicts signal enhancements at the street crossings.The signal levels for NLOS ofthe remaining three models reveal that Wiart predicts more optimistic values whencompared to Berg. On the other hand Jakoby is more pessimistic than Berg. FinallyBerg’ s model has been selected for the specific implementation, which has also beenselected by 3GPP for the Pedestrian test Environment [1]. Apart form the three basicsub-models VPM, TPM and MPM also the Vegetation Model (VM), see [32], is ap-plied to all three sub-models. Furthermore, de Jong’s model for penetration throughbuildings (BPM) is applied as an add-on to the TPM.

2.3.2.2 Criteria for the selection of sub-models. The VPM exists in all scenar-ios. Hence no selection criterion is required. However, depending on the selection ofthe concrete models from the list in Table 2-7, a distinction between different VPMimplementations is required. Therefore a differentiation between LOS/NLOS andmicro cell/ macro cell scenarios has been made. The MPM is selected based on adistance criterion, that is only, if the distance between BS and MS is below a certainthreshold the MPM is selected. Selecting the TPM is the most complex one. As a firstcriterion the TPM is selected only if the MS is within the same distance to the BS asspecified for the MPM. The second criterion makes a decision according to the base

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BS

BS

BS

BS

Figure 2-21: Results from different micro cell models; upper left: Berg’s recursivestreet micro cell model [3]; upper right: Wiart’s model [44]; lower left: Jakoby’smodel [25]; lower right: Goncalves model [20]; same legend as in Figure 2-25;c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

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station height relative to the building heights and considers the street width at theMS location as well. Therefore a decision function [33] is introduced. The decisionfunction k gives values between 0 and 1, where k = 1 for a pure micro cell scenarioand k = 0 for a pure macro cell scenario. The TPM is selected only if k is larger thana threshold kth. k is calculated as the product of k1, k2 and k3(0 < ki < 1). k1 = 1,if the BS antenna height is below the average building height at the BS location. k2

depends on the BS antenna height, the average building height and the standard devi-ation of the building heights along the profile between BS ⇒ MS. k3 depends on thestreet width.

2.3.2.3 Superposition of the sub-models. The superposition is done in the fol-lowing way by calculating the total path loss Ltotal:

Ltotal =

{

L1, if LOS exists between BS and MS

L3, else(2.5)

where

L1 = min(

L2, LV PM∗

)

(2.6)

L2 = 10 log10

(

10LV PM /10 + 10LMPM /10)

(2.7)

L3 = 10 log10

(

10LV PM /10 + 10L4/10)

(2.8)

L4 = min(

LMPM , LTPM

)

(2.9)

and LV PM is the path loss in the vertical plane, LTPM is the path loss in the transver-sal plane, LMPM is the path loss in the vertical plane and LV PM∗

is the path loss inthe vertical plane taken from a micro-cell model (M3-type) when k > kth.

The predicted received power achieved by the three different sub-models and theBPM are shown in Figure 2-22 to Figure 2-24 at three antenna heights (5 m, 20 m,25 m). The mean building height at the BS location is 21 m. The three antenna heightshave been selected in order to have all three possible scenarios: a distinguished microcell scenario (5 m), a distinguished macro cell scenario (25 m), where the TPM isswitched-off (because of k1 = 0) and one scenario where the antenna height is aroundroof-top level (20 m).

Figure 2-24 contains the results from the VPM and MPM components only. Aninteresting result from the lower two antenna heights is that at most locations, wherea signal from the transversal plane can be received a multi path signal with at leastcomparable strength is received as well. This is an interesting result by itself. Thismeans, that most of the energy in micro cells propagates via scattering processes. Inthis case the signal strength determined by scattering processes only (using MPM) isapproximately the same as computed using models for a combined consideration ofboth diffraction around the building corners and scattering processes (using TPM).

2.4 Indoor and outdoor propagation mechanisms: Interactions

This section presents prediction models for describing the interactions between in-door and outdoor propagation. Outdoor-to-indoor propagation is dealt with in Sec-tion 2.4, while indoor-to-outdoor progagation is handled in Section 2.4.2.

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BS

BS

BS

BS

Figure 2-22: Components determined by the sub-models in the specific implementa-tion (BS antenna height 5 m). upper left: VPM; upper right: MPM; lower left: TPM;lower right: BPM; same legend as in Figure 2-25; c© Digital Building Model Berlin(2002), E-Plus Mobilfunk GmbH & Co. KG

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BS

BS

BS

BS

Figure 2-23: Components determined by the sub-models in the specific implemen-tation (BS antenna height 20 m). upper left: VPM; upper right: MPM; lower left:TPM; lower right: BPM; same legend as in Figure 2-25; c© Digital Building ModelBerlin (2002), E-Plus Mobilfunk GmbH & Co. KG

BS BS

Figure 2-24: Components determined by the sub-models in the specific implementa-tion (BS antenna height 25 m). left: VPM; right: MPM; TPM and BPM not appliedin this situation; same legend as in Figure 2-25; c© Digital Building Model Berlin(2002), E-Plus Mobilfunk GmbH & Co. KG

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Figure 2-25: Total result achieved by superposition of the path loss from sub-modelsfor the three different antenna heights: 5 m (top), 20 m (bottom left) and 25 m (bottomright); c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

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2.4.1 Calculation of indoor coverage from outdoor base stations

An detailed description of a general model that is extended for indoor coverage pre-dictions can be found in [32]. This model can be applied to any prediction model thatis able to predict outdoor path loss based on high-resolution building data. This sec-tion gives a brief summary of the general approach and the specific implementation.

