Model Tuning Radio

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How-To Guide forModel Calibration

Summer 2004

RADIO ENGINEERING SOLUTIONS


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How-To Guide for Model Calibration

For Internal Use Only Preliminary v1.0 Page 1

IntroductionThis document outlines and provides selected details about building a empirical model in ASSETusing drive test data. Data collection is not extensively discussed, as its procedures vary

depending on equipment used. In general, it is assumed that DTI equipment is used to collectBCCH data on a live network.

An update of the various data collection methodologies, data filtering guidelines, and discussionof acceptable formats is presented. It illustrates how to navigate between various modules of

ASSET and suggests useful tips on various user dependent options. It provides some generalguidelines to an ASSET user to better calibrate the propagation model. but do not address everypossible approach to model calibration.

CW MeasurementsTraditionally CW Field measurements are carried out using a spectrum analyzer, which measurethe output of a test transmitter, which produces a Continues Wave Output at the desiredfrequency and output power. This document does not discuss traditional CW type drive testing,

but data preparation, import, and analysis is essentially the same.

In carrying out CW type measurements, the engineer has full control of the transmit facility andknows with great certainty site power and antenna parameters. Most often, this is a omniantenna, so azimuth and downtilt become irrelevant. Unfortunately, in a live network, there maybe some errors associated with the site databases used for this work.

While CW measurements may only involve 2 or 3 site locations (and 1-5k sample points), BCCHmeasurements can utilize as many site locations as time permits, and the number of samplepoints can be magnitudes larger (100 200k). With the large diversity of site locations that maybe used, it will be of greater difficulty to achieve traditional error limits of 8 dB (std. dev.) If this is alimiting factor to your work, reduce the number of sites used in analysis.

Live System/BCCH MeasurementsThe need to carry out measurements on modulated Broadcast Channel (BCCH) arises from thelong setup time involved in CW measurements and from the large overhead of data collectionover the repeated routes in the same location.

Modulated BCCH measurements involves using a Scanner that carries out fast multiplefrequency scanning, and is also able to decode the Base Station Identity Code (BSIC) and theTransmitter ID. The scanning is carried out on LIVE networks, and does not use up systemresources. The scanner scans all the frequencies that are used as a Broadcast Channel, and logsthe position, the frequency, the BSIC and Transmitter ID.

The major advantage of this method is the near nil setup time and the ease of data collection.This enables the data collection of many sites, and hence a more accurate model calibration.

There is also a flexibility of choosing any site to carry out model tuning, even after the datacollection is completed.

There are a few disadvantages in carrying out Modulated BCCH measurements for modelcalibration:

The most prominent disadvantage being the use of directional antennae with very narrowvertical beam widths and having appreciable vertical down tilts. This tends to distort theradiation pattern of the antenna which has a significant effect on the model developed.

In dense urban areas, often antennae are below the surrounding clutter, with the boresight of the antenna pointing towards the street. This leads to tunneling of the signalthrough the street, with a very high roll off of signal strength of streets perpendicular to


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the main street. Calibrating this type of propagation is very difficult, if not impossible,using a slope intercept model.

Also, the modeling of the data collected in the back lobe of the directional antenna is very

difficult and tends to introduce error into the model. This problem can be addressed byusing appropriate antenna filtering (i.e.) using a filter to exclude points outside the 3dBbeamwidth of the antenna.

There are advantages and disadvantages to each method.

Post-Processing of Scanned Modulated BCCH dataPost processing of the data involves assignment of a particular measurement of a particularfrequency to its respective transmitters using unique BSICBCCH-Transmitter Ids. Formeasurements in which the BSIC and/or the Transmitter ID are not decoded, the assignment isdone on the basis of knowledge of the site location, the EiRP of the cell, the antenna pattern, theantenna height and basic propagation fundamentals.

Post processing requires a database of the entire site database, which necessarily containsindividual cell IDs, the parent site ID, location information in Latitude and Longitude BCCH, BSIC,Transmitter Cell ID. Also required are the antenna type, antenna height and EiRP. When usingthe DTI Clarify Product, the post-processed output will be in a form similar to that shown below:

Longitude Latitude Drive_Number Sector_Rx_Power C_I BER RXQUAL

-74.20389187 40.52577304 61 -99.3 16.32775 2.468901 4

-74.21020485 40.52476605 61 -112.7 -0.4478591 33 7

-74.21688479 40.52201996 61 -110.36 -0.9116459 33 7

-74.20082879 40.52710476 61 -83.99 27 0.19 0

-74.19245104 40.53154366 61 -51.69 27 0.19 0

-74.18919493 40.53318964 61 -54.8 27 0.19 0

-74.17711683 40.5362465 61 -91.71 27 0.19 0

This data is produced, on a per sector basis, in an MS Access format (*.mdb). In order to importinto ASSET, it must be edited (with a text editor, or more simply, MS Excel) and put into a formthat ASSET will recognize. Although there are several formats that ASSET can read, a commonone for model tuning is the Signia format.

The Signia FormatThe Signia format is used as it is convenient and easy to create (MS Excel is the most likelyeditor).There are two files needed for Signia, a Header (*.hd) file and a Data (*.dat) file. TheHeader and Data files are linked by an identical file name.

