MT Presentation

Propagation Model Calibration-2G (900/1800 Urban & Suburban)

Ranjit Kumar Karna

10 April 2023 Graphical Guidelines for PowerPoint2

Purpose and Output

• To produce calibrated propagation model for use in Aircom Enterprise Asset 6.2• The Models are for use in Radio Network Planning in GSM 900/1800 MHz.• The use of calibrated models provides assurance in the choice of transmitting sites and expected signal

coverage and calculation of interference.

• Four Propagation Models will be produced after Model Tuning Process

Standard Macrocell type 3, clutter type – Suburban, frequency band 1800MHz.

Standard Macrocell type 3, clutter type – Urban, frequency band 1800MHz.

Standard Macrocell type 3, clutter type – Suburban, frequency band 900MHz.

Standard Macrocell type 3, clutter type – Urban, frequency band 900MHz.


Model Tuning Process

• At Ncell, previous Propagation Models were based on the theoretical formulations only.• This tuned Prop Model is based on the actual measurement carried out in Urban and Suburban Clutters in

Kathmandu.

• In the fulfillment of the main objectives set out by Ncell RNP&O, MT procedure has undertaken the following tasks:

Clutter Analysis, Modal Type Analysis and Modal Sites Selection.

Collection of Drive data from modal sites selected by Operator(Ncell)-export to .FMT

Drive data conversion into .hd (header) and .dat (measurement).

Model calibration Urban & Suburban for 900/1800MHz.

Prediction analysis using the new models with the Drive-Test Plot at non-Modal sites.

Production of a report to describe the propagation model and processes.

Aircom Enterprise Accet 6.2,a radio network planning tool, includes advanced survey analysis & processing capabilities, which have been utilized in the development of a generic model from the survey data. The models highlight the dependency of the received signal level on the base station heights, the distance from the base stations, frequency, terrain and the effect of local clutter. Topographical terrain and clutter databases of 5m meters resolution, available at Ncell, have been employed in this process. The clutter database comprises of xx clutter categories. The data processing and model tuning was carried out in Aircom Enterprise Accet 6.2 at Ncell office .

Field Measurements

File Processing

File Import

Filtering

Calibration


Clutter and Modal Type Analysis

Clutter Type Analysis:

Location: Kathmandu (Two Regions Inside Ring Road: Mostly Urban, Outside Ring Road: Mostly Suburban and Villages)

Common Clutter Sub-Types: Urban: Low, Medium & Dense Urban; Suburban: Suburban & Village

Capacity of Local Network: >250 sites

Operational Band: 900, 1800, 2100 MHz

Cell Type: Macrocell

Modal Sites Selection:

Sites selected by Ncell Engineers have been used to develop generic models, which utilize detailed topographical terrain height and clutter database. The sites for the model calibration process were selected with the help of local Ncell Engineers due to their extensive knowledge of the local terrain and morphology. The sites chosen in each morphology were meant to be representative of the antenna heights so as to enable good modeling of antenna height gain. The number of sites selected for each class would ensure that sufficient data points would be collected to calibrate the models.

Modal Type Analysis: Recommended usage • Sites in environments where the distance from the site is greater than approximately 500m • Base station antenna heights in the range of 15-200m • Receiver heights in the range of 1-10m Mapping data needed • Terrain DTM height – raster data • Terrain clutter – raster data


Survey Measurement Methodology

Modal Sites Selection:

Sites selected (by Ncell Engineers) have been used to develop generic models, which utilize detailed topographical terrain height and clutter database. The sites for the model calibration process were selected with the help of local Ncell Engineers due to their extensive knowledge of the local terrain and morphology. The sites chosen in each morphology were meant to be representative of the antenna heights so as to enable good modeling of antenna height gain.

The number of sites selected for each class would ensure that sufficient data points would be collected to calibrate the models.