2.4.1.1 General approach. Indoor coverage prediction can be done as an add-onto any outdoor M2-type path loss prediction model, which is able to predict outdoorpath loss values based on high-resolution building data. The basic calculation stepscan be summarised as follows:

1. Check for LOS between all walls – intersected by the terrain profile betweenBS and MS – of considered floor and the BS.

2. If LOS exists, use a LOS-model based on [4].

3. In the NLOS case the indoor path loss for the floor level is calculated based onthe outdoor path loss at ground floor of all pixels surrounding the building byapplying a constant building penetration loss.

4. For the higher floors an empirical height gain is applied.

5. In both LOS and NLOS only one average value per floor and building is deter-mined since no additional information about the interior structure of the build-ings is available.

In those cases, where only a prediction based on low-resolution data (includinga statistical database) is available, the constant building penetration factor is appliedto all buildings within the low-resolution pixel. This applies for example to modelsdescribed in Section 2.2.1.

2.4.1.2 Specific implementation. The specific implementation is the one that isdescribed in [32], where also a detailed verification with measurements can be found.The constant building penetration loss applied is 22 dB. An example for the completeindoor and outdoor coverage prediction is given in Figure 2-26 for the same basestation as used in Section 2.3.

2.4.2 Calculation of outdoor coverage from indoor base stations

The fraction of outdoor path loss, where the antenna is mounted inside a buildingin order to provide dedicated indoor coverage, is usually quite high due to the highpenetration losses. In conjunction with the relatively low transmission powers ofsuch cells the signals are receivable (even as an “interferer”) only within a very smalldistance outside the building. Therefore a quite simple approach based on the Multi-Wall-Model introduced by Mottley-Keenan [36] is sufficient.

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BS

Figure 2-26: Example of a prediction result for outdoor-to-indoor propagation; in-door and outdoor coverage for a macro cell using a base station at 23 m height;c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

2.4.2.1 General approach. If a BS antenna is deployed indoor the path loss iscalculated by the following equation [36]:

Ltotal = Lfreespace + nwLwall (2.10)

where

Lfreespace free space loss

nw number of walls penetrated

Lwall penetration loss of each wall

The number of walls penetrated is determined by counting the number of inter-sections between buildings and the profile vector drawn between the BS and MS, seeFigure 2-27.

Figure 2-27: Determining the number of penetrated walls

2.4.2.2 Specific implementation. In the specific implementation the indoor-to-outdoor prediction model is automatically selected, if the BS is in A2-areas, the BS

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location is inside the building vector and the antenna height above ground level isbelow the height of the building. This means that the co-ordinate of the antenna hasto be chosen carefully (located slightly outside the building), if antenna is mounted onthe wall, but outside the building in order to prevent this “indoor mode” for outdoormicro cells. The wall penetration loss (Lwall) is set to 22 dB. This value ensures theconsistency to the outdoor-to-indoor propagation model.

Figure 2-28: Example of a prediction result for indoor-to-outdoor propagation; in-door and outdoor coverage for a base station antenna deployed within a building;c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

An exemplary result is shown in Figure 2-28. In this figure only the signal out-door and inside the building of deployment is shown. However, also the outdoor-to-indoor model described in Section 2.4.1 can be applied yielding an indoor-outdoor-indoor-model.

2.5 Combining the sub-models

All the prediction model extensions, switching criteria and transition models de-scribed in Sections 2.2–2.4 have been integrated into one adaptive propagation model.The overview illustrating the work flow is available in Figure 2-29. This adaptivepropagation model automatically analyses the availability of digital terrain data, pro-cesses the digital terrain and makes decisions on the models and sub-models to beused based on the location and antenna height of the BS and MS. The only selec-tion the user has to make is the grid size of the path loss results. The grid size isdistinguished into high-resolution (∆p = 5 m in the specific implementation) andlow-resolution (∆p = 50 m in the specific implementation). The flowchart of theadaptive model is displayed in Figure 2-29. The first decision is made based on theBS location, where an M1-type prediction model is selected, if the BS is in A1-typeareas. If parts of the prediction areas cover A2-areas as well additionally the tran-sition model from Section 2.2.1 is applied. For those cases where the BS is in A2aareas the ray-tracing-model, described in Section 2.3 is applied, if the MS is in A2-areas and at a distance of less than 2 km. Figure 2-30 summarises also the relevant

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BS location

MS locationMS location Indoor-to-Outdoor-Model

A1 A2b

A2a

start

Macro cell model (M1)

Macro cell model (M1)+ Transition Model

A1 A2

A1

A2

M2-type-model (VPM)+ Transition Model

M2-type-model (MPM)