Hint: The header and data file must be in the same folder, and the folder can have any path.


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Header files (*.hd)A Header file is a tab-delimited text file containing information regarding the individual cells.Although there are many fields, only a few of them are critical for model tuning.

Hint: If header file does not load, check format, spacing, and EOF marker (carriage return), for errors. Remove any tags orunit (degrees, feet, etc.) from the input.

Hint: Be sure ANTENNA_TYPE is located in the antenna database file, or an error message will be generated whenloading. TX_HEIGHT should be in meters, and TX_POWER in dBm. TX_POWER is the site EIRP, not hatchplate power.

Hint: To facilitate file management, make the SITE_ID, the header file name, and the data file name identical.


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Data Files (.dat)The Data file is a tab-delimited text file containing Long, Lat, and RSSI of each cell as measuredby the receiver device. Decimal Lat-Long (DLL) formatting is required and each line represents

one measurement location. There is no limitation to the number of measurement points in a Datafile. If MS Excel is used as the text editor, there will be a limit of 65k points.

Single data entry

Longitude (DLL)

Latitude (DLL)

Received Signal Strength Indicator (RSSI) dBm

Ta s


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Loading Drive Data into ASSETThe Drive Data is loaded from the Main Menu Tab ToolsCW Measurements Analysis Tab.This opens a pop-up window as shown below.

Hint: When a sector drive file is added, the user is prompted for 'Bin Averaging', which averages all the samples foundwithin a map bin. This feature is usually not selected, but may be applicable for drives with high number of samples, suchas in a Dense Urban area where the test vehicle was moving very slowly.

Add/Remove ButtonsThis adds or removes individual sector data files for analysis. The loaded sectors and filepath/name are shown in the main window. It pop-up a standard Windows browser screen to AddFile.

Adds/Removes individual sector drivedata files

Displays sector informationcontained in header (*.hd) file

Pops-up Filtering and Model

Selection Window

Pops-up Graph window for RxLev VsDistance, or Mean Error Vs Distance

Not applicable. For use with RANOPToptimization tool.

Begins regression curve fitting andprompts user for Error Report Type

Pops-up AutoTuner Window, showinginitial parmeter set.


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Info ButtonOnce a sector file is loaded, the site information pertaining to that cell can be reviewed.Information about the location of the test site, output power, antenna height, cable and connector

type and losses, and the antenna type. If there is cause or need to edit this info, it can be done atthrough this window.

Hint: Most often, site parms such as FEEDER_LENGTH, TYPE, CONNECTOR_ LOSS, etc. may not be known. Insert thesite EIRP value as the transmit power (TX_POWER) value and zero the cable and connector losses.

Hint: If site parameter changes are made in these windows, the changes will be applied, but not committed. You mustmanually change the header file if you desire any permanent parameter changes.


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Options/Filter TabThis window provides filtering options that the user may wish to employ, depending on the task

involved. Distance, signal level, Line-of-Sight, and Antenna Filtering are shown. Also given is theoption of removing specific data points assigned to clutter types. More on the usage of thesefilters is given in the Tuning and Analysis section of this document.

Graph ButtonThe tool will also produce a graph of the sample data vs. distance. This graph shows a numericalintercept and gradient value for the data, but does not typically give useful insight for calibration.

Also available is mean error vs. distance. This gives.


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Analyse ButtonGenerating an analysis of the chosen base model versus the actual data points generates anInitial Statistics of the data loaded. The Analyse button prompts for Analysis Report Options,

and based on user needs, the report options are chosen.

Note: The report can either be generated in either MS EXCEL (best option) or any Text Editor.The following shows example of Report Options generated upon completion of analysis.

File Summary - provides summary on a per-file or per-sector basis (i.e. per drive test) The filesummary identifies the various sites loaded into the system for analysis, along with a site wisebreakup of the data points (Num.Bins) collected for that sector, the Mean Error, the Root MeanSquare Error (RMS Error), the Standard Deviation (Std.Dev Error) and the correlation coefficient(Corr.Coeff). It helps the user in assessing the model on a site by site basis, and also helps theuser, if required, to reclassify certain sites under a different morphology class.

Example File Summary

Site ID Site Name Num. Bins Mean Error RMS Error Std.Dev. Error Corr. Coeff.

DN03504C DN03504C 923 -15.6 18.8 10.5 0.8272

Overall Summary - gives overall summary of all drive files loaded. The overall summary providesthe combined statistics of how the model compares with the collected data. The values provided

in the overall summary are the key points by which the model is evaluated.

Example Model Summary

Model Num. Bin Mean Error RMS Error Std.Dev. Error Corr. Coeff.

DEN_SU_1004_v1 923 -15.6 18.8 10.5 0.8272

Clutter Summary - gives breakdown of error based on clutter type. The clutter summaryprovides clutter wise distribution of mean error and standard deviation. This particular table isvery useful to help tune clutter parameters.

Example Clutter Summary

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

Analysis Tab to generateInitial Statistics

Display Mode only valid forBin Info Report


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Forest 13 -13.0 14.5 6.8 0.9117

OpenLand 373 -20.8 23.2 10.3 0.7280

LowDensityBuilding 281 -8.6 10.7 6.4 0.8976

MediumDensityBuilding 204 -13.0 15.8 9.0 0.7547

Transportation 52 -27.0 27.8 6.9 0.2984

Num. Bins - The number of RSSI samples within the sector file, broken down by clutter class.

