wcdma rnp data analysis of propagation model tuning guidance-20040719-a-2.2

WCDMA RNP Data Analysis of Propagation Model tuning Guidance For internal use only

Document code Product name

Target readers Product version V100R001

Edited byDocument

versionWCDMA RNP

WCDMA RNP Data Analysis of Propagation

Model Tuning Guidance

For internal use only

Prepared by URNP-SANA Date 2003-04-28Reviewed by DateReviewed by DateApproved by Date

Huawei Technologies Co., Ltd.

All rights reserved

2003-04-28 All rights reserved of 37


Revision Record

DateRevision version

Description Author

2002/09/11 1.0 Initial transmittal Liu Yong

2002/10/23 1.1 Revision based on the review comments Liu Yong

2003/04/28 2.0 A description on dual-slope model tuning and K7 tuning methods is added in this document; for problems found in application, relevant supplement and revision are made.

Liu Yong

2003/09/18 2.1 The propagation model tuning methods are calibrated. The default value of clutter offset is completely adopted without change; for medium-sized cities, the values of K1 to K7 are tuned according to the COST 231-Hata formula as f=2000MHz; default values of clutter offest are tuned; and for the time to perform K7 tuning, a description is given. Revision and addition of the contents made this time are expressed in blue.

Wang Shengyou

2003/11/13 2.2 The guide to use the data processing tool is added to the part of CW measuremnt data processing; A new section, Section 7, is added to describe how to use the tool of propagation model tuning from Aircom.

Wang Shengyou



Table of Contents

1 Overview..................................................................................................................................62 Propagation Model Tuning Principles......................................................................................63 CW Test Data Processing.......................................................................................................83.1 Data Filtering......................................................................................................................83.2 Data Dispersion...............................................................................................................103.3 Geographical Averaging..................................................................................................113.4 Format Conversion..........................................................................................................12

4 Header File Compilation........................................................................................................135 Model Tuning.........................................................................................................................135.1 Model Setup.....................................................................................................................145.2 Data Import......................................................................................................................175.3 Map Calibration................................................................................................................185.4 Information Setting (info>)...............................................................................................185.5 Filter Setting (Options).....................................................................................................195.6 Parameter Tuning............................................................................................................21

5.6.1 Model Tuning Principle of Enterprise.........................................................................215.6.2 Tuning of K2...............................................................................................................215.6.3 Tuning of K1...............................................................................................................235.6.4 Tuning of K3 and K4..................................................................................................245.6.5 Tuning of K5 and K6..................................................................................................245.6.6 Tuning of K7...............................................................................................................255.6.7 Clutter Offset Tuning..................................................................................................255.6.8 Tuning Result Analysis..............................................................................................27

6 Dual-Slope Model Tuning Method.........................................................................................276.1 Model Setup.....................................................................................................................276.2 Tuning of K1, K2 and K1 (near), K2 (near)......................................................................286.3 Tuning of Other K Parameters.........................................................................................29

7 Automatic Model Calibration Utility........................................................................................307.1 Input.................................................................................................................................307.2 Main Interface..................................................................................................................307.3 Data Import......................................................................................................................327.4 Data Tuning.....................................................................................................................33

8 Notes......................................................................................................................................348.1 Model Tuning Method with Test Data based on Ec.........................................................348.2 Problems Concerning Find site Function.........................................................................348.3 Problems in BS Longitude and Latitude Format Conversion...........................................35



List of Figures

Figure 1 Proper data range schematic diagram............................................................................9Figure 2 Model tuning procedures..............................................................................................14Figure 3 Model setup...................................................................................................................15Figure 4 Default parameters.......................................................................................................15Figure 5 Selection of effective antenna height type....................................................................16Figure 6 Selection of knife-edge diffraction calculation method..................................................16Figure 7 Initial value settings of clutter parameters....................................................................17Figure 8 Model tuning operation interface...................................................................................17Figure 9 Antenna diagram import in the “info” window...............................................................18Figure 10 Filtering setting............................................................................................................20Figure 11 Clutter before filtering..................................................................................................20Figure 12 Clutter after filtering.....................................................................................................21Figure 13 Obtaining K2 value......................................................................................................23Figure 14 Obtaining of K1 value (Method 1)...............................................................................24Figure 15 Obtaining K1 value (Method 2)...................................................................................24Figure 16 An example of initial value of dual fold-line model......................................................28Figure 17 Distance filtering setting in the tuning of K1 (near) and K2 (near)..............................29Figure 18 Distance filtering setting in the tuning of K1 and K2...................................................29Figure 19 Main interface of automatic calibration utility..............................................................31Figure 20 Data import..................................................................................................................32Figure 21 Archive Viewer window...............................................................................................32Figure 22 Data tuning window.....................................................................................................33Figure 23 Clutter Offset values...................................................................................................33Figure 24 Test data points of various Clutters and Mean Error values before the tuning..........34Figure 25 Find site prompt information.......................................................................................34Figure 26 Values of the BS longitude and latitude expressed in degrees/minutes/seconds when being imported...........................................................................................................................35

Figure 27 Error values of the imported BS longitude and latitude after conversion....................36Figure 28 Manual modification of the imported longitude and latitude.......................................36



WCDMA RNP Data Analysis of Propagation Model Tuning Guidance

Key words: WCDMA, model tuning, CW test (continuous wave test), dispersion, geographical

averaging

Abstract: This document describes the method and procedures of data processing of CW

measurement and propagation model tuning, which are applied to obtain an accurate

electromagnetic propagation model to achieve reliable network planning before

WCDMA network construction. And it also describes the principles that must be

followed to ensure accurate and reliable tuning.

List of abbreviations:

Abbreviation Full spelling



1 Overview

The propagation model is the basis for cell planning of a mobile communication network.