Distance BS->MS

< 500m

> 2000 m

500m < d < 2000m

M2-type-model (VPM)

k>kth

M2-type-model (TPM)

yes

no

Superposition of VPM/MPM/TPM

ready

M2-type-model (VPM)+ Trans. Model

∆phigh

l ow

Apply M4-typemodel to allindoor locations

Generatehigh-

resolutiongrid

∆phigh Aggregate

to low-resolution

grid

low

chapter 4

Figure 2-29: Flowchart of the adaptive propagation model (∆p corresponds to thegrid size of the path loss grid)

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selection criteria and the superposition. For distance larger than 2 km the VPM of theray-tracing model is applied to the statistical database according to Section 2.2.1. Incase the MS is located in A1-areas the transition model introduced in Section 2.2.2is used. The Indoor-to-Outdoor model (Section 2.4.2) is applied, if the BS is in A2b(indoor deployment). Based on the required resolution of the resulting path loss gridthe generated grids are either split into high-resolution grids or aggregated into low-resolution grids in those cases where the grid size is not identical to the output gridsize. Additionally, in those cases, where a high-resolution grid is selected both out-door and indoor coverage is determined in A2 areas. If only a low-resolution pathloss grid is selected the mean outdoor path loss is determined for each pixel of thegrid. The flowchart in Figure 2-29 is valid for both the general approach presented inthis document as well as for the specific implementation, of course.

2.6 Comparison with Measurements

Since the specific implementation is based on E-Plus’ sub-models, which are pub-lished and compared with measurements in the open literature a separate verificationof the models has not been done. However, for some parts of the reference scenariosof MOMENTUM also measurement data is available. Figure 2-30 shows an examplefrom a measurement run for a small macro cell located in the Karlsruhe reference sce-nario. In this case the City-Prediction model [32] has been selected by the adaptivepropagation model. Additionally the prediction by the simple MOMENTUM predic-tion model [6, 11] has been included. The MOMENTUM model has been used toproduce the path loss grids of the public scenarios [15, 19]. The MOMENTUM modelconsists basically of a combination of Okumura-Hata-Model and a knife-edge modelusing a refine effective antenna height module and the four clutter classes open, for-est, urban and water. It can be observed, that the City prediction model follows mostof the variations of the measurement, which is not the case for the simple model,although it is able to predict the general tendency.

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-120

-110

-100

-90

-80

-70

-60

-50

-40

-30

1 101 201 301 401 501 601measurement bin

rece

ived

po

wer

/ d

Bm

Measurement

Adaptive propagation model(as used for reference scenarios)

Simple MOMENTUM model (as used for public scenarios)

Figure 2-30: Comparison measurements with predictions by the City predictionmodel [32] and the simple MOMENTUM model [6, 11]

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3 Scope of Radio Network Optimisation

As stated before one goal of WP- is to develop automatic planning and optimisationmethods for the radio network interface with focus on the “static” installation ofradio base stations. We are thus concerned with the following decisions: which ofthe candidate sites to use as base station locations; what sectorisation to use at eachsite; which antenna types serve the individual sectors; what are the heights and thetilts of the antennas; and how much power to allocate to the pilot signals. Thesedecisions are to be taken for each site in a planning area. The goal is to design anetwork that is capable to support the offered traffic at a minimum cost.

3.1 Mathematical Optimisation Models

The corresponding optimisation problem is formalised in different ways in the math-ematical optimisation problems presented in [13, 14]. In both cases, the satisfactionof users’ demand is of primary importance and the cost minimisation is subject tomeeting this goal. The second model, in [13] strictly enforces that all user demandis satisfied so that the optimisation may not choose to leave areas with traffic uncov-ered. As a consequence, the latter model requires a planning situation in which theavailable network infrastructure is in principle able to cope with the traffic. We willcome back to this later.

Formalising the optimisation problem is not solving it, but formalising the prob-lem is often a good step towards solving the problem. Let’s first explain why simplyproviding a sound mathematical model in the form of a mixed integer linear program-ming model does not solve the problem. Although there has been stunning progressin developing general purpose mixed integer programming solvers (academic andcommercial) over the past decade, the MOMENTUM network optimisation problemsare simply too challenging for state-of-the-art solvers.

Nevertheless, we use these models for solving the planning and optimisation tasksin several ways. On the one hand, the model presented in [13] is indeed often solvableusing mixed integer programming solvers for subproblems of larger planning prob-lems. In Section 4.1 we sketch how the ability to solve small problems can be usedwhen solving the large MOMENTUM network planning scenarios. On the other hand,the mathematical models also serve as guidelines or “role models” for the develop-ment of heuristic methods. Such methods do not guarantee to produce an optimalsolution to the planning problem, but ideally they empirically produce good resultswithin reasonable running times. Several examples of heuristic network planningmethods are presented in Chapter 6 of [13]. An overview of these methods is alsoprovided in Section 4.2.

3.2 Input to the Optimisation

Prior to successfully mastering the planning and optimisation task, the mere gener-ation of all required input to the planning and optimisation process turns out to be achallenge itself. In addition to most of the input required for classical (static) networksimulation, see WP-, automatic network planning requires a whole new dimension

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of input data. While a fixed network configuration is given for network evaluation,the task of network planning is to come up with a good network design. For doing so,all degrees of freedom and all limitations have to be specified explicitly or implicitly.It has to be specified

• which sites to set up Node-B equipment are available

• which Node-B equipment is available

• which antenna locations are available at a site, e. g. roof-top corners

• whether certain sector numbers are required per site

• which antenna types may be used

• whether restrictions occur in setting azimuths and tilts

• which antenna heights may be used per site

• which costs are associated with planning decisions

• which service (mix) is the network to be planned for

• which additional planning guidelines to obey

These are some examples from a potentially very detailed list of specifications for aplanning task. The ability to answer these questions is built into the MOMENTUM

XML formats [15], and we make extensive use of this ability in defining the MO-MENTUM planning scenarios in [35]. Chapter 3 of [13] provides a good overview ofwhat enters the definition of a planning task and how this data is retrieved from theXML data files.

