Model Tuning

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Asset Propagation Model Tuning

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Table Of Contents

Page

1.

Introduction...............................................................................................................................3 1.1 Purpose ................................................................................................................................3 1.2 Scope....................................................................................................................................3 1.3 References............................................................................................................................3 1.4 Definitions ...........................................................................................................................3

2.

General Equation Background................................................................................................4

3.

Database Requirements............................................................................................................5

4.

Prediction Model Initial Conditions........................................................................................6 4.1 General.................................................................................................................................6 4.2 Creation of Model in Asset..................................................................................................7 4.3 Creation of Reference Site in Asset.....................................................................................7

5.

Drive Test Data Import Into Asset ..........................................................................................7 5.1 File Import ...........................................................................................................................7 5.2 Data Verification .................................................................................................................8 5.3 Reference Site Model Tuning..............................................................................................8

6.

Model Tuning ............................................................................................................................9 6.1 Numerical Analysis Process ................................................................................................9 6.2 Initial Tuning of K1 ...........................................................................................................10 6.3 Initial Tuning of K2 ...........................................................................................................11 6.4 Multi-site Drive Test Analysis. .........................................................................................11 6.5 Multi-site Drive Model Tuning. ........................................................................................12 6.6 Tuning of K7......................................................................................................................13 6.7 Tuning of Clutter Coefficients...........................................................................................13 6.7.1 Clutter Offsets........................................................................................................13 6.7.2 Clutter Heights and Separation ..............................................................................15

7.

Document Control...................................................................................................................18 7.1 Authorisation .....................................................................................................................18 7.2 Amendment List ................................................................................................................18

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1. Introduction 1.1

Purpose The purpose of this document is to specify the recommended method of tuning the Aircom – Asset, radio propagation prediction tool.

1.2

Scope This document is intended for technical staff involved in the design of cellular mobile phone networks.

1.3

References Singtel Optus

Drive Test Methodology for Propagation Model Tuning

Aircom

Asset User Reference Guide Version 4.1

1.4

Definitions

CBD

Central Business District

CW

Continuous Wave

ERP

Effective Radiated Power

EIRP

Effective Isotropic Radiated Power

RMS

Root Mean Squared

DB

Decibel

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2. General Equation Background The research work performed in Japan in the 1960’s by Okumura and Hata, culminated in the formulation of the General Equation for radio propagation path loss in a mobile phone environment. This equation has limitations, but contains terms which take into account the path loss influence of the following effects, 1) Frequency. 2) Distance. 3) Antenna height. 4) Antenna pattern. 5) Ground reflection. 6) Clutter. 7) Diffraction into non-line of site regions. The general equation is the recommended prediction method for modelling of mobile phone base station coverage in Asset. This model provides a high accuracy for rural, urban and highdensity urban areas where the antenna height is greater than the surrounding structures. Within an area like a CBD, the limitations become apparent. For complex areas like a CBD, details of the individual buildings and structures need to be present in the database. Also, the effect of reflection must be taken into account. A better prediction model for this environment would use “ray tracing” techniques to predict the “multi path” nature of the coverage patterns. These prediction techniques are not presented in this document. The general equation is essentially a linear prediction method and determines the radio signal strength along a direct path away from the transmitting source. No account is included for reflection from structures which, are alongside the direct path. The equation of path loss is specified below. Loss = K1+ K2Log(d) + K3(Hms) + K4Log(Hms) + K5Log(Heff) + K6Log(Heff)Log(d) + K7Diffn + C_Loss.

Where d = distance between base station and the mobile specified in Kilometres. Hms = Height of the mobile above ground in metres. This is usually set to the value of 1.5m Heff = The effective base station antenna height in metres. Diffn = The diffraction loss. Alternative methods are available. Epstein–Peterson is recommended. K1 = Constant offset. K2 = Fade slope constant. K3 = Mobile antenna height factor. This parameter is normally set to zero. K4 = Okumura – Hata multiplying factor for mobile height. Normally set to zero. K5 = Effective antenna height gain. Options are available but “Slope Method” is preferred. K6 = Log of the effective antenna height gain. K7 = Diffraction loss Version: Draft 1 14/7/2003

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RESTRICTED C_Loss = Loss due to clutter. Eg, urban, CBD, trees. Individual class coefficients are tuned. In addition to the loss coefficient for clutter, provision is included to account for individual clutter heights and separation distance between the clutter and the mobile. This provision produces a more accurate result but dramatically increases the calculation time. The general equation prediction method has less accuracy when the antenna height becomes comparable with the height of the nearby clutter. In these situations, the model will overpredict the potential coverage. It may be necessary to provide a separate model, specifically tuned to compensate for the increased fading of this type of base station. Similarly, the general equation will tend to under-predict the potential range of a base station located on a high hilltop or mountain, which is typically 400-500m above the valley. A separate prediction model for these situations may also be required.