The accurate propagation model can make cell planning reliable, and it also can help operator

meet users’ requirements with economical investment. Therefore, in order to obtain a radio

propagation model of the actual local environment and to improve the accuracy of coverage

prediction, propagation model tuning is of great significance. This document gives a description

of propagation model tuning methods and CW measurement data process, and the principles to

be followed to achieve accurate tuning.

2 Propagation Model Tuning Principles

The studies on the propagation model can be classified into two kinds: one is the theoretical

analysis method based on the radio propagation theory; and the other is the empirical method

based on large amount of measurement and experienced formulae. In the mobile communication

system, since MS is moving continually, the propagation channels are influenced not only by

Doppler Effect, but also by terrains and clutters. In addition, the interferences from the mobile

system itself and outside should not be ignored. Based on the above properties of the mobile

communication system, it is very difficult to realize strict theoretical analysis. Usually, it is

required to simulate and simplify the propagation environment, but this leads to big errors. So,

we generally use the statistical model. The most famous statistical model is the Okumura model.

It is the propagation model expressed with curves based on large amount of measurement by

Okumura in Japan. Based on the Okumura Model, a kind of regression method can be used to

make some analytic experienced formulae suitable for computer calculation. These experienced

formulae include the Okumura-Hata Formula applied to GSM900 macrocells and the Hata

formula extension applied to GSM1800. In addition, there are the Walfisch Formula applied to

microcells and the Keenan-Motley Formula applied to indoor propagation environments. These

experienced formulae are calculation-intensive and have more or less errors with actual

environments. Therefore, in the practical prediction of the field strength, the calibrated Okumura-

Hata Model is usually taken as a prediction model, and certain planning software is used to tune

the above-mentioned formulae by importing the CW test data of the local radio environment.

We usually use the Enterprise planning software of Aircom Company for model tuning.

Enterprise supports many propagation models for cell planning in various environments. These

propagation models include:

1. Standard MacroCell Model

2. Standard MacroCell Model 2

3. Standard MicroCell

4. WaveSight Model from Wavecall



These four models are all statistical models derived from the Okumura-Hata Model. This

document introduces the most common Standard MacroCell Model.

“Standard MacroCell” propagation model is based on the Hata Model of ETS and is added

with some extra capabilities. Thus the flexibility and accuracy of the model are improved. This

model is applied to the frequency range from 150MHz to 2GHz. The model is dedicated to

MacroCell design with the dual-slope algorithm.

The following are the common formulae:

Prx = Ptx - Ploss

Where,

Prx = Received power (dBm)

Ptx = Transmit power (EiRP) (dBm)

Ploss = Path loss (dB)

And,

Ploss = K1 + K2log(d) + K3(Hms) + K4log(Hms) + K5log(Heff) + K6log(Heff)log(d) + K7diffn

+ Clutter_Loss

Where,

d refers to the distance between the base station (BS) and the mobile station (MS), in kms.

Hms refers to the height of the MS to the ground (m). This value can be specified as the

general value, or specific to a certain clutter.

Heff refers to the effective height of the antenna of BS (m).

Diffn refers to the diffraction loss calculated with the equivalent knife-edge diffraction

methods, such as Epstein, Peterson, Deygout or Bullington.

K1 and K2 refer to the intercept and the slope. These factors correspond to a fixed offset

and the increment factor of the log value of the distance between BS and MS.

K3 refers to the height factor of the MS antenna, which is used to correct the influence

imposed by the effective antenna height of the MS.

K4 refers to the increment factor of the Okumura Hata Model of Hms.

K5 refers to the gain of the effective antenna height of BS. It is the increment factor of the

log value of effective antenna height.

K6 refers to the coefficient of Log (Heff)Log(d), which is the increment factor of

log(Heff)log(d) value of Okumura Hata type.

K7 refers to diffraction coefficient, which is the increment factor of the diffraction calculation.

The diffraction methods are optional for users.

Clutter_Loss refers to the clutter specification, for instance, the height and interval must be

taken into consideration during calculation.

With the increment coefficient which can be defined by users, the propagation model can be

entirely customized. Dual fold-line model supports two coefficients k1 and K2, and the variable



point that can be defined by users. The model also adopts various diffraction loss algorithms and

effective BS height algorithm.

3 CW Test Data Processing

Reliable CW test data is the basis for propagation model tuning, and it is also the first step of

input. The reliability of CW test data imposes direct influence on the accuracy of tuning. For

details on how to obtain reliable CW test data, please refer to CW Measurement Guide.

However, in spite of rational design of the measurement, the obtained data are not perfect; and

further processing will be necessary. Usually, there are four steps: data filtering, data dispersion,

geographical averaging and format conversion.

3.1 Data Filtering

During the actual test process, it is inevitable that some of test data do not meet the

requirements of the model tuning. In order to avoid adverse influences by these data on the

model tuning, it is necessary to filter these data off. The data that need to be filtered off include:

1. Data collected in the places where GPS information is unavailable, for instance, under the

viaducts, in the tunnels.

Because it is necessary to know the exact position of each sample point in model tuning, the

data collected in the places where GPS information is unavailable should be filtered off. Such

cases include:

1) Under viaducts

2) Tunnels

3) Narrow streets with high-rises on both sides

4) Narrow streets with dense foliages above

5) Others

These data should be marked during the test, and the test engineers should make

comments in the test data documents.