One issue concerning the input data provision shall be pointed out specifically,namely, the definition of reasonable antenna installations for each site (consisting ofan antenna location, type, height, azimuth, electrial and mechanical tilt). We take asemi-automatic approach to derive suggestion for such configurations, which we callinstallation site sets. Three types of site sets are used during WP- optimisation:

Star. A star is a set of installations with at most one installation per azimuth (in stepsof 10◦). That is, each feasible azimuth direction is represented by one candidateinstallation with a specific antenna type, height, electrical and mechanical tilt.

Blossom. A blossom is a subset of a star, where all installations are separated inazimuth by at least 30◦.

Clover leaf. A clover leaf is again a subset of a star, it contains at most three instal-lations, all of them separated in azimuth by at least 90◦.

These concepts are illustrated in Figure 3-1. A star, a blossom, and a clover leafare provided as part of the optimisation input for each potential site. An extension tothe regular MOMENTUM XML format is used for this purpose. The stars, blossoms,and clover leaves are determined via an analysis of path loss and traffic conditionsaround a site.

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(a) Stars (b) Blossoms (c) Clover leaves

Figure 3-1: Three types of site sets used for planning, Berlin Alexanderplatz; c© Dig-ital Building Model Berlin (2002), E-Plus Mobilfunk GmbH & Co. KG

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4 Mathematical Toolbox

This section deals with solving the mathematical optimisation problems derived fromnetwork planning tasks. We sketch how the application of mathematical program-ming techniques as well as heuristic planning methods are employed to obtain “cheap”UMTS radio networks that are nevertheless capable of supporting the users’ demand(as specified as part of the planning task).

The mathematical optimisation models given in [13, 14] are used in four ways:

• we solve the original model using mathematical programming techniques onsmall subproblems

• we solve the original model using heuristics on the original problem

• we solve modified/simplified models using mathematical programming tech-niques on small subproblems

• we solve modified/simplified models using heuristics on the original problem

The use of the first and the third approach is addressed in Section 4.1, while thesecond and the fourth approach are addressed in Section 4.2.

All approaches discussed in the following suffered from a serious flaw in mostof the planning tasks designed within WP-. The user demand to be served turnedout to be excessively high, much too high to be served by any reasonable networkconfiguration on the basis of the available sites and hardware.

4.1 MIP Approach

This sections explains how the mathematical optimisation problem obtained from theplanning task as indicated in Section 3.2 is solved using mathematical programmingtechniques. The basic idea is to apply a standard mixed integer linear program solver,ILOG CPLEX Version 8.0 in our case, to mixed integer programs (MIPs) associatedwith subproblems of the original planning tasks as follows:

1. All potential sites are pre-configured with clover leaves (see Section 3.2), andthe solution to the MIP is used to determine which sites to be used in the finalnetwork design.

2. Each of the sites selected in the first step is supplied with a blossom of installa-tions (again, see Section 3.2); and the corresponding MIP is used to determinewhich 1 up to 3 installations shall be used at each site.

2′. Alternatively, a sequence of MIPs is solved. The starting point is the resultfrom the first step. One site is selected for optimisation. This site is thensupplied with all installations from its star (see Section 3.2). The solution tothe MIP determines which of the installations to use, given that the surroundingnetwork remains. The resulting network is part of the input to the next iteration,where another site is optimised. This procedure is executed at least once foreach site.

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The MIP-based optimisation is typically performed on the basis of 5–10 trafficsnapshots at a time.

While the procedure described as (2′) performs a local optimisation, (1) and (2)have a global scope. Both steps may therefore depend on solving a rather larger,challenging MIP. This did not work in all cases. Whenever solving such a large MIPfailed, we resorted to (2′). A pre-configured network with all potential sites is thenused as starting point, and the local optimisation is also allowed to close the site it iscurrently optimising.

Despite performing only a local optimisation, the procedure (2′) fully takes thesurrounding network configuration into account, including all interference stemmingfrom surrounding cells.

4.2 Heuristics

In Sections 6.1 through 6.5 of [13] we described many heuristic methods for powerassignment, mobile assignment and installation selection. All of these heuristics fol-lowed more or less the general scheme described in Algorithm 1.

Algorithm 1: General Heuristic Scheme

1. Select a subset I ⊆ I of installations.

2. Assign (some) mobiles from M to I .

3. Run a power assignment algorithm to evaluate the result of the previous steps.

4. If the configuration found is infeasible or not good enough go back to eitherinstallation selection or mobile assignment.

In this section we will now present which of the heuristic algorithms are actuallyfeasible for large-scale, real-world problem instances.