Clutter Type Cell ID

SuburbanKAT018CZ KAT037AX KAT043AX KAT064AX KAT083CZ

KAT089BY KAT096AX KAT100BY KAT194CZ KAT210CZ

UrbanKAT002CZ KAT013AX KAT022AX KAT045BY KAT065AX

KAT075AX KAT108AX KAT112CZ KAT118AX,CZ KAT122AX



Selected Sites Location : Suburban and Urban

Suburban

Urban



Route Selection

After all the sites were selected, survey(DT) routes for each cell were identified. The measurement routes were selected to provide a mixture of main roads and side roads tangential and arbitrary orientation with respect to the selected cell and the particular antenna configuration of each sector.

Although all drive-test routes were pre-defined for a maximum radius of about 3-4 km, drive-test teams were instructed to continue driving further away from the site if signal strength had not fallen below level of -110dBm. This was done to ensure that the propagation model can accurately predict far-field path-loss.

Transmitter Configuration

Normally, for model tuning, a low gain omni-directional antenna is used, but in our case we were using sectorized antenna Andrew DBXLH-6565B-VTM. Mechanical and electrical tilts were set to 0 in order to extend coverage area and to decrease impact of the tilt evaluation. After drive-tests they were set to initial positions.

• Recommendations: During drive-test,

(1) BCCH of testing cells was set out of common list in order to prevent the impact of interference from other cells.

(2) Disable Hopping( BBH/SFH)

(3) Disable Power Control


Survey Measurement MethodologySurvey Data Conversion, Filtering and Processing

Drive-Test/Survey Data Export to (.FMT): Open the export (.FMT) in excel which has Time, MS, Frame Number, Location (Longitude/Latitude) and RxLevel.

• Remove RSS less than -110 and higher than -40dBm from Excel

• Now Keep only three columns in excel as Longitude, Latitude and RxLevel

• Modify the excel as: Add first “RxLevel” by string “ start” in 1st Row and similar for last row with “finish”. Save this file in Notepad with extension as (.dat) as measurement file. Then do it for all (.fmt) exports for each cell.

• Remember: only one (.FMT) per cell.

• Create header files (.hd ) including (.dat) file names and according to site database within it for each cell as attached.

• Now we have two files (.dat & .hd) for each cell.

• Load them into Asset (measurement files) and better to Associate them.

Example_KAT018C.dat

Example_KAT018C.hd



Filtering due to Signal Strength: All samples with RSS less than -110dBm should not be considered during calculation, while they are affected by noise, readings with RSS higher than -40dBm should be removed due to saturation as well.

• The measurements should exactly match the map data, including clutter and road vectors.

• To avoid successive readings from same location (or at locations that are not further away), the measurements should be re-sampled using averaging distance 1m.

Filtering to Adjust to Digital Map: During the model calibration process two types of an assessment the model accuracy were used – numerical, based on errors between measured and predicted values and visual comparison of overlapping layers of the coverage and drive-test data.

• In order to ease visual assessment, the same legend of signal strength will be used for coverage prediction and measurements. Such limits are used to consider only sufficient readings.

• The legend used as



Filtering to Adjust to Digital Map: contd..

• Survey/Recorded Data Map with Map Data:

Before After

The layer with recorded CW data is not matching with map data. The significant errors of the prediction will occur in this case. So, in order to fit survey data to map, the coordinates of each sample were adjusted – Longitude + 0.0001 (DLL); Latitude + 0.00003 (DLL).



Survey Data Filtering - Distance Evaluation In order to avoid the saturation of signal, all CW measurements near the site should be cancelled from the tuning process. Following figures show distribution/regression of the samples against distance from site for each model separately.

Urban 1800

Sub Urban 1800

Sub Urban

900

Urban 900



Survey Data Filtering – Loading into Tool and Averaging :

In order to get an accurate prediction the data headers of log-files should be prepared in correct way, so our readings will be associated with the existing site database.

Below is the measurement loading to Aircom Asset ( Suburban, 1800 MHz), so as other models can be done.



Survey Data Filtering – Loading into Tool and Averaging :

Now it is needed to average the recorded samples for exclusion unnecessary point. It could be done in the tool.