3. Database Requirements In order to perform accurate predictions, an adequate database of the topology (terrain) and morphology (clutter) is required. Higher resolution will produce a more accurate prediction. The trade-off with the higher database resolution will be a slower calculation time. For rural areas a 100m-pixel resolution is usually sufficient. Within a metropolitan area, 50m-pixel resolution is preferred. Within a CBD a 5m resolution is preferred but a different prediction model is required, and the Okumara-Hata general equation is not suitable. The clutter database should classify the following regions, 1) Low Density Urban (Suburbs, detached single storey houses less than 6m high) 2) Medium Density Urban (Residential multistorey buildings less than 15m high) 3) High Density Urban (Residential multistorey buildings various heights up to 25m) 4) High Rise Industrial (Large factories up to 40m height, metal roof, brick structures). 5) CBD. (High rise buildings) 6) Trees. (A few categories here would be better. Such as “dense forest”, “open forest”) 7) Open areas. 8) Grasslands. (Crops, low scrub, pastures etc with less than 1m height vegetation). 9) Roads. (Wide major roads only). A complete database of the antenna patterns is required. For the model tuning process only the antenna pattern of the test antennas are required. However, once the prediction model is tuned, patterns for each antenna used within the base station network must be included in the prediction for each site.

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4. Prediction Model Initial Conditions. 4.1

General

An extensive history of testing and experimentation has already taken place with optimum tuning of the Okumura-Hata general equation. For this reason it is possible to specify several initial conditions of the model. Fine-tuning may then be confined to the primary coefficients and the specific differences of the localised clutter effects on the signal fading. The following initial conditions can be specified and an explanation for these choices. 1) Asset Model Type = Standard Macrocell. Selects the general equation model. 2) Frequency = Selected as required. Example 900MHz, 1800MHz. 3) Mobile RX height = 1.5m 4) Earth Radius = 8491.2 Km 5) K1, K2, K7 = These parameters will be tuned. 6) K3 = 0 7) K4 = 0 8) K1 near, K2 near, d near = 0 9) K5 = -13.82 This value has been derived by Okumura-Hata. Standard value is suitable. 10) K6 = -6.55 This value has been derived by Okumura-Hata. Standard value is suitable. 11) Effective Height = Slope. This consistently produces higher accuracy than other methods. It is particularly effective at predicting the effect of rising terrain when the signal strength generally increases due to the path appearing more like free space. It provides up to 15dB better accuracy than other methods. Other methods available are “Absolute”, “Average”, and “Relative Height”. For more information on these options refer to the Aircom Asset User Reference Guide. 12) Slope distance = 10 times the resolution. Ie. 50m resolution, distance = 500m 13) Heff min and Max. These parameters will be tuned. 14) Diffraction Method = Epstein-Peterson. This method has consistently provided more accuracy. Other methods available are “Bullington”, “Deygout”, and “Japanese Atlas”. For more information on these options refer to the Aircom Asset User Reference Guide. 15) Merge Knife-edges = 2 times the database resolution. I.e.50m resolution, Merge =100. 16) Clutter Offsets. These parameters will be tuned. Initially set all offsets to 0. 17) Clutter heights = Enabled. This is optional and depends on acceptance of longer calculation time versus the desire to have the slightly higher accuracy. Initially set all heights to 0. 18) Clutter separation = Enabled. This is optional and depends on acceptance of longer calculation time versus the desire to have the slightly higher accuracy. The separation distance is also tuned. Initially set all separations to 0. 19) Use Mobile Heights = Disabled 20) Mobile Height = 0

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4.2

Creation of Model in Asset.