4.2.1 Power Assignment

Recall from [13] that the “Power Assignment Problem” is defined as follows:

Assume we have a configuration C = (I,M,X), that is

• a set I ⊆ I of currently selected installations,

• a set M ⊆ M of currently served mobiles and

• a set X ⊆ I ×M of current mobile/server-connections, where

(i,m) ∈ X ⇐⇒ mobile m is currently served by installation i.

Given this configuration C , find (minimal) feasible transmission pow-ers for each connection in X . If no such assignment can be found, theconfiguration C is claimed infeasible (or INVALID).

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In [13] we also described how we can compute a value (or score) for feasible con-figurations and how we can compare two different configurations or identify optimalconfigurations based on this score.

As outlined in [13], we must run a power assignment strategy as soon as we wantto decide whether a currently given configuration is feasible or if we want to com-pare different configurations. All heuristic algorithms from [13] proceed by selectingsome solution and trying to improve it by a local exchange routine. In other words,all heuristics produce a very large number of configurations that must be tested andcompared in order to decide in which direction the search should proceed.

Thus power assignment is the bottleneck of all these algorithms and hence anefficient power assignment strategy is vital in order to keep the running times of themobile assignment and installation selection algorithms tolerable.

In [13] we have described three variants for power assignment in detail: an iter-ative approach, an approach based on LP techniques and an approach that results insolving a system of linear equation. The system in the latter approach is normally ofsize |M| × |M| but it was shown in [13, 38] how this system can be reduced to asystem of size |I| × |I|. It turned out that Algorithm 2 using this linear system ofreduced size is the only strategy that is reasonable for large-scale instances.

4.2.2 Mobile Assignment

Also described in [13] was the “Mobile Assignment Problem”:

Given a set I ⊆ I of currently selected installations and a set M ofmobiles, find a best feasible configuration C = (I,M,X) where M ⊆M.

Among all mobile assignment algorithms described in [13], Algorithm 3 has provento be the only feasible one to be used as a subroutine in the selection of installations.

This algorithm gives each mobile in M exactly one “chance” to be served andis therefore much faster than algorithms that try different servers for each mobile.On the other hand, it basically simulates the “best-server” planning rule and thusproduces reasonable results.

In order to improve solutions returned by the above algorithm we tried differentlocal search/improvement techniques such as Tabu Search, Simulated Annealing orEvolution Algorithms. For these strategies it turned out that the only applicable oneis Tabu Search which provides visible improvement in reasonable time.

4.2.3 Installation Selection

Similar to the Power Assignment and Mobile Assignment Problems, the “InstallationSelection Problem” was also defined in [13]:

Given a set I of installations and a set M, find an “optimal” subsetI ⊆ I of installations, that is one that allows an optimal configurationC = (I,M,X) with M ⊆ M.

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Algorithm 2: Power Assignment

Input: A configuration C = (I,M,X).

1. For both uplink and downlink derive a (different) quadratic system of linearequations as follows:

(a) Fix

xim =

{

1 if (i,m) ∈ X and,

0 otherwise.(4.1)

(b) For each served mobile pick its corresponding CIR-inequality (in whichall integral variables are fixed now).

(c) This yields two inequalities for each mobile: one for uplink and one fordownlink.

(d) Collect all uplink CIR-inequalities in one system and all downlink CIR-inequalities in another one.

(e) In the two systems replace inequality with equality and assume pilotpower to be constant.

2. Both systems are now |M |×|M | systems of linear equations in |M | continuouspower variables.

3. Reduce the obtained |M | × |M | systems to |I| × |I| systems (details for thisreduction can be found in [13, 38]).

4. Solve both systems of linear equations in order to determine power values foreach mobile/server-connection.

5. If there is at least one connection for which the transmission power found ishigher than the maximum allowed transmission power. In this case return IN-VALID (the configuration C is infeasible).

Otherwise all transmission powers assigned meet their respective upperbounds. In this case return these power values.

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Algorithm 3: Mobile Assignment Algorithm

Input: A set I ⊆ I and a set of mobiles M ⊆ M that should be assigned to someinstallation i ∈ I .

1. For each mobile m ∈ M define

γm = min{

i ∈ I| γ↑mi + γ↓

im

}

(4.2)

and find im ∈ I that satisfies

γ↑mim

+ γ↓imm = γm. (4.3)

2. Sort the mobiles in M to obtain a sorted list (m1,m2, . . . ,m|M |) that satisfiesγmj

≥ γmkfor j < k, that is, sort the mobiles by decreasing sum of

attenuation.

3. Set C = ∅ (C is the set of chosen connections).

4. For j = 1, . . . , |M | perform the following step

5. If it is possible to additionally connect mobile mj to installation imjadd the

connection (mj, imj) to C .

6. Return C .

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Among the set of installation selection algorithms that were described in [13], thereare only two algorithms that can handle our large-scale problem instances reasonablywell: The first one is the Greedy Algorithm 4, the second one is the Set CoveringAlgorithm 5.

Algorithm 4: Installation Greedy

Input: A set of installations I and a set of mobiles M.

1. Set I = ∅ and S = ∅.

2. For each installation i ∈ I find a set of mobiles Mi that i can (potentially)serve if all other installations are off.