Mean Error - the calculated mean error between the measured and predicted values. A negative value indicates themodel is underpredicting.

RMS Error - the root-mean-squared error. Generally a measure of the 'spread' of the error between the measured

and predicted values.

Std. Dev. Error - the classic measure of 'goodness' in model tuning. It is more a measure of the 'magnitude' of error

between the measured and predicted values.

Correlation Coefficient - between 1.0 and -1.0, it is a statistical measure of degree of linear relationship between the

measured and predicted values, or how well the sample points fit the model curve. The higher the value, the betterthe relationship. A value of 0.7 is typical.

These reports are useful to help tune your model and guide parameter changes. Error values(high or low) are not relevant with a small sample size (i.e., less than 200-300 pts.)

Autotune ButtonOnce Header files are loaded, when the Autotune button is selected, a Model Calibration Utilitywindow will appear. In the Status Log, the data files will load individually and the tool will computeInitial Statistics based on the selected model chosen in under 'Options'.

Status Log

Initial Statistics


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NotesInsert pic of model parms window.

Discuss use of Height Profiler tool in analysis


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Preparing the DataFile ScreeningFile Screening refers reviewing a sector file with Asset and making a subjective call as toacceptance or rejection of the data set.

As a first step, each drive file must be screened. Individual sector files are loaded and inspectedin the ASSET map window. Screening of individual sectors is performed to check for anomaliessuch as possible blocked antenna or errors in the site database such as:

Incorrect antenna orientation

Excessive downtilt (greater than 10 deg for a very narrow (less than 4 deg BW) antenna

Low antenna height (less than 10 meters, but dependent on cluster average

Low EIRP (less than 33 dBm)

Low number of data points (less than 300 samples)

The Sectors that are discarded are summarized on a Mortality List, with specific reasons andrecommendations are made based on them.

Example of a Failed Sector Azimuth Error:

The site data indicates an azimuth of 340 degrees, but the plot shows very little correlation. Thissample set may be discarded and added to a mortality list. A suggestion was made to the marketto check for a possible sector cabling and/or verify antenna azimuth.


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Data FilteringAfter screening each sector file and accepting the data, the sample file needs to be filtered toremove data points that are either unreliable or not desirable for model tuning. These include data

points that are within a certain radius of the antenna, beyond a certain radius of the antenna, datapoints that have RSSI less than a specified power, and data points that have a RSSI that isconsidered to be weaker than the noise floor of the scanner.

The filtering process aids in excluding data points that lie outside the linear region of the amplifierof scanner and hence the propagation path. The values for power levels and distances are largelybased on equipment specs and site specs respectively.

Other filtering options can be applied based on Line-of-sight (LOS) or Non Line-of-sight (NLOS)data points. The filtering is based on terrain data, but can also take into consideration buildingvector heights and clutter heights if they are assigned. This filtering is used to compute the effectof diffraction.

Exclude ClutterThis removes samples based on clutter type. Often, clutter types with an insufficient number ofsamples (for reliability reasons) may also be excluded from analysis. This is done by selectingthose clutter types from analysis in the filter window.

Antenna Beamwidth FilteringWhen using live-system or BCCH drive data, or when using directional antennas, it is necessaryto filter datapoints outside of the main antenna beamwidth. This removes the sample pointsoutside of the calculated 3dB beamwidth

1of the antenna, as inclusion of these points will distort

1The beamwidth is determined by ASSET by reading in the antenna pattern and cannot be altered or changed by thelabel in the antenna pattern file.

Removes samples based on signalstrength. Values shown are typical.

Removes samples outside of givendistances. Values vary givenmorphology, site height and terrain.

Removes either LOS or Non-LOSsamples. Based on terrain data only.Useful for evaluation of K7.

Removes samples outside of antennabeamwidth. Checked when usingdirectional antennas.


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the model as there will be a wide spread of signal values vs. distance. The influence on K1 andK2 may be substantial, as there will be a wider spread of sample points relative to distance fromthe site.

See example graph.

Plot of unfiltered drive data:

Antenna Azi = 340


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Plot of data file with 3dB Antenna filter

Options/Model TabMultiple map resolutions

ASSET can have Digital maps with more than one resolution (typical 25m and 100m or 30m and90m). Since Model Calibration is done based on bin by bin basis, selection of the Map Resolutionis needed.

Map Resolution at whichModel is to be tuned

Model to be tuned. See'Adding a Base Model'


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Hint: If there is only a single Map Resolution, then that resolution is default, otherwise, a selection needs to be made.Choose a Map with a higher resolution, so as to produce a more finely tuned models, but if there is drive samples in lower

resolution bins, these will not be included in the Analysis.


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Adding a Base ModelAsset Default Model

After setup of the Autotuner and Filtering Options, the user must define a 'default' model. Thedefault will be renamed after tuning to according to market requirements and categorization of thesample files. This is usually based on build-up, with each market defining its morphology classessuch as Dense Urban, Urban, Suburban, Rural.

The Default model contains all the standard (untuned) values in the model, such as frequency,Effective Antenna Height Algorithm, the Diffraction Methodology, etc. These are seen under'Configuration, -> 'Propagation Models'.