To create the new model, in Asset select under the “Tools” tab, the “Propagation Model Editor” option. Select “Add” and enter a name for the new model. Note that write privileges are required to perform this task. Enter the new model parameters using the recommended defaults and assign the default numbers a described below for the un-tuned coefficients. The primary coefficients of the model are K1 and K2. The K1 coefficient sets the overall “size” of the coverage whereas K2 predominantly effects the level of coverage close to the site. K6 effects how the prediction behaves in non-line-of-sight regions. These parameters are tuned primarily by the influence of the terrain. The influence of the various clutter classes is effectively fine-tuning of localised attenuation. Initial starting values are required for K1, K2 and K7 and the effective height. Default values of K1 = 140, K2 = 40 and K7 = 0.6 will be suitable. Also, use a minimum height of 10m and a maximum of 100m for the slope method of effective height. Ensure all clutter offsets, heights and separation distances are set to 0. Disable clutter heights.

4.3

Creation of Reference Site in Asset

From the range of test site locations, select a site, which would be considered “typical” suburban. This site should be on relatively flat terrain and surrounded predominantly by lowrise dwellings and buildings for several kilometres radius. The site antenna height should be 25-30m above ground or at least 20m higher than the surrounding clutter. This test case or “reference site” will be used for the visual analysis of the prediction model performance when compared to the drive test data. Create the reference site in Asset and ensure the location, antenna type, antenna height, transmit power level and prediction model are all correctly applied to the site configuration. Other details normally entered in Asset to fully describe a base station are not required at this stage. The details need only be sufficient for modelling of the reference test transmitter site.

5. Drive Test Data Import Into Asset The drive test data should be stored on a file server or hard disc drive, under a directory, which can be accessed by the Asset application. The different categories of drive test (ie macrocell, microcell) need to be in separate directories and clearly labelled.

5.1

File Import

1) In the Asset application, select “Tools” and “CW Measurements”. 2) Select “Add” and choose the type of measurement file. The formatted files should contain a header file and a data file. The header file contains the test transmitter set-up details and is linked to the data file. The data file will contain the measurement point locations and a signal strength reading. 3) Using the directory tree, search for the file locations. Select and “Add” the file. At this stage select only one drive test file at a time and perform a data verification process. See section 5.2 of this report.

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5.2

Data Verification

It is assumed that Asset has available, a database of the local city streets or roads, as well as the terrain and clutter information. 1) In Asset under the 2D-display view, load a roads or streets vector from the standard vectors tab. 2) Select the “CW Measurements” tab and double click on “CW Signal”. 3) Select the “Categories” tab on the top of the window. 4) Add and create a range of signal strength thresholds using unique colours for each. A suitable range of threshold and colours are indicated below. -50dBm

Bright Yellow

-60dBm

Orange

-70dBm

Blue

-80dBm

Red

-90dBm

Green

-110dBm

Pale Yellow

These same colours and thresholds must also be applied to coverage predictions in Asset, so a direct visual comparison can be made. 5) Choose a solid plot character and a circle shape is suitable with size ‘50’ for the plot character. Plot each drive test measurement one at a time and verify the accuracy of the navigation compared to the vector data of the street database. Check the expected signal fading. If the plot has numerous errors of navigation, missing data, unusual and unexpected signal levels (too high, too low) the test may be unsuitable. Verify each drive test in this way before performing the model tuning. Discard any drive test, which may be considered invalid.

5.3

Reference Site Model Tuning

1) Once each drive test has been checked using the procedure in section 5.2 and found to be suitable, select the “Reference Site” and remove all other files from the CW Measurement Analysis. 2) Confirm the file configuration using the “Info” tab under the CW Measurement Analysis. Ensure that the essential details of Transmitter power, Transmitter test frequency, Antenna height, Antenna type and Site location are all correctly entered. Note that transmitter power must be specified in terms of EIRP and compensated for both the test transmitter and test receiver antennas. If the recorded power was specified as ERP of the test transmitter only, the power must be adjusted by +4.3dB. This is because Asset predictions are based on isotropic transmitter and receiver antennas. For practical reasons, the test transmitter power is usually specified in terms of ERP. Compensation is also required however for the receive antenna which will provide signal strength measurements in terms of normalised power relative to a dipole antenna. The compensation between dipole and isotropic antennas is 2.15dB with a dipole having more gain relative to the isotropic antenna. Twice this factor equates to 4.3dB. 3) Select the “Options” tab and under the “Model” tab, select the required resolution (this should match the terrain/clutter database resolution) Version: Draft 1 14/7/2003