2. Data collected in the place close to or far away from the antenna

Because the purpose of propagation model tuning is to obtain appropriate propagation

model to guide cell planning, the expected model is related to the antenna height and the cell

radius. The antenna height and the measurement range should be selected properly in the CW

tests. However, limited by test routes and with the margin reserved, the test range is generally

larger than the ideal one, so the points located beyond the ideal range should be filtered off in

model tuning. Usually, we take the range from 0.1R to 2R away from the antenna as a proper

range, where R refers to the cell radius, as shown in the following figure:



Figure 1 Proper data range schematic diagram

The reason we select this range is that the signal strength has not strictly linear distributed

with the propagation distance. In actual environments, there are many LOS paths in the area

near the antenna, and the signal strength is good; but there are few LOS paths in the area far

away from the antenna, and the signal strength is bad. So the farther the data is collected from

the antenna, the less proportion the LOS will possess, and the larger the propagation loss will be;

the nearer the data is collected from the antenna, the more proportion the LOS will possess, and

the smaller the propagation loss will be. So this range is related to the cell radius. After careful

consideration, 2R is regarded as the most rational. The data collected beyond the range of 2R

should be filtered off. So should the data collected within the range of 0.1R, which is attributed to

the following reasons: it is too close to the antenna in this range and will be influenced greatly by

the pattern of the antenna vertical plane; there are few test data, and it is difficult to distribute

them evenly within this range because of the route. So they should be filtered off.

3. Data of small signal

In some cases, the signal is of too weak strength to be reliable because the strength of the

signal is close to the decoding threshold of the receiver and the decoding is liable to be

influenced by instant fluctuation. Take E7476A Receiver supplied by Agilent for example, its

noise figure is about 8dB, and RBW = 8kHz, so the noise floor of the receiver is

KTW+NF=KTw+10lg(RBW)+NF=-174dBm + 39dB +8dB = -127dBm. Generally, it is reasonable

for the lowest level to be 6dB higher than the noise floor (in this case, the influence of noise floor

over the test result is less than 1dB), that is, -121dBm. Data that are less than -121dBm should

be filtered off. So do signals in the deep fading and an optimistic model will be made. In order to

avoid such a case, we can use this method: Take the base station as the center, make

concentric circles with the same spacing (100m is recommended), and then count the test points



in each circle. If the number of the test points with the signal strength smaller than -121dBm are

more than 20% of the total points in the circle, the data on and outside this circle will be

excluded.

4. Incorrect data caused by inaccurate antenna pattern

If a CW test is carried out with directional antenna, as the antenna patterns described in the

specifications is more or less different with those of exact antenna, especially in the side lobe

and the back lobe, the data in the directions other than the direction of the main lobe should also

be filtered off. It is recommended to take boundaries at 60 degrees on both sides of the main

lobe, and filter the data out of this range. In addition, while omni-antenna is used in the test, if it is

installed at one side of the building and blocked by the building, the data on the other side which

is sheltered by the building should also be filtered off.

5. Other data on the route segments specified as the ones disagreeing with the requirement

at CW test route design process

In order to keep the objectivity of collected data as much as possible, we usually filter some

data out according to some rules set in advance, like the filtering methods above; but it is not

applied to the test results. However, as the tuned model will be applied to areas with similar

environment characters, we hope that the generality of such environment is kept and the

specialty of the tested area is eliminated. Thus, we should filter some improper data out, with the

premise that the improperness of the data has been predicted before the test, and they are

included in the test process because of the difficulty in operation or route selection. Such data

include:

1) For the paths passing by the base station and stretching along the direction of signal

transmission, the data collected in these paths are more optimistic than the adjacent data

because there are many LOSs and it is likely to produce waveguide effect. The data should be

filtered out.

2) On viaducts. A viaduct is usually higher than the surrounding buildings and there are

many LOSs, so the collected data more optimistic and should be filtered off.

3) Huge bridges. The reason is similar with that of the viaduct.

4) Others.

These data must be predicted and marked upon the route design before CW test. The test

engineers should make description about it in the measurement document; otherwise, these data

cannot be filtered off.

The filtering of Types 1 and 5 should be finished before data dispersion, and filtering of the

Types 2, 3 and 4 should be finished after geographical averaging.

3.2 Data Dispersion

Analyzed with stochastic theory, the signal propagation in the mobile communication can be

expressed as follows:



r(x) = m(x)r0(x)

Where, x refers to the distance; r(x) refers to the receiving signal; r0(x) refers to Rayleigh

fading; m(x) refers to the local mean value which is the combination of the slow fading and space

propagation loss. It can be expressed as:

Lx

Lx

dyyrL

xm )(2

1)(

Where, 2L refers to the average length of sampling interval, which is also called intrinsic

length.

The purpose of CW tests is to obtain the local mean value of every geographical position in

an area as much as possible, so as to minimize the difference between r(x) and m(x). Therefore,

Rayleigh fading’s influence must be excluded to obtain the local mean value. Upon the averaging

of a group of test data, if the intrinsic length is too short, the Rayleigh fading’s influence still

exists; if 2L is too long, the normal fading will be averaged. According to Lee’s Theorem, when

the intrinsic length is 40 wavelengths and 50 points are sampled, the difference between the test

data and the local mean value can be less than 1dB.

Therefore, the intrinsic length is the geographical averaging length. At the 2G frequency

band, the transmission wavelength is 0.15m, and it is 6m for 40 wavelengths. That is, we should

make an averaging every 6m. However, in the data test, only one point can be located because

GPS locating speed is too slow. If the vehicle speed is 50km/h, a point can be located in each

14m. It is obvious that the geographical averaging cannot be directly carried out, so the

dispersion processing should be performed before the geographical averaging.

The dispersion processing is as follows:

For the collected CW test data, because the receiving speed of the receiver is far greater

than the locating speed of GPS, many measurement records are listed with same location

information (in the same longitude and latitude) and in the sequence of time. Suppose that the

vehicle is at uniform speed between each two locating points, and the time interval of each two

measurement records is the same (this can be satisfied within the allowed error range), these

measurement records can be distributed to the route segment between two points evenly at the

time sequence, thus there will be enough points within each 6m in the test route.

For the existing E7476A drive test equipment, its receiving speed can be about 180 points

per second. According to Lee’s Theorem, if the vehicle speed is 50km/h , 117 points should be

measured each second. It is obvious that the existing receivers can meet the speed requirement.

If the receiver cannot meet the collection speed requirement, the influence of fast fading cannot

be excluded effectively even if the averaging is done.