Note: The 'Macrocell 3 Model' is used as a base model with its defaults for 1900 MHz. For amodel in the PCS band, the frequency is set to 1920 MHz. The Effective Base Station AntennaHeightalgorithm used is the Relative algorithm (this is the calculated height between the base

station antenna and the mobile antenna and is the most accurate representation). Diffraction Lossis calculated using Epstein-Peterson method without merging any of knife-edges along the pathof the terrain database.

Macrocell 3 Model Defaults

K1 K2 K3 K4 K5 K6 K7

160.00 40.00 -2.55 0.00 -13.82 -6.55 0.80

K1 (near) = 0 K2 (near) = 0 D =0.0 km

Effective Antenna Height Algorithm Relative

Diffraction Loss Calculation Method Epstein Peterson

Mobile Antenna Height (m) 1.5

Clutter Types Through Clutter Loss (dB/km) Clutter Offset (dB)

Water -6.0 0.0

Forest 6.0 0.0

Open 0.0 0.0

Low Density Building 3.0 0.0

Med Density Building 6.0 0.0

High Density Building 9.0 0.0

Major Transportation -3.0 0.0

Airport -3.0 0.0

Through Clutter Loss Distance (m) 800


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General Tab

Path Loss Tab

Two-piece

model parms


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Clutter Tab


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Tuning the ModelSetup of AutoTuner ParmsWithout setting limits for the tool, it will return results that provide statistical merit, but are notnecessarily engineering sound.

Initial parameter values, iteration limits, Delta Ranges (which limit the change it can make to aparticular parameter), and the Fixor locking of other parameters which the user does not wantchanged during the auto routine need to be initialized. These may be narrowed as the userprogresses towards a final value.

For the initial setup of Optiimiser Parameters:

Max Iterations - 100

Conv. Accuracy - 0.001

For Delta Ranges of K-parameters

K2 Delta Range - 1.0, changing to 0.1 when narrowing.

K7 Delta Range - 0.01

Zero all Through Clutter Settings and Fix

Fix K5 to K7 to default

Do not change K3 or K4 from default value

Lock Values

Iteration Limiter

Value Limiter


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Initializing K1 and K2The first step in model calibration is determining initial (or base) values of K1 and K2. The

thinking at this point is to determine a best fit line for the most likely mobile location (dominantclutter type). K1 will be changed at several points during the tuning process, but should beadjusted periodically to prevent other parameters from deviating too far from final value.

Load Header files for a Single Morphology Type

Determine most significant clutter type for given morphology

Apply Autotuner defaults (given above)

Apply Changes and record initial error values

Turn off all clutter except that which is determined most dominant and Analyse. The Autotuner willreturn an initial value for K2 and K1. If the value for K2 is reasonable, then commit the values andcontinue. Remember these values are just preliminary, and further tweaking will be necessary atthe end of the calibration process.

Hint: K1 always zeros the mean error. When mean error is positive, the model is underpredicting compared to the drivedata and K1 should be reduced. When mean error is negative, the opposite applies.

Tuning for K7The next step is tuning for diffraction loss and shadowing effects caused by the terrain. In urbanor flat terrain areas, this may not be a significant factor, but must be investigated. K7 is amultiplying factor that alters the impact of the diffraction loss and its value is always less than 1.0

To assess the effect of the diffraction effect, data points with Non-LOS with the transmittingantenna are chosen. This is done be deselecting the LOS data points in the filter options.

The Non-LOS data is then auto-tuned to return a value for K7 alone, by locking ALL otherparameters in the Auto-Tune Module.

Uncheck LOSCheck Non-LOS


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The Delta value for K7 can be set to 0.01, and the Iteration limits to 100. If the returned value forK7 is acceptable, the value is manually applied to the model.

To check the effects of the change in K7 on the overall model, the data points with LOS areincluded and the analysis is rerun. A change in the standard deviation, and or a change in meanerror and correlation coefficient are observed. If the statistics show improvement, then thechanges are committed.

Note: The change in K7 value may result increase mean error in the analysis report. Do not worryabout changing K1 until later in the process as other changes are still necessary. The mean erroris zeroed out as a final step of model calibration.

Tuning K3 - K6Model coefficients K3 K6 are constants which alter the effect of the BS Effective Height Gainand the MS Antenna Gain.

K3 and K4 are used to modify the effect of the mobile antenna height on the received signalstrength. In most mobile networks, the mobile height is considered to be fixed at 1.5m above theterrain height. The default values for 1900MHz systems are K3 = -2.55 and K4 = 0.00. Thesevalues are not altered when model tuning.

K5 and K6 are used to modify the effect of the base station antenna height gain on the receivedsignal strength. Since the Effective Height Algorithm used is the Relative Method, the effect of theterrain data is more prominent than the absolute base station antenna height. The default valuesfor K5 and K6 are hence not generally altered.

Locked Parameters


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Tuning for ClutterClutter Thru-LossWhen using high-resolution clutter data, a more accurate model can be developed utilizing theThru-Loss algorithm within the Macrocell 3 model. A reduction in std. dev of 1-2 dB can usuallybe achieved if applied properly.