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RESTRICTED 4) Select the model to be tuned. The new model should have been entered following section 4.2 of this report. 5) Under the “Filter” tab, exclude the “Null” and “Water” clutter by highlighting these items. 6) Adjust the calculation radius. Minimum of 0m and maximum of 100,000 will usually allow all test data to be valid. The range of valid data may be adjusted if data at either extreme of distance is found to be unsuitable. 7) Set the filter for upper and lower limits of the test receiver. These limits will depend on the test receiver performance. Generally signal levels above –40dBm enter into the saturation limit of a receiver and levels below –110dBm are affected by noise. These may be suitable limits. Refer to the drive test receiver equipment specification or calibration report for more details. 8) Select LOS (Line of Sight) and NLOS (Non Line of Sight) data visibility. This setting will be changed later to tune different aspects of the prediction model. 9) Display the drive test on the 2D view in Asset. 10) Display the prediction of the Reference test site as an overlay on the drive test plot. 11) Examine the overall differences between the measurements and the prediction. This view should be evaluated after each step change in the model tuning process to observe progressively, the effects of the tuning. 12) Note how the measured signal fades and how the radii of the signal threshold step changes relate to the corresponding step changes on the prediction. If the measurements at the outer fringe of the plot are typically much higher than the prediction, then K1 may be too high. If the measured threshold steps close to the site indicate a lower rate of change with increasing distance than the prediction, then K2 of the model may be too high. In each case the converse will be also be true. Many of the individual difference errors between the measured and predicted at this stage will be related to the fact that the influence of clutter has been disabled. A prediction with greater detail will be produced when this feature is enabled and tuned.

6. Model Tuning The model tuning will involve a combination of graphical analysis comparing the drive test data with prediction plots as described in section 5.3, and numerical analysis using the statistical study available in Asset. Relying only on the numerical analysis is not recommended. The numerical analysis is a guide, which can offer insights into the potential accuracy of a prediction model. It can also assist in finding an optimum value for a specific coefficient by tuning for a RMS minimum around a particular coefficient value. However, due to errors in the registration between the drive test measurement samples and the clutter database, an accurate numerical analysis is not possible. The following steps 1 to 9 provide the method of preparing the measurement data for the numerical analysis.

6.1

Numerical Analysis Process

1) With the drive test file for the reference site entered as per section 5.1 of this report, select the “Analyse” tab. A window will provide a further set of options. Use the defaults as selected but de-select the “Show Individual Bin” information. This option provides excessive and non-essential information in the report. 2) The analysis process will produce an excel spreadsheet. A sample of this output is presented in table 1.

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Site ID

Site Name

Num. Bins

Mean Error

RMS Error

Std.Dev. Error

Corr. Coeff.

H:\SJB\MSI Data\M90\30m90_9.hd

30m90_9

Survey

6528

1.5

7.8

7.6

0.8624

Model

Num. Bin Mean Error RMS Error

Std.Dev. Error

Corr. Coeff.

Training G9 Model

6528

7.6

0.8624

Clutter

Num. Bins Mean Error RMS Error

Std.Dev. Error

Corr. Coeff.

Open

129

3.3

6.8

6.0

0.9574

K1 = 130

Roads

2971

3.8

8.2

7.3

0.8546

K2 = 40

Vegetation

14

6.0

8.5

6.3

0.9451

K7=0.6

Low Density Urban

3124

-0.9

7.4

7.4

0.8381

Clutter Disabled

Medium Density Urban

23

2.8

4.0

3.0

0.3658

High Density Urban

124

2.5

6.5

6.0

0.6701

Light Industrial/Commercial

143

3.5

7.4

6.5

0.8210

1.5

7.8

Table 1. Model Tuning Analysis Output One Site. 3) This example has 1 drive test file, the reference site. In this example a 30m base station in a suburban area. Copy this spreadsheet into a new model-tuning directory and give it a label, which will provide an indication as to the progress of the tuning steps, ie. K1RefTuneStep1. The settings of the parameters being tuned should also be entered on the spreadsheet for reference. This table provides information about the mean and RMS errors of the propagation model. The values are in decibel (dB). A target RMS error of less than 7dB will provide a prediction model that has acceptable accuracy.