3.3 Geographical Averaging

Geographical Averaging can be performed after data dispersion. The purpose is to eliminate

the influence of fast fading and keep that of slow fading. The averaging range is the intrinsic

length, and there are two averaging methods:

1 Divide the entire area into grids, with the grid side length being 6m, and make arithmetic

averaging of the data in each grid, and then take the grid center as the new position point;

2 Since data are collected by drive test, their positions are basically in a line. Divide the path

into segments at the interval of 6m. Make arithmetic average of the data in each segment and

select points with same interval as the locations of mean value. Method 1 is simple and quick,

but it cannot ensure that the intrinsic length is 6m, which would vary from 6m through 8.5m.

Method 2 it is complicated and slow in calculation though it can ensure that the intrinsic length is

6m. Method 2 is recommended.

3.4 Format Conversion

The data format exported by Agilent E74xx Series is different from the required one

supported by the Enterprise, so it is necessary to convert the format. The format of the CW test

data exported by Agilent E74xx Series is as follows:

"X" "Y" "CW_Power_List__Freq__Hz_" "CW_Power_List__Ampl__dBm_" "Time"

"Date"

117.0017237161 36.6635851496 2140000000.0000000000 -61.7812500000

"14:58:59.08" "02-7-18"

117.0017237161 36.6635851496 2140000000.0000000000 -64.9062500000

"14:58:59.14" "02-7-18"

117.0017237161 36.6635851496 2140000000.0000000000 -62.9531250000

"14:58:59.25" "02-7-18"

117.0017237161 36.6635851496 2140000000.0000000000 -68.3437500000

"14:58:59.36" "02-7-18"

While the data format required in Enterprise is as follows:

117.0017237161 36.6635851496 -61.7812500000

117.0017237161 36.6635851496 -64.9062500000

117.0017237161 36.6635851496 -62.9531250000

117.0017237161 36.6635851496 -68.3437500000

Where, the first column corresponds to "X", the second column corresponds to "Y" and the

third column corresponds to "CW_Power_List_Ampl_dBm". So the following three operations

should be carried out: 1) Keep the above three columns, and delete other columns manually. 2)

Delete the title bar. 3) Save the file as a *.dat file.



At present, we can complete data dispersion, geographical averaging and data filtering of

Types 1, 2, 3, 4 and 5 by using the dedicated CW data analysis tool CW Data Editor. The data

filtering of Type 2 can be completed with Enterprise. For the usage of the CW data analysis tool

CW Data Editor, please refer to the document WCDMA RNP CW Data Processing Tool Usage

Guide.

4 Header File Compilation

After the CW test data processing is completed, we can use Enterprise to carry out the

model tuning. Enterprise need to read a header file (*.hd) for importing test data and antenna

information, so the header file should be created first. The header file format is as follows:

A default example of header file

The parameters to be given with accurate information include: DATA_FILENAME (CW test

data filename to be imported), SITE_LONGITUDE, SITE_LATITUDE (the antenna installation

longitude and latitude), TX_HEIGHT (antenna height), TX_POWER (transmission power at the

antenna) and ANTENNA_TYPE (antenna type); others are all description text information

(according to the test, FEEDER_TYPE, FEEDER_LENGTH and CONNECTOR_LOSS are all

invalid).

Note that the header file and CW test data files should be put in the same directory and

should be of the same filename (with different suffix); otherwise they cannot be imported.

5 Model Tuning

After composing the header file, import it to Enterprise fort model tuning. Enterprise is a

powerful planning and optimization software tool, the model tuning is only one of function

modules. Some preparations should be made before the model tuning.

First, create a project. In Enterprise, all the works about planning, optimization and model

tuning are carried out on the base of each project. For the detailed operation procedures for

project creation, refer to Reference [2]. Note that WGS84 Ellipsoid and UTM projection must be

selected when the digital map is imported, otherwise the map should be transferred. For the

specific reason, refer to Reference [1].

After the project is created, it is necessary to import the pattern file of the antenna used in

CW test. Although omni-antenna is usually used in CW tests, we also adopt the directional

antenna to collect data sometimes. Even if the omni-antenna is adopted, the pattern also varies

with different manufacturers. Therefore, the accuracy of antenna pattern data becomes very

important. For the specific operation procedures for antenna file import, refer to Reference [2].

After all those preparations, import the data to carry out the model tuning. There is an



iteration process in model tuning. Here are the procedures of model tuning with Enterprise:

Figure 2 Model tuning procedures

5.1 Model Setup

First, set up a model of the standard macrocell to be tuned. Select the 3G module--

>Options-->Propagation Models Edits menu, and the window shown in Figure 3 appears. Click

<ADD> in “General” window and input the model name. Set the earth radius to 8493km, and set

other items according to the actual conditions. Set all the parameters from K1 to K7 in the “Path

Loss” window to the default values, as shown in Figure 4. Select relative in the “Eff.Ant.Height”

Window, select Epstein Peterson in the “Diffraction” window and fill 0m in the Item of Merge



Knife-edges close than. Then, set every value in the Clutter offset frame in the “Clutter”

window to 0 (in Model 1, Clutter offset is equivalent to Clutter Loss in the formula). By now, a

standard model setup is completed. The entire consequent tuning process is to tune the

parameters of this model, so as to make it compatible to the tested radio propagation

environment to the best.

Figure 3 Model setup

Figure 4 Default parameters2003-04-28 All rights reserved of 37


Figure 5 Selection of effective antenna height type

Figure 6 Selection of knife-edge diffraction calculation method



Figure 7 Initial value settings of clutter parameters

5.2 Data Import

Select the 3G module-->Tools -->CW Measurements menu, the window as shown in

Figure 8 appears. Click <ADD>, find the directory of the *.hd file, and then select the *.hd file and

open it. Then the CW test data and the antenna information included in the header file will be

automatically imported to Enterprise. (Note that CW test data and the header file should be in the

same directory.)