3. Let ci denote the cost for installation i, cs the cost for site s ∈ S and cm thecost for an unserved mobile. With these values define

C ′i = ci − |Mi| · cm (4.4)

Ci =

{

C ′i if σ(i) ∈ S and,

C ′i + cσ(i) otherwise.

(4.5)

4. Find

i0 = arg min{

Ci| i ∈ I}

. (4.6)

5. If Ci0 < 0 set I = I ∪ {i0}, S = S ∪ σ(i0) and M = M \ Mi0 .

6. Set I = I \ {i0}.

7. If I 6= ∅ and M 6= ∅ goto step 2.

8. Return I (and S).

If the problem instances become huge (say I has cardinality of several thousandsand M has cardinality of a few ten thousands) the Greedy Algorithm also fails toterminate in an acceptable time.

As the solutions of the Set Covering Algorithm also tend to be better than thoseof the Greedy Algorithm our recommendation is as follows:

• Use the Set Covering Algorithm in order to obtain results in reasonable time.

• In [13] we proposed to run the set covering algorithm multiple times and tochange to cost for the installations during the algorithm. This technique isnot feasible if problem instances grow large. Instead one should adjust theparameters of the algorithm to the given problem instance and compute onlyone set covering solution.

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Algorithm 5: Installation Set Covering

Input: A set of installations I and a set of mobiles M.

1. For each installation i ∈ I find a set of mobiles Mi that i can (potentially)serve if all other installations are off.

2. Define the matrix A = (aij)i=1,...,|M|,j=1,...|I| by

aij =

{

1 if the i-th mobile is in Mj and,

0 otherwise.(4.7)

3. Solve the set covering problem

min∑

i∈I

xi

Ax ≥ 1 (4.8)

x ∈ {0, 1}|I|

4. In the set covering problem (4.8) each variable xi corresponds to an installationi ∈ I . Define

I ={

i ∈ I| xi = 1 in the optimal solution to (4.8)}

. (4.9)

5. Return I .

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Of course, we did not only try to use Greedy and Set Covering, but also well-known techniques like Tabu Search, Simulated Annealing or Evolution Algorithms.However, it turned out that all these algorithms are way too slow and therefore inap-plicable for large-scale, real-world instances of our problem.

4.3 Conclusions

Using the Set Covering algorithm together with the Greedy mobile assignment algo-rithm and I×I power assignment we are (and have been) able to determine solutionsof appropriate accuracy in relatively short (compared to the problem size) runningtimes.

The fact that we can determine good solutions in short time even for very large-scale real-world problem instances justifies the “Feedback Optimisation Scheme”that was presented in [13].

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5 Planning Results

We now discuss the effectiveness of our automatic planning approach on the MO-MENTUM planning scenarios [35].

5.1 Coverage and Capacity Shortages

As already indicated in Section 3.1, the optimisation model is very sensitive to thebalance between traffic demand on one side and offered site locations, allowed di-rections, and possible tilts and heights on the other. In some scenarios the supply ofinfrastructure did not suffice to serve all the traffic.

5.1.1 Detecting Shortages

Recall that we have two objectives in radio network planning: providing coverageand providing capacity. Achieving both of these goals depends on a sufficient supplyof potential sites as specified within the scenario. We found means to easily identifyareas where this is not the case, i. e., demand and possibilities do not match. Wecomputed a minimum obtainable pathloss map. To do this, for each pixel the bestpathloss that is obtainable allowing every possible antenna azimuth for every possiblelocation is computed. If in the resulting map the pathloss for a pixel is too high toallow coverage even using the maximum pilot power, it is clear that no solution,i. e., possible network configuration, exists that covers this pixel. Since this maprecords the best possible value for each pixel, we know that any configuration has todo worse. So the maximum possible pathloss map is a valuable tool to detect areaswhich cannot be covered given the current possibilities, thus indicating the need foradditional locations.

To analyse capacity is a bit more involved. In practice we used the network con-figurations provided by the operators for the scenarios, but any “sane” configuration,using all possible sites and suitable tilts would do. Using this configuration we com-pute the uplink and downlink load for the cells, based on the average traffic grid. Ifthis load is exceedingly high, it is obvious that it is impossible to handle the trafficwithout additional cells. Since we used all possible locations and fully populatedthem, again additional sites are needed. Typical examples for this problem are theLisbon and Berlin (public) scenarios.

However, it is not feasible to add new sites to the scenario during the automaticplanning process. The availability of site locations is subject to conditions and restric-tions outside the scope of WP-. For each additional site, a set of available antennalocations would have to be provided, each antenna location having a distinct isotropicpropagation grid on a selection of heights. These propagation grids would then haveto be generated using all available building and terrain information. (The genera-tion of path loss grids for the public scenarios on the basis of the MOMENTUM pathloss predictor [33] would have been an option. This has not been pursued within theproject.)

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5.1.2 Scenarios with Shortages

Lisbon Public. Evaluation by WP- of the network configuration provided by theoperator for the Lisbon scenario resulted in 46% missed traffic overall (by missedtraffic we refer to traffic that is left unserved because either outage or dropping oc-curred). As can be seen in Figure 5-1, this reaches values over 70% at some sites.