6.2

Initial Tuning of K1

1) In the CW Measurements “Options”, de-select the Non-LOS visibility data filter. 2) Run the analysis tool and keep a record of the spreadsheet output. Note the RMS error for the particular K1 and K2 value used. 3) Change the value of K1 in the prediction model by a small increment (less than 5) with either an increase or decrease in value depending on the initial observations from step 12 of section 5.3. For example, if the model appears to be over-predicting, then increase the value of K1. 4) If K1 was increased in value but the RMS error also increased, then K1 must be adjusted in the opposite direction, to a lower value. 5) This process is iterative until the lowest RMS error can be found. As the minimum RMS error is approached, adjust K1 in steps of 1. If a range of values provide the same RMS error, select a mid-point. 6) Evaluate the result on the Asset 2D-view presentation. Ensure the prediction is re-done for the new values.

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6.3

Initial Tuning of K2

1) Using the final tuned value of K1 established in step 6.2 above, apply the same process for the adjustment of K2. If a range of values provide the same RMS error, select a midpoint. 2) The analysis result will indicate the mean error. This value may be subtracted from K1 to achieve the final optimised values for K1 and K2 at this stage of the process. 3) Evaluate the result on the Asset 2D-view presentation. Ensure the prediction is re-done for the new values. At this stage the most accurate values for K1 and K2 have been found for the “typical” macro-cell base station, using the Reference site. It is now necessary to introduce the other drive test measurements to further fine tune K1 and K2 and begin tuning the other parameters of the general equation.

6.4

Multi-site Drive Test Analysis.

1) As per section 5.2 “File Import”, in the CW Measurements Analysis application in Asset, Add all the other drive test files which are suitable for model tuning. Do not include microcells or drive test data that was found to be unsuitable. 2) For each drive test file, confirm the file configuration using the “Info” tab under the CW Measurement Analysis. Ensure that the essential details of Transmitter power, Transmitter test frequency, Antenna height, Antenna type and Site location are all correctly entered. Note that transmitter power must be specified in terms of EIRP and compensated for both the test transmitter and test receiver antennas. If the recorded power was specified as ERP of the test transmitter only, the power must be adjusted by +4.3dB. 3) Perform steps 3-8 of section 5.3 if these settings have been changed. 4) Run the analysis again this time for the combined collection of drive test data. A table similar to that shown in Table 2 will be produced. 5) Sort the output result for the different drive tests in terms of antenna height. 6) Indicate on the table the model parameter settings and save the table in a suitable directory with a label indicating the tuning progress stage ie K2MultiTuneStep1, for later reference. 7) Calculate the weighted mean and RMS error for each antenna height. This may be performed by first, multiplying each mean error (or RMS error) by the number of bin samples. Then the sum of all the weighted mean errors (or RMS errors) are divided by the total number of bin samples. The results of this calculation for the example have been inserted at the bottom of the Mean Error and RMS error column in table 2. 8) The overall-mean error will indicate the magnitude of the prediction error. A positive mean error indicates under-prediction. 9) The RMS error indicates the spread of results and a RMS error of less than 7dB is an acceptable target.

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The weighted mean and RMS errors may appear to increase with lower antenna height. This will be because as the antenna height gets closer to the height of the surrounding clutter, the coverage potential will be reduced. The model will not have the ability to accurately predict the effects of clutter at low antenna heights since individual buildings and trees do not exist in the database. The model tuning process will attempt to produce an optimum model, which is useful for a range of antenna heights. For base stations with very low antenna heights which are within only a few metres of the height as the surrounding clutter, the prediction model may have unacceptably high errors. For these special cases, a separate model may be required. File

Site ID

Site Name

Num. Bins Mean Error

RMS Error

Std.Dev. Error Corr. Coeff.

H:\SJB\MSI Data\M90\30m90_9.hd

30m90_9

Survey

5005

-0.0

6.6

6.6

0.8250

H:\SJB\MSI Data\M100\32m100_9.hd 32m100_9 Survey

1323

0.9

8.7

8.6

0.8183

H:\SJB\MSI Data\m710\20m710_9.hd 20m710_9 Survey

2711

-2.2

6.8

6.4

0.7712

H:\SJB\MSI Data\M500\20m500_9.hd 20m500_9 Survey

2156

-6.5

8.7

5.8

0.8980

H:\SJB\MSI Data\m710\15m710_9.hd 15m710_9 Survey

2766

-3.3

7.6

6.9

0.7372

H:\SJB\MSI Data\M500\15m500_9.hd 15m500_9 Survey

1479

-6.2

7.9

4.8

0.9015

0.19

7.04

-4.1

7.64

-4.3

7.7

H:\SJB\MSI Data\m710\13m710_9.hd 13m710_9 Survey

1912

-7.5

10.5

7.4

0.7109

H:\SJB\MSI Data\M500\13m500_9.hd 13m500_9 Survey

1458

-6.0

8.1

5.5

0.8860

-6.85

9.46

Model

Num. Bin

Mean Error RMS Error Std.Dev. Error Corr. Coeff.