Figure 8 Model tuning operation interface



5.3 Map Calibration

For GPS location in CW tests, we usually select WGS84 coordinate and UTM projection. As

mentioned above, the digital map is imported through the WGs84 coordiante and UTM

projection. But in some countries, such as China, the digital map (especially the digital maps

supplied by the State Geography Information Center) is usually imported without WGS84

coordinte or UTM projection, and this makes discrepancy between the test data and the digital

map. In addition, the test data and the digital map may not match to each other because of the

error of the map itself. So we need to carry out the map calibration. A tuning method is to

calibrate four parameters of the rectangular coordinates of the digital map (that is, four

parameters in the file index.txt), so as to make it match to the test data to the best. This is an

iterative process of revision-proving-revision. For the specific theory and methods, refer to

Longitude and latitude Data Conversion between WGS1984 Coordinate System and “Beijing54”

Coordinate System (V1.0).

5.4 Information Setting (info>)

The info> Item in the window in Figure 8 shows the information of all the parameters of the

imported header file. You can check whether the information is right. If not, you can correct it.

Note that we do not know the exact filename of antenna pattern when we prepare the header file,

so we fill unknown in the item of ANTENNA_TYPE and then change it in the item of info later.

The imported antenna file will be automatically indexed here for selection, and so does the item

of FEEDER_TYPE, as shown in Figure 9. After the change is completed, use the SAVE menu to

save the changed information into the header file.

Figure 9 Antenna diagram import in the “info” window

The value of EiRP should be emphasized here. The EiRP value in item of info> is the 2003-04-28 All rights reserved of 37


TX_POWER value in the header file. This parameter is defined at the connection port of

transmission antenna, the antenna gain and the feeder loss of the receiving end should be

considered when its value is calculated. The calculation formula is as follows:

EiRP=Ptx-Floss1+Gtx+Grx-Floss2.

Where, Ptx refers to the output power of the signal source (after the power amplifier),

Floss1 refers to the feeder loss of the transmission end, Gtx refers to transmission antenna gain,

Grx refers to the receiving antenna gain and Floss2 refers to the feeder loss of the receiving

end.

5.5 Filter Setting (Options)

There are two tabs in the window of Options: “Model” and “Filter”. The “Model” window is

used to select the model to be tuned and the map resolution, and the “Filter” window is used to

set the filtering criteria. Enterprise provides some data filtering functions, including Clutter

filtering, distance filtering and signal strength filtering. The principles of the filtering based on

distance and the signal strength have been described above in the section of Data Processing,

so they will not be repeated here. Only the filtering based on Clutter will be described here. It is

the destination weighting method that the Standard MacroCell Model uses. That is, when the

influences of various Clutters on propagation loss are calculated, only the test points in this kind

of Clutter should be calculated for evaluation, without the consideration of the influences of

Clutter in the propagation paths to these points. Therefore, in order to estimate the influences of

the Clutter on the propagation accurately, there must be enough points as the experiences

required. We think that there must be 300 to 400 sample points for each clutter type to be

calibrated, so as to ensure the effective tuning results, and the Clutters with insufficient points

should be filtered off. The filtering method is shown in the following figure:



Figure 10 Filtering setting

As shown in the above figure, the selected Clutter is the filtered off; and data of r<150m or

r>3000m is filtered off; and data of Signal>-40dBm or Signal<-121dBm is filtered off (Because

the path loss is usually bigger than 70dB, when the transmission power is 30dB, it is required to

set the data of Signal>-40dBm as the one to be filtered off.); the LOS data (visible) and the

NLOS data (invisible) should be the included as well. The Clutter that should be filtered off can

be obtained through the following analysis: set the appropriate filtering conditions about the

distance and the signal strength, and then enable the analysis function (by clicking the icon) to

obtain the number of test points (the column of Num.Bins) distributed in every Clutter, as shown

in Figure 11. After that, come back to “Filter” again to set Clutter filtering, and filter out the

Clutters with less than 300 points. Check them again with Analyse after the filtering. Figure 12

shows the result:.

Figure 11 Clutter before filtering



Figure 12 Clutter after filtering

5.6 Parameter Tuning

5.6.1 Model Tuning Principle of Enterprise

The basic model tuning principle of Enterprise is as follows:

Select a model first and set each parameter value of K1 to K7 (default), and then carry out

radio propagation estimation with this model. Compare the predicted value with the drive test

data to get the error between them. Tune parameters of this model according to the statistics

results of the differences. Repeat the tuning until the mean square errors of the predicted values

and the drive test data are acceptable, then each model parameter value worked out here is the

value we need.

During the fit analysis of the set model and the actual data, the following statistics analysis

values are used: Mean Error, RMS Error, Std.Dev.Error and Corr. Coeff. Where, Mean Error

refers to the statistical mean error of the predicted value and the collected value, RMS Error

refers to the root mean square error of the predicted value and the drive test data, Std.Dev Error

refers to the standard deviation error of the predicted value and drive test data, and Corr. Coeff.

is the correlation coefficient. The detailed meanings are as follows:

Suppose that is the predicted value of a certain test point, is the collected

value of this point; set = - , and = (N refers to the total number of

tested points), refers to Mean Error. Therefore, RMS Error= , Std.Dev.Error=

. It shows that RMS Error=Std.Dev.Error when is 0. So we can use

RMS Error and Std.Dev.Error to evaluate the tuned model. The smaller the values of RMS

Error and Std.Dev.Error, the closer the prediction results to the test result collected with this

model will be, indicating this model is suitable for the current radio propagation environment.