Measurements averaging into Aircom Asset (1m).

Filtered out, averaged and map-adjusted data have been successfully loaded into Aircom Asset. Now drive-test/survey data is ready to be used in the calibration process.


Calibration Process Path Loss Definition and Objectives

• Path Loss Formula

PL (dB) = K1 + K2*log(d) + K3(Hms) + K4*log(Hms) + K5*log(Heff) +

+ K6*log(Heff) *log(d) + k7(diff) + Clutter_loss

D - Distance from the base station to the mobile station (km).

Hms - Height of the mobile station above ground (m). This figure may be specified either globally or for individual clutter categories.

Heff - Effective base station antenna height (m).

Diff - Diffraction loss calculated using either the Epstein-Peterson, Bullington, Deygout or Japanese Atlas knife edge techniques.

K1 and K2 - Intercept and Slope. These factors correspond to a constant offset (in dB) and a multiplying factor for the log of the distance between the base station and mobile.

K3 - Mobile Antenna Height Factor. Correction factor used to take into account the effective mobile antenna height.

K4 - Multiplying factor for Hms.

K5 - Effective Antenna Height Gain. This is the multiplying factor for the log of the effective antenna height.

K6 - Multiplying factor for log(Heff)log(d).

K7 - Multiplying factor for diffraction loss calculation.

Clutter_loss - Clutter specifications taken into account in the calculation process.


Calibration Process

Initial (default) parameters for Standard Macrocell Model (type 3): Non-calibrated model

Parameter definition Value (900MHz/1800MHz)

Mobile Rx height, m 1.5

Earth radius, km 8493

Effective antenna height algorithm Relative

Diffraction loss algorithm Epstein-Peterson

K1 150.9/160.9

K2 44.9

K1 near 0.0

K2 near 0.0

Near distance 0.0

K3 -2.55/-2.88

K4 0.0

K5 -13.82

K6 -6.55

K7 0.7/0.8


Calibration Process Prediction with Initial Default Parameters: Prediction with initial default parameters is done against cell-

id for four models and clutter types to see the errors in prediction of Std. Deviation and Correlation coefficient.

CELL ID # bins Mean Error RMS Error STD Error Corr. coef.

KAT002Z 3164 -4.5 15.3 14.6 -0.0231

KAT013X 2162 -3.2 7.6 6.8 0.5288

KAT022X 3477 -6.7 12.4 10.5 0.5432

KAT045Y 2546 -1.5 6.7 6.5 0.6006

KAT065X 2620 -5.0 13.1 12.1 0.2995

KAT075X 2973 -14.7 17.8 10.1 0.5685

KAT108X 2201 2.8 11.8 11.4 0.5521

KAT112Z 2469 -5.7 8.8 6.7 0.7576

KAT118X 3916 3.4 7.1 6.2 0.5980

KAT118Z 2468 -7.6 9.7 6.0 0.7139

KAT122X 3122 -5.5 8.9 7.1 0.6481

TOTAL 31118 -4.4 11.5 10.6 0.4257

Clutter Type # bins Mean Error RMS Error STD Error Corr. coef.

OPEN 7751 -9.1 14.4 11.2 0.3930SEMIOPEN_AREA 33 -15.5 21.6 15.3 0.3526

PARKS 54 -13.6 17.1 10.4 0.5857VILLAGES 121 -3.2 6.3 5.4 -0.3098

INDUSTRIAL_ZONES 459 -4.7 10.2 9.0 0.2371

SUBURBAN 2229 -2.7 9.7 9.3 0.5376LOW_URBAN 197 -3.4 8.6 7.9 0.1648

MEDIUM_URBAN 3065 -3.9 10.7 10.0 0.5114DENSE_URBAN 17209 -2.5 10.3 10.0 0.4606


Calibration Process Tuning of the model: The calibration process consists of steps as: Iterative estimation of K1, K2, K7, clutter

offsets, height and separation and final analysis of models performance. For instance, these steps will be done for model urban 1800MHz. The rest of models will be calibrated in same way.