Training G9 Model

18810

-3.2

7.9

7.2

0.7881

K1 = 135 K2 = 43 K7 = 0.6

Clutter

Num. Bins Mean Error RMS Error Std.Dev. Error Corr. Coeff.

Open

1436

2.3

7.8

7.5

0.7630

Roads

5319

-2.6

8.0

7.6

0.7205

Vegetation

259

-0.9

7.5

7.5

0.5160

Low Density Urban

3162

-0.3

6.2

6.2

0.8125

Medium Density Urban

6303

-5.6

8.1

5.9

0.8391

High Density Urban

432

-2.7

6.8

6.2

0.7113

Light Industrial/Commercial

1802

-6.7

9.2

6.4

0.8645

CBD

97

-1.7

5.8

5.6

0.3981

Clutter Disabled

Table 2. Model Tuning Analysis Output with Multiple Sites.

6.5

Multi-site Drive Model Tuning.

1) The tuning of K1 and K2 must be repeated for the multi-site case. The overall mean error provides an indication of the K1 error so this value may be subtracted from K1 to commence the first step of the tuning process. Follow the steps described in 6.2 and 6.3 for the process, tuning K1 first to reduce the overall mean error to 0, then tuning K2 to produce the lowest RMS error. Ensure the Non-LOS data is excluded in the CW Measurement Analysis filter. Version: Draft 1 14/7/2003

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RESTRICTED 2) Evaluate the results on the Asset 2D view comparing the prediction with the drive test for different example sites. Note that each new site must be entered into the Asset database and the configuration entered to match the test transmitter. The revised values of K1 and K2 will of course degrade the prediction of the “reference” site but will provide a more useful model for other site locations. The degradation in the numerical analysis of the reference site is expected and acceptable, provided the RMS error remains low.

6.6

Tuning of K7

1) In the Asset CW Measurement Options Filter, select the “NON-LOS” data and de-select the “LOS” data. 2) Run the analysis process and evaluate the table produced. Consider small changes to K7 with steps less than 0.2. If an increase in K7 causes the RMS error to increase, try a reduction in the value. 3) Once an optimum value for K7 has been found, try a few case studies by comparing the prediction with drive tests. The case study examples must have situations in the drive test where a non-line-of-sight situation occurs such as a drive through a cutting or river valley. At the lower part of the valley, the terrain will block the path between the test transmitter and the drive test vehicle. Proper tuning of K7 will provide more accuracy for the coverage in these specific cases of the environment. At this stage, the final values for K2 and K7 have been established. K1 will require further fine-tuning following the influence of the clutter class tuning.

6.7

Tuning of Clutter Coefficients 6.7.1

Clutter Offsets

1) In the Asset CW Measurement Options Filter, select the “NON-LOS” data and also select the “LOS” data. 2) In the Filter settings, exclude all clutter type except for “open” clutter type. If an open clutter class is not provided, a similar clutter type may be suitable, such as “scrub”, “fields”, “crops” and “rocky ground”. A suitable class which represents the major area of morphology which has structures less than 1m height and has been used to represent open areas of land such as parks, fields crops etc. 3) Run the analysis and evaluate the table produced. 4) Subtract the overall mean error of the model from the value of K1 and run the analysis again. The overall mean error should now be zero and the model is now normalised to the “open” clutter class. 5) In the Asset CW Measurement Options Filter include all the clutter types with the exception of Water and Null. 6) Run the Analysis and evaluate the table produced.