Tuning for Thru-Loss, like the model constants, is partly a manual and iterative process, but theAutotuner can help the user make initial assignments. As with other model parameters, the usermust help guide the autotune process through use of range deltas and fixing of parameters. Aftera sanity check of values and noting cause and effect the values can be applied.

Review the Clutter Summary to get the number of sample points used to make an assignment bythe Autotuner. Some clutter types will have a very low number of samples and will need somemanipulation by the user. Clutter types with a high number of samples are generally reasonablefor assignment. Use these values to help guilde manual assignment to the ones with few sample

points, as there should be a trend in the values.

Lastly, round-up or down values to maintain simplicity (ex: 5.94 to 6.0). Use the table below forsanity checking of assignments. It will vary slightly from model to model, but will maintain a trendas mentioned (values will be higher for an urban, or more built-up area, and lower for a more ruralor open area).

Clutter Default Values

K1 K2 K3 K4 K5 K6 K7

160.00 40.00 -2.55 0.00 -13.82 -6.55 0.80

K1 (near) = 0 K2 (near) = 0 D =0.0 km

Effective Antenna Height Algorithm RelativeDiffraction Loss Calculation Method Epstein Peterson

Mobile Antenna Height (m) 1.5

Clutter Types Through Clutter Loss (dB/km) Clutter Offset (dB)

Water -6.0 0.0

Forest 6.0 0.0

Open 0.0 0.0

Low Density Building 3.0 0.0

Med Density Building 6.0 0.0

High Density Building 9.0 0.0

Major Transportation -3.0 0.0

Airport -3.0 0.0

Through Clutter Loss Distance (m) 800

Once all the Thru-Loss assignments have been made, Thru-Loss Distance is examined. Thru-Loss Distance is based on morphology, but is also influenced by the average antenna height.Typical values for Thru-Loss Distance range from 500 to 1000 meters. Use the Autotuner resultsfor initial guidance and finalize based on error results. Finally, tweak the Thru-Loss values byexamining the Clutter Summary.


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Clutter OffsetsLastly, clutter offsets are assigned. Unlike Thru-Loss, Clutter offsets have no trend and will oftenbe very close to zero when using Thru-Loss. This is a final offset made by the Autotuner to

reduce the mean error, but like above, is meaningless for clutter types with very few samplepoints. Use these returned values with the same discretion as all other values.

Clutter Offsets are end-point offsets associated with each clutter type. Clutter Offsets are basedon statistical analysis that makes the final adjustments to the Through Clutter Loss Slope/Intercept model. This value is used as a balancing mechanism to minimize the mean error.Hence, it values may not appear to be intuitive or follow the trend of values for Through ClutterLoss. Clutter offsets work best to characterize Oceans, Lakes, and Rivers (or Water). Anassignment with deviates from the Autotuner value is most often required, as it will mis-characterize the cross-water effect. A value of -6.0 dB is typical.

Final Tuning of K1, K2 and Clutter OffsetsAfter all the Thru-Loss values and Thru-Clutter Distance are tweaked, finalized, and locked down,

final adjustments need to be made to K1 and K2. Repeating the process for the initialadjustments of K1 and K2 returns the final values. This ensures a null mean error and null cluttermean errors, for the best slope possible.

In very few cases does K2 require a change. It is more likely if the thru-loss of the dominantclutters was changed greatly in the above and the distribution of those changes (positively ornegatively).

Because clutter offsets cannot be fixed (or locked), the Autotuner assigns or updates them eachtime. The offsets will not be valid for clutter types that have very few data points for a statisticallyreasonable assignment. In these cases Clutter Offset must be manually assigned and reviewedbased on the trend shown.


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Error AnalysisDetermining if your model is sound and reasonable is difficult. What if your results give a standarddeviation greater than 8 dB? Where to you look for errors? What if none are found?

If error values are not acceptable, consider possibility of a two-piece model.

Eventually you will have to stop your analysis as every possible parameter will have beentweaked and modified.

Effective Height Algorithm Select a different effective height algorithm and recalculate the K5and K6 parameters.

Diffraction

Choose a different diffraction algorithm and retune the diffraction parameter (k7).Also investigate merging knife-edges. The Height Profile window and the drive test signal andsignal error on the Map View provide valuable visual aids to identifying possible areas wheremerging may be required and by how much.

Other parameters that may be changed are Clutter Heights, Separation and Mobile Heights.Adding clutter heights a separation value (must be > 0) can be of occassional aid when modelingurban environments. Clutter Separation has the effect of modeling the 'urban canyon' situation ofa mobile being at street level. Lastly, mobile height models the situation of the mobile being at aspecified height within the clutter.


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Comparison TestUsing comparison plots to change the model parameters is difficult, but is effective to developmore reliable models. Changes are made manually, the results noted and its effect of other

parameters. If the changes are reasonable to other tests and show an improvement in statistics,the changes are committed.

Example of a Comparison Plot:

Hint: When producing a comparison plot, after you have determined the number and signal level for the respective bands,it is convenient to represent the bands with a color that is a lighter shade of that used in the drive test. This helps make

the comparison more intuitive and easier to visualize.


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Relative Parameter TestAre the individual K parms and Thru-Loss values reasonable? How do the value compare frommodel to model. It is reasonable to assume K1 and K2 are larger for urban environments than

rural environments. Does this trend hold?