• Tuning of K1 The purpose of tuning K1 is to reach the lowest RMS. Few iterations have been done to get this value. Before this Non-LOS measurements were deactivated to avoid impact of diffraction losses. The optimum value of K1 at this stage is 158.1. With this value we have following results of error evaluation. Errors in clutter type will not be considered at this step.

• Tuning of K2 Likely to K1 during tuning K2 the lowest RMS error will be found. Before this Non-LOS measurements were deactivated to avoid impact of diffraction loss. The optimum value of K2 at this stage is 54.0. With this value we have following results of error evaluation. Errors in clutter type will not be considered at this step.

• Tuning of K7 Likely to K1 and K2 during tuning K7 the lowest RMS error will be found. Before this LOS measurements were deactivated and NLOS were activated. This was done, because K7 (diffraction loss coefficient) must be tuned only for measurements, recorded at non-LOS areas.

The optimum value of K7 at this stage is 0.35. With this value we have following results of error evaluation. At this stage, final values for K2 and K7 were found. K1 will require further fine-tuning following the influence of the clutter type tuning.

• Errors in prediction against Cell-ID with K1=158.1, K2=54.0, K7=0.35 is calculated for each model.

• Errors in prediction against Clutter Type with K1=158.1, K2=54.0, K7=0.35 is calculated for each clutter.


Calibration Process Tuning of clutter offsets Different clutter types produce different errors level. So, now its mandatory to find

optimum value of clutter offsets in order to have minimum errors for each of them. In this case, impact of clutter penetration will be taken into account, so the accuracy of prediction will be improved.

For semi_open area and parks the number of bins is too low (less than 100), so tuning of clutter offsets for them is not possible. They will be kept as 0 at this stage. The majority of bins are about urban areas and open as well. We have so much measurements in open area, because the driven roads belongs to this type. Anyway, first stage of clutter offsets tuning is normalization its values to open type only. So all other clutters were deactivated and following table is produced.

Errors in prediction against cell id and clutter type with K1=158.1, K2=54.0, K7=0.35 (open area only)CELL ID # bins Mean Error RMS Error STD Error Corr. coef.

KAT002Z 1063 -3.4 14.4 14.0 0.0805KAT013X 389 1.2 5.5 5.3 0.4575KAT022X 1497 -1.2 10.2 10.2 0.5499KAT045Y 518 -0.9 5.4 5.4 0.7051KAT065X 873 -4.4 8.7 7.5 0.7009KAT075X 766 -10.9 13.5 7.9 0.4266KAT108X 263 0.3 9.0 9.0 0.8367KAT112Z 292 -0.5 7.0 7.0 0.6420KAT118X 408 6.3 8.5 5.6 0.7607KAT118Z 457 -7.3 9.5 6.1 0.5708KAT122X 1225 -2.0 6.0 5.6 0.7536TOTAL 7751 -2.7 9.9 9.5 0.4889

Clutter Type # bins Mean Error RMS Error STD Error Corr. coef.

OPEN 7751 -2.7 9.9 9.5 0.4889


Calibration Process Tuning of clutter offsets (contd..)

Overall mean error of the model now must be substracted from value of K1 in order to get it normalised to the “open” clutter type.

Errors in prediction against Cell-ID and Clutter Type with K1=155.4, K2=54.0, K7=0.35 (open area only)

According to new value of K1=155.4 models performance must be assessed once again, taking into account all clutter types.

In order to involve clutter influence on propagation loss, the mean errors of every clutter type should be considered via clutter offset. For this, it is required to substract overall mean error specified from the table for each clutter class. In next performance assessment involving clutter influence,

Errors in prediction against Cell-ID and Clutter Type with K1=150.9, K2=54.0, K7=0.35

At the next stage its required to substract every clutter types error from zero and define it as a clutter offset. With such set of values for clutter offset model performance indicators are following.