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RESTRICTED 7) For each clutter type in the model, subtract the overall mean error specified from the tables for the specific clutter class. If a particular clutter class is not included in the table or the number of samples in the Bins are low, this means the drive test did not pass very often through the particular clutter zone, or did not pass at all through the zone. In this situation, a large error may occur when tuning the coefficient of this particular clutter class. The specific coefficient may therefore need to be set by careful study of the drive tests and prediction plots to produce a realistic prediction based on experience and the numerical analysis process should not be used. 8) Run the analysis and evaluate the table produced. The mean errors should now be zero and the RMS error should be an acceptable level. If the examples with low antenna height have high RMS errors (greater than 9dB), a separate model should be considered for accurate prediction of these cases. The “low antenna height” model will typically only require special values for K1 and K2 to obtain a more acceptable accuracy. The final values of the clutter coefficients should be used on the special variants. Recalculate the prediction of the “reference” site and plot the coverage on the Asset 2D view. This plot should now reveal more detail and match the drive test data in many more locations than before the clutter offsets were enabled. 9) If a “Roads” or “Streets” clutter class is available in the clutter database, there may be a tendency to over predict these regions. The result will look on a coverage plot as though the roads (or streets) are highlighted with more coverage than other surrounding regions. To some extent this is true since the open area of a road allows for a more direct path to the transmitter and coverage can be better on a wide road than within a narrow street for example. This is also true when the road is aligned in a radial path with respect to the transmitter. Unfortunately, the clutter class database does not usually accurately register with the actual roads on which the drive was performed. For this reason the numerical analysis will be misleading. It is recommended to “de-tune” the roads clutter loss to some degree and it may also prove to be optimum to set the “roads” clutter offset to match that of the most common clutter class in the database, such as Low Density Urban. 10) The clutter coefficient for water can be set to 0. 11) Although the clutter coefficients can be adjusted to get the numerical analysis optimised for minimum errors, the eventual coefficients need to have sensible relativities. For instance, the vegetation (trees) coefficient should be higher than the open class. Similarly, the High Density Urban areas should have more attenuation than Low Density Urban areas and the loss coefficient should be proportionally higher. Some exceptions to this generality may occur however. A clutter class such as “Industrial” may appear to have a low value of loss coefficient compared to High Density Urban for instance. This can occur if the typical industrial zone has widely separated and low-rise buildings and structures. Areas such as this will potentially have higher signal strengths because of the general open nature of the area and coverage can also be enhanced by reflection off the buildings. As a guide, the expected variation of the offset between the different clutter types should be of the order of 1-4dB. Individual case studies can be done to look specifically at various clutter zones to see if the model is performing as expected. 12) Finally, the coefficient offsets can be “rounded off” to whole numbers.

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6.7.2

Clutter Heights and Separation

The influence of clutter heights and separation distance will further enhance the prediction accuracy. Asset performs a diffraction loss calculation over the edge of the clutter and therefore requires the clutter height and the distance between the edge of the clutter and the mobile end point. Each clutter height needs to be set and the logical starting value depends on the specific clutter zone. A residential area or Low-Density Urban class will typically have a clutter height of 6m. This is because the typical building height of a single storey house is about 6m. Local variations will of course change these values. The separation distance defines the distance between a typical mobile phone and the nearest structure such as a building within the clutter zone. For example, in a suburban street the width of the street is typically 8m wide with a nature strip of 2m on each side. The house is set back from the street by typically 15m. Therefore the separation distance between the mobile phone on the street and the nearest structure will be about 20m. This could be used as a starting value for tuning of this coefficient. In all cases the separation distance should be less than or equal to the database resolution. Some suggested starting values for clutter heights and separations are listed below. Clutter type Low Density Urban Medium Density Urban High Density Urban High Rise Industrial CBD Roads Trees

Clutter Height 6m 10m 15m 20m 20m 0m 10m

Clutter Separation 20m 20m 20m 30m 10m 0m 30m

The following procedure is a guide for tuning the clutter height and separation. 1) In Asset, enable clutter heights and enter the recommended default values for heights and separation as listed above. 2) Ensure that the drive test data for multiple sites are entered in the CW Measurements Analysis. 3) Run the CW Measurement analysis. Note that the numerical analysis now indicates a high Mean and RMS error. 4) Subtract the overall mean error from the value of K1 and re-run the analysis. The numerical analysis should now be restored with an overall mean error of zero and a low value of RMS error. The RMS error may also be lower than the result achieved without the clutter heights and separations enabled. Individual clutter class, mean and RMS errors provide an indication of the accuracy of the clutter heights and separation values. 5) Select a specific clutter type one at a time and perform the tuning process described in steps 5 to 9. 6) Keep the value of clutter separation fixed and adjust the value of clutter height in small steps (less than 2). 7) After each iteration of the analysis, readjust the value of the clutter offset by subtracting the clutter mean error. If the RMS error of the clutter in the analysis report increases with an increase in the value of the clutter height, reduce the value of the clutter height. Version: Draft 1 14/7/2003