Error TestOverall, mean error, RMS error, and std dev are used in regression analysis to quantify theresults. Individually, the results are not significant, but have depth when viewed collectively.

Mean ErrorIn all cases, overall mean error should be, or very close, to zero. When not, it gives indication themodel is overpredicting or underpredicting cell coverage. In most cases it will range from +15 to -15 when viewed on a per cell basis. If a cell mean error is significant, for example -20, it mayindicate an operational problem with the cell and the site should be removed from the analysislist. Also, the morphology of the cell could be mis-classified compared to the other cells and it is

just not a good fit.

Std. Dev. and RMS ErrorStd. Dev and RMS error are almost the same and it is usually user preference on which one ismost important.

Error stats alone are not sufficient, as 8dB std dev may be impossible given environment andnumber of sites/sample points. If you look at the site statistics, you will see some sites above 8dBand some that are below.

Modeling error can be broken down into two parts, the error due to signal fading and the humanerror tied to the accuracy of the databases used to model the source of the signal.

Single Slope Model vs. Dual Slope Model

Sometimes it is more appropriate to model the data distribution with a 2-piece model. A two-piecewill fill in coverage near the site if the drive data shows this trend and occasionally, can improveerror results (1 to 2 dB Std.) It is applicable for rural environments, as man-made reflections maskthis in urban settings.

The characteristics of the radio propagation differ at the near-end and the far-end of the site. Thismodel has a second K1 and K2, which serve to characterize near antenna coverage, and then,after some breakpoint distance, trends a line with a shallower K2 value. This is demonstrated inthe picture below.


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Two Part Models

Distance from Base Station

R

eceiveLevel

Intercept 1Slope 1

Intercept 2

Slope 2

Break Point

Hint: For the above graph to be theoretically valid, K1near has to be less than K1far, and K2near has to be greater (3-5dB) than K2far.

There is no clear way to tell if the drive data is single or dual-slope other than close visual andanalytical inspection of the data. Start with a single slope model and if it does not give the resultsdesired, a two-piece should be investigated. The breakpoint distance is best determined by

inspection of the drive data for a good number of sectors under test. Most often it is seen that thebreak point distance is between 1.5 and 2 km for typical cells, however it will vary based onantenna height, EIRP, and morphology class.

Developing a 2-Piece ModelA base model may be retuned to achieve the desired error statistics and at the same timeconcentrating on a best fit between the drive data and the propagation at the far-end of the site.Having calculated the various K values, Clutter Thru-Loss and Clutter Offsets, proceed todevelop a model for the near-end by just tweaking K1 and K2 values by specifiying K1 (near) andK2 (near) to achieve a best f it for the near-end of the site. The near-end of the site is determinedby a factor called break point distance (D).

Analyze data on whol as normal, and come to stopping point based on final error stats.

Break data into two parts, near and far.

Filter the data on breakpoint distance. Approximate using 4H1H2/lamba2 Analyze near field data to obtain K1 and K2 (near).

Hint: It also important to develop smooth transition from the near end to the far end. There should be no abrupt changes insignal level and 'feathering' of the transition must be taken to ensure satisfaction with coverage plot. Most often this is

apparent when K2near and K2far get too far apart (>10dB typical). To smooth, slight adjustments to K1 and K2 may beneeded after inspection.

Sources of ErrorThe RF environment is very strange behaving at times. Direct and ground-reflected waves, aswell as reflections from buildings all impinge upon the mobile and produce a signal that is widely


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varying. Diffraction, shadowing, tunneling, and cross-water effects contribute as well. Incombination is the speed of the mobile and its movement across a sector face, with varying gainand an you can produce a incredibly varying random environment.

Fortunately, this randomness follows some sort of order and can be quantified by regressionanalysis and statistics. The 8dB figure mentioned above is a starting point, but it attempts toidentify the randomness described as well as the FIT of a slope/intercept line across spacecontaining the randomness. In some cases, this is very difficult, especially in areas of denseurban buildup.

Database control is obviously critical. Sanity checks of system info and databases used in modeltuning can be provided at key intervals, but some errors go unidentified. All databases (site,antenna, channelization, land-use and terrain) are possible sources of error. Using site locationswith antennas that are unobstructed in their near-field is essential. All these factors add up toincreasing the error of the model but are generally averaged out if enough sites and samples areobserved and added to the sample set.

ConclusionModel tuning is an iterative process that requires time and patience, but most importantly - adeliberate approach. This document attempts to give one such approach that the authors havefound successful. The overall strategy to maintain, regardless of approach is to find a middlepoint and then apply successive tweaking, trying to improve the results. Check your results, andtweak again. If you go off course, then you revert back to a known good result and try again, thistime in another direction or with another parameter.

This document has tried to mention, if not discuss, every parameter available for model tuning,and give some insight on how best to apply it.

Model tuning is part science, part art. The science is knowledge of radio behavior and statisticalmerits. The art comes with adjustment of some parms and seeing their effect on others. Trial anderror is the only way to become adept.


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Page 29

APPENDIX 1 - About the ASSET Macrocell 3 Model

IntroductionThe form and parameters of the base Macrocell 3 model is based on the ETSI-Hata/COST231 model with a few additional features incorporatingalgorithms for diffraction loss and effective base station heights. It also provides an accurate antenna masking process through interpolation andquantization of the antenna mask.