Since all possible locations were used, there is not much room for improvement.It is definitely not possible to find an acceptable configuration with the locationsprovided. We made some limited tries to mend this situation by scaling the trafficdata. This was not very successful: either the traffic was scaled uniformly, leadingto large areas nearly void of traffic, or the traffic was scaled selectively in the hightraffic areas, which lead to an levelling of the areas which more or less rendered themobility model useless. Discussions with the operators made clear, that the onlycorrect or “real” solution would be to add more potential sites. This was not possibledue to lack of time and man-hours.

While of course a “tuned” network might give better results, in practice there isno difference between 46% missed traffic and 30% missed traffic since the networkis useless to the customer either way. For these reasons the results of automaticplanning are indicator maps that show overloaded areas. Comparisons showed, thatthese resembled the results obtained by the WP- static simulator very closely andresulted in pictures similar to Figure 5-1(b).

(a) Total blocking (b) Downlink load

Figure 5-1: WP- simulation results for Lisbon public scenario

Berlin Public. This scenario suffers mostly the same problems as the Lisbon publicscenario. The overall missed traffic as computed by the WP- static simulator isabout 43%, reaching over 75% in certain areas (see Figure 5-2). Since much betterdata is available for the reference scenario, especially regarding the predictions, weconcentrated our efforts on the Berlin reference scenario.

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(a) Total blocking (b) Downlink load

Figure 5-2: WP- simulation results for Berlin public scenario

5.2 Optimisation Results

In two cases we were able to perform automatic planning based on mathematicaloptimisation techniques, see Section 4. The scales for the following maps on theoptimisation results are shown in Figure 5-3.

(a) Load maps (in %) (b) Coverage maps (in dB)

Figure 5-3: Scales for result maps

5.2.1 The Hague

Analysis of Input Data. Our analysis of the data lead to the conclusion, that thepredictions of the The Hague public scenario with their 50 m resolution without build-ing data are too coarse to allow for reliable automatic planning. This is shown inFigure 5-4: the three initial sectors of a three-sectorised site 5-4(a) (at height 18.0m,electrical downtilt 4◦) are rotated by 60◦ in 5-4(b), and tilted down to 8◦ in 5-4(c).

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These changes exploit all degrees of freedom for network planning, yet they haveonly a small effect, especially regarding the core cell area (center).

(a) Three-sectorised site, (height 18m, 4◦

electrical downtilt)(b) Same site with all antennas rotated by60◦

(c) Same site with all antennas tilted downanother 4◦

(d)Scale indB

Figure 5-4: Effects of configuration changes on propagation grids, The Hague sce-nario. The black dots indicate other potential sites

MIP Approach. The traffic load for the The Hague public scenario is reasonable.It is also the only scenario with an extended number of possible site locations. Weautomatically computed the solution shown in Figure 5-5.

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(a) Best server map (b) Coverage map

(c) Load map uplink (d) Load map downlink

Figure 5-5: Planning result for The Hague (Figure 5-3 for scales and units)

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5 Snapshots 10 snapshots 20 snapshots

time 73 s 1435 s 4283 s

Table 5-1: Running time of the Set-Covering Algorithm on an “The Hague” probleminstance with 912 installations and 76 sites on a 1 GHz Pentium III.

Heuristics

Computation Time. Table 5-1 shows the computation time of the Set-Coveringalgorithm for a “The Hague” problem instance with 76 sites and 912 installationson a 1 GHz Pentium III CPU. The time displayed in the table does not include theinitialisation time that is required for reading in the problem data and setting up basicinternal data structures. This time is within the margin of several seconds for small (5snapshots) problem instances and in the range of up to ten minutes for large probleminstances (20 - 30 snapshots). However, the initialisation time of the Set-CoveringAlgorithm is still smaller than the time required to generate the MIP problem, writeto disk and read it into some MIP-solver such as CPLEX.

On the one hand, the table shows that we need at most a little more than one hourin order to solve a problem with 20 snapshots. That is, the algorithm does performwell, even if the number of users/snapshots becomes big. On the other hand, thenumbers in the table make clear that computation time does not grow linearly withthe number of snapshots. This can be best seen when we compare the running timesof the 5 snapshots (73 seconds) and the 10 snapshots (almost half an hour) instances.This dramatic increase in the computation time is mainly caused by the MIP-solver(in our case CPLEX) that is applied to solve the set-covering problem (4.8): it isknown that these problems are NP hard and therefore the solution time will becomeunreasonably long for larger instances.

Observations. Basically, the solutions produced by the Set-Covering algorithmwere of good quality for “The Hague” scenario: the missed traffic in a static simula-tor is below 3%. However, when we took a closer look at the solution we found thatthere were a lot of selected sites on which only one installation was planted. From apractical point of view this is not desirable. In order to circumvent this problem weadded the following constraint to the set-covering problem (4.8)

As soon as a site is selected, at least 2 (or 3) installations must be putthere.

For convenience we call this the “min-2” (or “min-3”) constraint.It turned out that this modified set-covering problem is much harder to solve

(at least for “The Hague” instances): the time required for solving (4.8) with thisadditional constraint was more than two times as high as for the original problem.Nevertheless, using the additional min-2 or min-3 constraints we managed to generatesolutions for “The Hague” that had a reasonable number of installations on each site.But when we analysed these new solutions, we found that they were not better thanthe solutions where we had only one installation on most of the selected sites.