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RESTRICTED 8) Tune the height value for a minimum RMS error for the clutter class (not the overall model RMS error). If the clutter height does not appear to tune to a minimum RMS error value or the tuning is “vague”, select a suitable default value for the clutter height. Poor tuning can occur if insufficient drive test data is available for a particular clutter class, or if the clutter data is inaccurate. Adjusting the clutter value to a height, which is unrealistically lower than the actual real world height is not recommended. However, If the particular clutter class only occurs in the actual environment in small areas or pockets, the influence on the path loss may also be small. Experience and best judgement may be required when setting the final clutter height in these cases. 9) Evaluation of the final clutter height tuning using a prediction plot and examination of “case studies” will also assist in determining a suitable clutter height. 10) When all the clutter heights have been tuned, commence tuning the clutter separations one at a time in a similar manner, using steps 5 to 9. Adjustment of the clutter separation may be done using larger steps such as 5 to 10m. The tuning of the clutter separation usually has a low sensitivity and a well-defined minimum in the RMS error may be difficult to find. In situations where it is not possible to obtain a clear minimum on the RMS error, experience and best judgement may be required to set a suitable value. 11) When the Clutter heights and separations have all been set to an optimum value, ensure that the CW Measurements Options filter has selected all clutter types except Null and Water. 12) Run the analysis once more and note the clutter mean errors. 13) Subtract the mean errors from each clutter offset in the prediction model. Round off the Clutter offsets to the nearest integer. 6.7.3

Effective Height Limits

At this stage the Effective Height will have the default settings of minimum 10m and maximum of 100m. If the lower limit is reduced below 10m, as may be expected for a base station, which has a low antenna height, a problem may appear. The method Asset uses to apply the antenna pattern to the prediction, allows all characteristics of the antenna pattern to modulate the prediction. This includes the pattern nulls on the underside of the main lobe. The underside nulls may affect the actual coverage but this effect becomes less significant when the distance is less than a few hundred metres distance from the base station. At short distances, the effects of reflection become dominant and counteract the influence of the antenna pattern underside nulls. To simulate the effect of these near field reflections, it is necessary to use a suitable value for Effective Height minimum. If the minimum is reduced to 0 for instance, the effect will be to reveal coverage holes quite close to the base station, which are created by the antenna pattern. This is not found in practice and will look unrealistic.

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RESTRICTED At the other extreme, the maximum value of Effective Height needs to be tuned based on a specific characteristic of a base station. The specific characteristic is the relative height difference between the base station antenna height and the predominant height of the area receiving coverage. As an example, a typical suburban base station with 25m height on relatively flat terrain will only require a maximum Effective Height value of approximately 50m. This limit will be valid for base station heights up to 50m in this type of environment. Alternatively, the base station may be located on the top of a mountain and provide coverage to a valley below. In this situation the relative height difference may be as much as 300m to1000m. A much greater value of Effective Height is required for these “mountain” classes of base stations, and a value of 500 to 1000m may be suitable. If the Effective Height Maximum is not adjusted accordingly, an under prediction will occur for the mountain site with a low value set. Conversely, an over-prediction of the coverage will occur for a flat terrain suburban site if a high value is used for the Effective Height maximum. It is usually not necessary to have a large number of different Effective Height model variants. A “standard” and a “Mountain” are usually sufficient. To effectively tune the “Mountain” model Effective Height maximum value, it is necessary to have drive test data from a test case which will create this condition. The drive test will also need to collect measurements from a great distance since the range of the “mountain” site can be very large. The tuning of the Effective Height maximum will be done to make the more distant prediction more accurate. The tuning method using the numerical analysis may not be effective and case studies of drive tests compared graphically to the prediction may prove to be the best method for deciding on a suitable value of the Effective Height maximum. The model tuning process is complete.

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7. Document Control 7.1

Authorisation

A.

Author

B.

Authorised

C.

Approved

7.2 Version Draft 1

Version: Draft 1 14/7/2003

Stephen J. Bentley

Amendment List Date

Section

Nature of Amendment

Amendment Author

All

Document compilation

Stephen J. Bentley

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