5.6.2 Tuning of K2

Since the change of each parameter value of K1 to K7 influence one another, we should



tune parameter K2 first because it imposes the greatest influence on others and has the closest

relation with the distance variance. The formula is transformed as follows:

Ploss = [K1 + K3(Hms) + K4log(Hms) + K5log(Heff) + K7diffn]+ [K2 + K6log(Heff) ] log(d) +

Clutter_Loss

If the digital map, test data and antenna information are specified, [K1 +K3(Hms) +

K4log(Hms) + K5log(Heff) + K7diffn ] can be taken as a constant. Set Clutter_Loss to 0, Ploss

will be in the linear proportion to log(d). Set up the coordinate system with the logarithm value of

the distance as the horizontal axis and the signal strength as the longitudinal axis. Distribute the

test data to this coordinate system and perform line fit of the data. The obtained line slope is

K2+K6log(Heff), which minus K6log(Heff) leaves K2. But K6log(Heff) is not a constant, Heff is

the relative height between BS transmission antenna and the MS receiving antenna, and it varies

with the fluctuation of the terrain. That is, Heff of each point is different. You can obtain the

specific value of each point by reading the digital map. Therefore, the best way is: Suppose K6 is

fixed, detract K6log(Heff)log(d) from the signal strength of each point, and then carry out linear

fit. The slope worked out will be the most approximate value of K2.

The Enterprise works a little differently. It does not provide K2 value directly, but predicts the

value according to the model and detracts the actual test value from the predicted value to obtain

a difference, and then conduct the linear fit of all the differences. The line slope in the fit result is

the deviation error of K2+K6log(Heff) value. Suppose that K6log(Heff) is a reasonable value, the

deviation error is that of K2.

Tune K2 as follows: Click the Gragh> icon, and two analysis diagrams will appear: one is

Received Level vs log(distance), the other is Error.vs log(distance). Gradient in the diagram

of Received Level vs log(distance) is K2+K6log(Heff), and Gradient in Error.vs

log(distance) reflects the deviation error of tuning value and original value of K2. The tuning

value of K2 is obtained by adding the preset K2 value to Gradient in Error.vs log(distance) in

the “Propagation Models” window.



Figure 13 Obtaining K2 value

5.6.3 Tuning of K1

Upon the tuning of K2, check it through Gragh>. If Gradient in Error.vs log(distance) is 0,

it means that the value of K2 has been tuned, and intercept in the diagram is the deviation error

of K1 (Actually, it should be that of [K1 + K3(Hms) + K4log(Hms) + K5log(Heff) + K7diffn].

However, if it is supposed that [K3(Hms) + K4log(Hms) + K5log(Heff) + K7diffn] is a constant, it

will be that of K1). Add the value of Intercept to the original value of K1 to obtain the tuned value

of K1. This deviation error can also be obtained by means of the Analyse function (the opposite

number of the item Mean Error in the table is this deviation error value).



Figure 14 Obtaining of K1 value (Method 1)

Figure 15 Obtaining K1 value (Method 2)

5.6.4 Tuning of K3 and K4

K3 and K4 are related to the antenna height of the MS. As shown in Figure 3, when the

model default value is set, the default value of antenna height of the MS has been already

preset, generally, it is 1.5m. So K3(Hms) + K4log(Hms) is a constant. K3 and K4 need not be

tuned because their changes can be presented by K1. Keep the default parameter values to be -

2.93 and 0.00.

5.6.5 Tuning of K5 and K6

K5 and K6 are related to the antenna height of the BS. Since we usually select the Relative

(Site antenna height relative mobile height), indicating the effective height of the BS antenna is

the relative height between the antenna and the MS, Heff is a variable. The change of K5 cannot

be presented by K1 entirely, and neither do the change of K6 by K2 entirely. But the tests proves



that, when the terrain fluctuation is small and K5 and K6 change little (less than 10), the changes

of K5log(Heff) and K6log(Heff) can be regarded as constants. In this case, the influence caused

by the changes of K5 and K6 can be substituted by the changes of K1 and K2, so they do not

need tuning. Keep the default parameter values to be -13.82 and -6.55.

5.6.6 Tuning of K7

K7 refers to the diffraction coefficient, which represents the weight of the diffraction loss to

the full path loss. As the diffraction is valid only for the sample points on NLOS paths, and the

diffraction loss of the sample points on LOS paths is 0, the data on LOS path should be excluded

before tuning. There are three kinds of information closely related to the diffraction: 1) Building

height information, 2) Clutter height information and 3) Terrain height information. At present, the

digital maps we used are usually lack of accurate building height information, while the Clutter

height information is not used (set it when defining the model parameter), so K7 cannot be tuned

according to the former two kinds of information. If the terrain fluctuation is small, LOS/NLOS

judgment will be not so accurate. In this case, the default value of the parameter is kept to be 0

as recommended. If the terrain fluctuation is large, LOS/NLOS judgment will be relatively

accurate. It is necessary to carry out K7 tuning when there are lots of NLOS data (more than 300

points). The tuning method is as follows:

1. Filter LOS data out: Select the Options-->Filter-->NLOS menu, and cancel the check of

the LOS item.

2. Tune the value of K7 at the equal intervals with the step of ±0.05 until Std.Dev.Error

reaches its minimum value, which is the tuning result.

3. Since the tuning of K7 has influences on the values of K1 and K2, K1 and K2 should be

tuned after K7. The tuning method is as above, but they should be tuned together with LOS data.

In addition, if the tuned model is used for link budget, the value of K7 cannot be used as

there is no digital map for the link budget. Therefore, in order to avoid big error in the link budget

when the terrain fluctuation is great, the propagation model used in the link budget should be

obtained under the condition that the K7 value is 0. And thus the error should be considered in

the shadow fading headroom. That is, in the case that the terrain fluctuation is great, it is

preferable to respectively tune the propagation models used in simulation and in the link budget

to get different parameter values.

5.6.7 Clutter Offset Tuning

Due to the restriction of the drive test, most of the CW test data are collected with the clutter

type of “Open land in urban”. In the case of no other clutters interference, the clutter offset of

“Open land in urban” will be tuned to 0dB after normal model tuning. However, in actual

operation, due to such factors as map registration, the accuracy of the map and some narrow

streets, some points will be located in the clutter “Common building”. According to the current



tuning methods, if the number of points in a certain type of clutter exceeds the threshold (300

points), these clutters will obtain a calibrated Clutter offset value. In this case, Clutter offset of

“Open land in urban” will have a little offset away from 0dB, so that the weighted average value

of Clutter offset of all clutters will be 0.