The base Macrocell 3 slope/intercept model is of the form:

Ploss = K1 + K2*log(d) + K3*(Hms) +K4*log(Hms) + K5*log(Heff) +K6*log(Heff)log(d) +K7*Diffn + Closs

Where Ploss is Pathloss, Hms is mobile station height, Heff is base station effective height, Diffn is diffraction loss, and Closs is clutter loss.Distance (d) is in kilometers.

K1 is the intercept and is thought of as the amount of pathloss encountered at the 1km point.

K2 is the slope in dB/dec and takes a range between 30 and 40.

K3 and K4 are modifiers to the gain effect seen when the mobile antenna is near the ground. K4 is typically zero, however.

K5 and K6 are modifiers to the effective height gain of the base station.

K7 is a modifier to the calculated diffraction loss, and is usually less than 1.0

Each clutter can also be assigned an associated Thru-loss in dB/km and is used in conjunction with a Thru-Loss Distance. A clutter-offsetparameter is utilized as a final adjustment to minimize the mean error associated with a clutter type. Other parameters associated with clutter areclutter heights and separation (average distance from obstruction to mobile). See below for a detailed explanation about Thru-Loss algorithm.

Effective HeightThere are four Effective Antenna Height Algorithms within ASSET, each suited to different terrain and network characteristics.

The Absolute method is not widely used in cellular networks but is in certain broadcast systems.

The Average method works well in flat or gently rolling terrain.

The Relative method works well in rolling-hilly terrain where the base station is normally above the mobile. The Slope method works well in hilly and severely hilly areas where the other algorithms consistently over-estimate the Heff.

DiffractionThe diffraction algorithm determines how a loss figure is calculated when multiple knife-edges are detected along the terrain profile from basestation to mobile. There are four methods within ASSET:

Epstein-Peterson - the loss from each knife edge is calculated and then summed together.


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For Internal Use Only Preliminary v1.0 Page 30 Page 30

Bullington - this method replaces the real terrain with a single knife edge.

Deygout - loss is calculated relative to the main obstruction. Japanese Atlas - similar to Epstein-Peterson, but height of transmitter is altered.

Hint: Typically, Epstein-Peterson or Bullington are the most popular, but is user-preferenced. When using high-resolution terrain, merge knife-edges less than zero.

Appendix B - More on Clutter Thru-Loss and Thru-Loss DistanceClutter offsets are a fixed end point correction factor that improves the correlation between measured and predicted pathloss. This improves theStandard Deviation appreciably, but takes into consideration only the clutter type that the pathloss is being computed for and doesnt take into

consideration the loss due to the different clutter types in the path of propagation.

Through Clutter Loss is the additional loss attributed to the clutter type that the signal propagates through. The total thru-loss for a prediction pointis calculated by examining the clutter lying between the mobile towards the base over a defined distance, the Through Loss distance, d. Whencalculating thru-loss, the individual bins are weighted linearly so that the ones closest to the mobile have greatest effect, and the ones at point dhave a minimum.

The value of Through Clutter Loss would vary for different environments, and depends largely on the clutter through which the signal has alreadypassed through. The effects of the clutter type in the path tend to have a residual effect on the value of the Through Clutter Loss parameter. It isnot a quantitative measure of the additive loss associated with the clutter type, but rather represents a value that could shape the straight linebetter in order to fit the measured data, and hence may not be intuitively assigned or predicted.

Through Clutter Distance represents the distance from the mobile towards the base station, through which the signal penetrates through theclutter. The remaining distance, the signal is assumed to propagate above the clutter. The Through Clutter Loss and Distance algorithm works asfollows:

Through Clutter Loss is added to the computed pathloss after applying a weighing factor.

The weighting is linearly applied, with a weighting factor of 1.0 for the bin closest to the Mobile Antenna and a weighting factor of 0.0 at thebin that is at a distance defined by Through Clutter Distance.

The Clutter Offset is used as an end point correction factor to balance the effect of Through Clutter Loss in order to minimize the meanerror.

Though Clutter algorithm provides a smooth transition, or averaging effect of patloss between clutter areas. For example, consider a water edgenext to a tree line. The loss does not jump from 4dB to -6dB immediately, but gradually decreases bin-by-bin. Through-Clutter models this moreaccurately than offset effects. It is similar to path profile algorithms found in other propagation tools.


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For Internal Use Only Preliminary v1.0 Page 31 Page 31

The following example illustrates the operation of Clutter Through Loss correction. Assume the following:Bin Size: 100x100 metersThrough Clutter Distance: 1000 metersTotal Through Clutter Loss correction = 0 + 0.06 + 0.12 + 0.3 + 0.4 + 0.5 + 0.36 + 0.42 + 0 .48 + 0.9 + 1.0

= 4.54 dB.