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5.2.2 Berlin Reference

This was the biggest and most sophisticated scenario, featuring path loss predictionswith a grid resolution of 5 m. Due to the huge amount of data, we focused on theAlexanderplatz area. The amount of traffic in this scenario is again too high to hopefor a satisfactory solution using the offered sites. Therefore we used an alternativetraffic grid, called Tg2, that has reasonable traffic load.

However, it is not demand alone which is responsible for missed traffic in thisscenario. We computed a minimum obtainable pathloss map that shows the bestpathloss value obtainable for each pixel. Looking at Figure 5-6, the red regions arethose which will have no coverage, regardless of the network configuration. The yel-low areas have a high probability of being problematic at best. Again, the correctsolution would be to set up additional possible site locations in or near the problem-atic areas.

Figure 5-6: Minimum obtainable pathloss for Berlin Alexanderplatz (Figure 5-3 forcolour coding); c© Digital Building Model Berlin (2002), E-Plus Mobilfunk GmbH& Co. KG

We automatically computed a network configuration using the techniques de-scribed in Section 4.1. This resulted in a configuration, using 16 out of 23 possiblesites and employing a total of 46 cells. According to WP- simulations, a missedtraffic below 0.7% can be expected. Best server, coverage and load maps are shownin Figure 5-7.

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(a) Best server map (b) Coverage map

(c) Load map uplink (d) Load map downlink

Figure 5-7: Planning result for Berlin, reference scenario, area around Alexander-platz, Tg2 traffic (Figure 5-3 for scales and units); c© Digital Building Model Berlin(2002), E-Plus Mobilfunk GmbH & Co. KG

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5.3 Assessing the Optimisation Approach

One outcome of our investigations into automatic planning methods for UMTS radionetwork planning was that a fine-grained analysis of the input data is indispensable.We had to spend significant effort on identifying zones in the scenarios that haveinsatisfiable demands or no possibility for coverage due to insufficient pilot penetra-tion.

In those cases where it was possible, we managed to find networks that performwell in static simulations and are cost-effective at the same time. This proves that ourapproach and methods are well suited to take on the UMTS network planning task.

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6 Conclusion

In this document the results of WP- on automatic RF planning of large-scale UMTSnetworks have been summarised. The approach developed within this project coversall major aspects of automatic planning. This includes propagation aspects, a rea-sonable mathematical model of the UMTS radio network, a couple of sophisticatedheuristic methods to short-cut the enormous amount calculations and mathematicaloptimisation methods.

Without accurate propagation prediction models automatic radio planning is notpossible. In order to enable the fully automatic generation of accurate predictions forall different operational environments, a general framework for an adaptive prop-agation model has been developed. This framework has been implemented basedon modules available within MOMENTUM and tested on the German reference sce-narios. The model is able to process digital terrain data of different resolution andgranularity and contains methods and criteria for unsupervised selection of propermethods for the different operational environments. This model has not only beenused within WP-, but has also been a basis for the simulations in other work pack-ages in MOMENTUM.

In order to be able to handle a complex system like a UMTS radio network, amathematical model for feasible network configurations has been developed. Thismodel has been used for analytical studies of structure of the optimisation problemas well as a starting point to develop the optimisation methods. Two principal ap-proaches for the optimisation problem have been developed. The mixed-integer pro-gramming approach using simplifications is applied in a pre-processing step, whereasthe second approach contains heuristics for both mobile assignment and installa-tion selection. For the heuristics different methods have been tested revealing TabuSearch, Greedy and Set Covering as the most promising approaches. All the differentcomponents have been integrated into an UMTS radio network optimisation softwareprototype. The rough flowchart of the automatic optimisation process is described inFigure 6-1. First computational experiments have shown that running times of lessthan one hour for realistic network scenarios can be achieved.

radionetwork

lack of capacity

lack of coverage

demandsnapshots

automaticplanning

network evaluation

capacityantennaconfigurations

pathloss

resources

QoSrequirement

Figure 6-1: Components of automatic optimisation

The automatic planning process produces optimised configurations if the avail-

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able infracstructure offered to the optimisation process is able to meet the trafficdemands. This is the case for the “The Hague” public scenario and the “Berlin” ref-erence scenario. If, however, this condition is not fulfilled, a detailed analysis (usingthe methods develeoped by WP- or WP-) gives hints concerning areas, where a lackof coverage or capacity is observed. In this case additional sites are required. Thissituation has been observed with the public scenarios for “Berlin” and “Lisbon”.

Despite the fact that quite a lot of simplifications and approximations have beennecessary both for the description of input parameters and for the optimisation modelitself, first performance evaluations of networks designed using WP- methods areencouraging. Solutions of high quality and low cost within short running times havebeen achieved. Although the proposed methods are able to generate reasonable goodresults there is still room for further improvement. Especially more advanced meth-ods for installation rating, which have not been investigated in all details, may be apromising starting point.

As a final remark, it has to be mentioned, that the finalisation of this project coin-cides with the period, where most UMTS operators in Europe are switching their firstnetworks revealing most probably the urgent need for methods as described in thisdocument. This situation will generate quite a lot of excellent opportunities to trans-fer the theoretical know-how accumulated in MOMENTUM WP- to the real practicalworld.

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