It can be seen from the above analysis that the adjusted Clutter offset of the clutters other

than “Open land in urban”, mainly caused by map error, cannot be the reliable tuned value of this

clutter for the whole target area. Therefore, for the model tuning adopting the drive test data,

keep Clutter offset of all the clutters to be 0dB and carry out the tuning on the parameters

K1/K2/K7. Finally, set Clutter offset of the clutter “Open land in urban” to 0dB, and set Clutter

offset of other clutters according to the recommended default values to complete the model

tuning.

Here is the table of the recommended default value of Clutter offset of each clutter type:

Table 1 Recommended default value of Clutter offset of each clutter type

Clutter Offset(dB)

High buildings 18

Dense urban 7

Larger and lower

buildings

-0.5

Ordinary buildings 2

Parallel and lower

buildings

-0.5

Parks in urban 0

Open land in urban 0

Wet land -1

Villages -0.9

Towns in suburban -0.5

Other lower buildings -0.5

Open land in villages -1

Green land 0

Ocean areas -1

Inland waters -1

Forests 15

Finally, for the calculation of Clutter offset in the link budget, If all the clutter offset value of

of an area are calibrated after propagation model tuning and the clutter area of this area are

known, it is suggested to obtain the clutter offset value uniformly by weighting and averaging the



clutter offset values based on the area proportion of each type of clutter in this area.

5.6.8 Tuning Result Analysis

It is required to analyze the accuracy of the model after tuning. The model accuracy refers to

the extent of fitness of the tuned model and the actual test environment. The accuracy is usually

evaluated through the value of RMS Error. Generally, if RMS Error is less than 8, it means that

the tuned model fits the actual environment, that is, the tuning result of this model is accurate

and can be the reference for subsequent jobs like planning. If RMS Error is greater than 8, it

means that there is a big error between the tuned model and the actual environment and it

cannot be the reference. There are four causes: 1) An error occurs in the tuning process, for

instance, inaccurate antenna pattern data, import error of the antenna information, uncorrected

map and clutter and so on. 2) The subsequent data processing is not done well, thus lots of

ineffective data are not filtered off, or effective data have been filtered off instead. 3) The digital

map is inaccurate. 4) CW test design is not reasonable, which leads to ineffective test data.

Therefore, when RMS Error is greater than 8, it is required to check it according to the four

reason above and calibrated it again.

If the result of RMS Error>8 is not caused by the above four reasons, it is probably because

this model is not applicable, or the radio propagation environment of this area is too complicated

and the propagation condition changes a lot. In this case, it is required to perform a survey on the

actual environment.

6 Dual-Slope Model Tuning Method

Enterprise also provides a dual slope model for an accurate propagation model description

of some complicated areas. Dual-slope model is also called dual fold-line model. As its name

implies, it uses two set of different values of K1 and K2 according to different distances.

Correspondingly, the model mentioned previously is also called single fold-line model. Dual fold-

line model is applied in this condition: there is an obvious difference between the radio

propagation environment within a specific range and that out of this range with its center at the

site. To use this model accurately, use the tuning method described as follows:

6.1 Model Setup

The model tuning principle of the dual fold-line model is the same with that of the single fold-

line model. Set up a model first, and then use this model to carry out the prediction. Compare the

predicted value with the actual test value, and calibrate the model parameters according to the

comparison result until the predicted value is very close to the actual test value. The dual fold-

line model setup is as shown in the following figure, the difference between the dual fold-line

model and the single fold line model is that for the former, K1 (near) and K2 (near) are not 0, but



there is an initial value for each of them. This initial value can be the same as K1 and K2, and the

corresponding d is also not 0, but determined according to the characteristics of the propagation

environment. This value is corresponding to an boundary, that is, there is obvious difference

between radio propagation environments inside and outside the circle with the site as its center

and this distance as its radius.

Figure 16 An example of initial value of dual fold-line model

Note: In the above figure, d<1.2km is only an example, which should not be the reference.

The actual value should be determined according to the characters of the environment.

6.2 Tuning of K1, K2 and K1 (near), K2 (near)

According to the features of the dual fold-line model, the dual fold-line model can be taken

as the combination of two single fold-line models. But the K1 and K2 of these two single fold-line

models are different and they are applied in different ranges. Therefore, the tuning method and

steps of the dual fold-line model is similar with that of the single fold-line model, except that K1,

K2 and K1 (near), K2 (near) should be tuned respectively. First, tune K1 (near) and K2

(near).The tuning method is the same as that of K1 and K2 of the single fold-line model, that is,

filter out the data outside a circle with the site as the center and d as its radius. The specific

method is as follows: Set Max in the distance range in “Filter” to d, and set Min according to the

data filtering mentioned above. For the tuning of K1 and K2, it is required to filter out the data

inside a circle with the site as the center and d as its radius. The method is the same as above

except that Min should be set to d and Max should be set according to the data filtering 2003-04-28 All rights reserved of 37


mentioned above.

Figure 17 Distance filtering setting in the tuning of K1 (near) and K2 (near)

Figure 18 Distance filtering setting in the tuning of K1 and K2

6.3 Tuning of Other K Parameters

Tuning method of other K parameters is in most part the same as that of the single fold-line

model. The only difference is that all the data inside and outside a circle should be included for



the tuning of other K parameters. Note that the tuning of other K parameters will have influences

on K1 (near), K2 (near) and K1, K2 in repeated tuning. Therefore, segment tuning should be

performed for K1 (near), K2 (near) and K1, K2 respectively.

7 Automatic Model Calibration Utility

To enhance the function of the model tuning module, Aircom provides an automatic model

calibration utility. Here is a brief user guide.