TX

Mobile

0. 0.2 0.3

Loss= 6 dB/ km * 100/1000 mts * 0.7 = 0.42 dB

Weight0.4 0.5 0.6 0.7 0.8 0.9 1.0

F F F B B B F F F B B Clutter Type

F = Forest (6 dB/km)

B = Buildings (10 dB/km)

Through Clutter Distance = 1km

0.06 0.12 0.3 0.4 0.5 0.36 0.42 0.48 0.9 1.0 Through Loss


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Glossary of Terms

Bins - the mapping resolution of the tool, usually 25x25 meters. The smallest measurable unit

in the modeling tool. Several sample points could be located in a bin.

CW Measurements - a generic term used to describe propagation testing done with anunmodulated carrier, supported from a temporary TX facility, i.e., usually low power andomni transmit.

Filtering - removal of select data points within a sector file.

Intercept/Slope - the basic form of the prediction model, defined by the equation K1 +K2*log(d).

Mean Error - a statistical measure of the average error calculated by examining thedifferences between the predicted and measured values. It should be very close to zero in

most cases.

Morphology - a general description for the area in which test sites are grouped, based on thesurrounding buildup or concentration of land-usage. For example, Suburban - Hilly or Rural -Coastal.

Mortality List - test locations that have been rejected for analysis due to discovery ofdatabase error, lack of samples, or unreasonable coverage footprint.

RMS Error (Root-Mean-Square) - A statistical measure of the spread of the error around themean value calculated through examining the difference between the predicted andmeasured values. Its value should be close to the Standard Deviation.

Sample Points - an individual measurement point derived for a particular channel, forexample: "The sector file is made up of 600 sample points."

Screening - review of sector files on the whole and determining acceptance or rejection of thefile for modeling.

Standard Deviation (Std Dev) - The most common statistical measure of model error. Mostoften stated as 8 dB. The Std Dev is similar to RMS error, and the two are identical when themean error is zero.

Two-Piece Model - a pathloss model with 2 distinct profiles separated at a break pointdistance. Identified by separate K1 and K2 values (near and far). Numerically, K2-near ishigher close to the cell.

Tweaking - making a small change to the model and examining results.


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Quik Guide for ASSET Model Tuning

To calibrate a macrocell model, perform these initial steps:

Inspect the drive test data to verify its validity and filter out any erroneous data.

Ensure that sufficient data points are available for each clutter class. In most situations it is desirable forthe data to be evenly distributed with respect to log (distance) from the site, clutter classes and thesectors.

Enter a set of default values as an initial step.

Rough Calibration of the Standard Macrocell Model

Having performed the initial recommended steps, use these recommended steps as a guide to roughlycalibrating the standard macrocell model:

Load one or more drive test files and use the filtering to remove questionable data and get an unbiaseddata set. For example, filter out readings with a signal level below the noise floor or clutter types withtoo little data to be statistically meaningful.

Derive a estimate of Slope Value (K2) from a plot of the Received Level vs. the 10 log (distance) usingthe Measurement Graph facility. Then fine tune this value.

Adjust the k1 parameter to a value, which will lower the mean error to 0. When the analysis reportshows a positive mean error, it means the propagation model is pessimistic when compared to the drivetest data by the reported value. In this case, you should lower the k1 value by the reported amount.Where a negative value is reported, the opposite applies.

Diffraction effects (k7) occur only when there is no Line of Sight from the site to the mobile. Therefore todetermine the k7 parameter, filter the dataset to include only the non-LOS and a value determined using

the process described in the above section. As a rule of thumb if the mean error is greater than 0,decrease k7 otherwise increase it.

Modify the filter to its original setting (to include LOS data as well in the analysis).

Readjust the k1 value if the reported mean in the analysis report has increased or decreased after thek7 change.

Adjust the k6 value, again using the process in the above section. It is useful to view the graphs and theSignal Error plot on the Map View to identify trends with successive parameter changes.

Readjust the k1 value if the reported mean in the analysis report has increased or decreased after thek7 change.

Adjust each clutter offset in turn trying to get the mean error of that particular clutter to 0.

Modify the k3, k4 and k5 parameters until the reported error is lowered.

Now you can fine tune the model.

Fine Tuning the Standard Macrocell Model

When you have performed the initial and rough tuning steps, use these recommended guidelines when finetuning the standard macrocell model. The objective is to identify what may be causing the differencesbetween the propagation model and the actual drive test data and act on minimizing the error. Use theanalysis, filtering and graph features to help you. Investigate:


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Effective Height Algorithm Select a different effective height algorithm and recalculate the k5 and k6parameters.

Diffraction

Choose a different diffraction algorithm and retune the diffraction parameter (k7). Alsoinvestigate merging knife-edges. The Height Profile window and the drive test signal and signal error onthe Map View provide valuable visual aids to identifying possible areas where merging may be requiredand by how much.

Two-Slope Model Define alternate values for the intercept (k1) and the slope parameter (k2) to beused for a defined radius from the antenna. Typically a higher slope is used close to the antenna and ashallower slope further away.

Inspect the survey data and use the graphs and the drive test data Signal and Signal Error displays onthe Map View to determine where the breakpoint (d) may be.

When a breakpoint distance has been found, calculate k1(near) and k2(near) in the same way as k1and k2 but only using a subset the survey readings which have a distance of 0) can be of aid when modelling urban environments. Theclutter separation has the effect of modelling the urban canyon situation of a mobile being at streetlevel. Finally mobile height models the situation of the mobile being at the specified height for theparticular clutter.

Model Tuning Radio

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