7.1 Input

The automatic calibration utility is only to perform automatic iterative processing on the

model tuning procedure to get the calibration result. The data filtering, geographical averaging,

header file information must be completed beforehand. So this utility performs calibration

analysis with the input being an Excel file outputted by the Analyzer function in the Measurement

module. However, this automatic calibration utility cannot import this Excel file directly, but just

import the contents into a txt file, so you need to convert the Excel file into a txt file before the

import.

7.2 Main Interface

The main interface of the automatic calibration utility is shown below:



Figure 19 Main interface of automatic calibration utility

As shown in the figure above, in the Info frame, the Currently Loaded File item shows the

path and file name of the imported file, the Analysis File Type item shows the type of the

imported file, and the Currently Selective Archive item shows the path and file name of the file

storing the tuning result.

The Initial Stats item shows the initial mean error and standard deviation, and the Tuned

Stats item shows the mean error and standard deviation after calibration. The Hata Params item

shows the calibration result of the parameter K, where the first column of Value specifies the

difference between the calibration result of each K and the corresponding initial value, the

second column Range shows the tunable range of the each K, and the third column of Fix

indicates whether it is fixed without tuning.

In the Opt Params frame, the Max Iters item specifies the maximum tuning times. If the

automatic tuning times of the system exceeds this value, the parameter Std.dev will not be tuned

even if it has not been converged to the minimum value. The Conv Accuracy item refers to the

convergence accuracy of Std.dev. That is, if the difference between the difference between two



values of Std.dev tuned consecutively is less than this value, it will not be tuned again.

The Status Log frame shows some procedure information.

7.3 Data Import

Select the File --> Open Analysis File menu to import the file required. After successful

import, the Currently Loaded File item in the Info frame will show the file name and path.

Figure 20 Data import

To save the calibrated model parameters, you can create an Archive to save them. Select

the File --> Create New Archive menu, and input the directory and file name of the file for

saving. To view the result saved previously, you can select the File --> Open Archive menu, and

designate a saved file, and the following window appears.

Figure 21 Archive Viewer window

This archive file can save not only multiple process results of one calibration, but also

multiple different results of different data. When saving a calibration result, you need to input

Comments as the identifier.



7.4 Data Tuning

The data can be tuned after the import. Select the Tools-->Auto-tune menu (as shown

below). The program will calculate the deviation of each K, and then add this deviation to the

corresponding K value set in Enterprise to get the actual tuning result required.

Figure 22 Data tuning window

After auto-tuning, select the Clutter --> View Required Offsets menu, and you can get the

Offset values of various Clutters

Figure 23 Clutter Offset values

Note: The clutter offset set in Enterprise is the opposite value of it. As the automatic model

calibration utility shows the absolute value of Clutter Offset, it cannot be applied directly, and we



do not tune the Clutter Offset. Therefore, this value can be the reference only.

In addition, this utility also provides the manual tuning function, Select the Tools --> Apply

Params menu, the program will tune the parameters of K1 and Kclutter only, and then set Mean

Error to 0, and calculate Std.dev. The values of other Ks need to be inputted manually.

Select the Clutter --> View Initial Mean Errors menu, and you can also see the test data

points of various Clutters and Mean Error values before the tuning, as shown below:

Figure 24 Test data points of various Clutters and Mean Error values before the tuning

8 Notes

8.1 Model Tuning Method with Test Data based on Ec

Currently, we usually perform model tuning with the drive test data based on Ec, so it is

necessary to introduce its tuning method. The model tuning process with the drive test data

based on Ec is basically the same as that of CW test data except for the data processing. The

differences are as follows: 1) Dispersion processing is unnecessary for the test data based on

Ec; 2) For the test data based on Ec, the data with the signal strength less than -110dBm should

be filtered off.

8.2 Problems Concerning Find site Function

When we click the Find site icon in the “CW Measurement Analysis” window, Enterprise will

automatically search for the corresponding information in the BS information table according to

the longitude and latitude. If it cannot find any, the following prompt will appear:

Figure 25 Find site prompt information



Meanwhile, Enterprise will change the antenna type in “info>” to Unknown, which will result

in model tuning error due to the improper antenna type So we do not use the “Find site” function.

In case you click the “Find site” icon by accident, remember to change the antenna type in

“info>”.

8.3 Problems in BS Longitude and Latitude Format Conversion

The imported Base Station Longitude and Latitude in “info>” can be expressed in three

ways: 1) in degrees, 2) in degrees/minutes/seconds, and 3) in length. We usually adopt the

expression in degrees. But Enterprise has a bug, that is, when we import the information in

degrees, the information will be expressed in degrees/minutes/seconds. And when we convert it

into the expression in degrees, a conversion error will occur. For example, the imported latitude

is 113.74874467 and the longitude is 23.04457588, but they are displayed in

degrees/minutes/seconds after being imported, as shown below

Figure 26 Values of the BS longitude and latitude expressed in degrees/minutes/seconds when being imported

When the longitude and latitude are converted into the expression in degrees by using the

<Set Loc> button in the figure, an error occurs, as shown below:



Figure 27 Error values of the imported BS longitude and latitude after conversion

Therefore, an error occurs in the conversion process, and the reason is unknown. To avoid

this error, it is required to check the BS longitude and latitude after importing them. If a

conversion error is found, import them again, or modify them manually, as shown in the following

figure:

Figure 28 Manual modification of the imported longitude and latitude



List of references:

[1] Gu Jufeng, Latitude and Longitude Data Conversion between WGS1984 Coordinate System

and Beijing 54” Coordinate System, 2002/07/08

[2] Chenjing, WCDMA RNP ENTERPRISEV4.0 Planning Software Usage Guide, 2002/07/30

[3] Wang Mingmin, Yang Puqu 2GHz Frequency Band Propagation Model Tuning Analysis

Report, 2002/06/05


wcdma rnp data analysis of propagation model tuning guidance-20040719-a-2.2

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