Clutter Type # bins Mean Error RMS Error STD Error Corr. coef.OPEN 7751 -0.0 9.5 9.5 0.4887

SEMIOPEN_AREA 33 -0.0 8.0 5.6 0.4986PARKS 54 -0.0 8.2 8.3 0.7770

VILLAGES 121 0.0 4.4 4.4 -0.2765INDUSTRIAL_ZONES 459 -0.0 6.8 6.8 0.5366

SUBURBAN 2229 0.0 9.2 9.2 0.4945LOW_URBAN 197 -0.0 6.5 6.5 0.2931

MEDIUM_URBAN 3065 0.0 7.2 7.2 0.6858DENSE_URBAN 17209 0.0 8.5 8.5 0.5998


Calibration Process Tuning of clutter height and separation distance

The influence of clutter heights and separation distance will further enhance the prediction accuracy and decrease the level of error. Aircom Asset performs a diffraction loss calculation over the edge of clutter and therefore requires the clutter height and the distance between the edge of the clutter and mobile end point. With starting values of clutter heights (like dense_urban = 30m, medium_urban = 20m, low_urban = 10m etc.) model shows totally different results in compare with the model without clutter height consideration. The point is to find optimum values of height of each clutter type, keeping overal error value as low as possible. Same procedure was accomplished for separation distance calibration. As a result of this stage, following table was achieved. These values are same for all types of model, because they define common clutter settings.

Clutter Type Clutter height, m Separation distance, m

OPEN 0.0 0.0SEMIOPEN_AREA 0.0 0.0

PARKS 20.0 20.0SPARSE_FOREST 10.0 30.0DENSE_FOREST 20.0 30.0

VILLAGES 9.0 10.0INDUSTRIAL_ZON

ES 10.0 10.0

SUBURBAN 10.0 10.0LOW_URBAN 13.0 15.0

MEDIUM_URBAN 15.0 20.0DENSE_URBAN 20.0 20.0


Calibration Process Clutter Offset Assessment with Clutter Height & Separation Distance Consideration:

• Mean error of each clutter type is non-zero again as well as overall error now. The reason is implementation of clutter height and separation distance. So, its required to substract every clutter types error from zero and define it as a clutter offset once again. Same action should be done with the K1 in order to achieve overall mean error equals to 0. So K1 will be 145.00. (Below table for urban 1800MHz)

Parameter definition Value

K1 145.0

K2 54.0

K1 near 0.0

K2 near 0.0

Near distance 0.0

K3 -2.88

K4 0.0

K5 -13.82

K6 -6.55

K7 0.35

Clutter type Offset Value

OPEN 1.4

SEMIOPEN_AREA 1.7

DENSE_FOREST 3.0

VILLAGES 0.6

INDUSTRIAL_ZONES 4.3

SUBURBAN 1.7

LOW_URBAN -0.05

MEDIUM_URBAN 3.8

DENSE_URBAN 7.6


Calibration Process

Assessment of Model Performance Indicators after Clutter Offset Calibration

Errors in prediction against Cell-ID with K1=145.00, K2=54.0, K7=0.35, clutter settings on.

Errors in prediction against Clutter Types with K1=145.00, K2=54.0, K7=0.35, clutter settings on

CELL ID # bins Mean Error RMS Error STD Error Corr. coeff.KAT002Z 3164 0.5 6.1 6.1 0.6467KAT013X 2162 0.5 9.5 9.5 0.6233KAT022X 3477 2.1 5.8 5.4 0.7438KAT045Y 2546 0.2 8.6 8.6 0.5242KAT065X 2620 -5.2 8.5 6.7 0.7295KAT075X 2973 0.7 6.1 6.1 0.7842KAT108X 2201 4.6 7.3 5.7 0.7035KAT112Z 2469 -4.8 8.1 6.5 0.6889KAT118X 3916 -1.1 6.2 6.1 0.7504KAT118Z 2468 0.5 6.1 6.1 0.6467KAT122X 3122 0.5 9.5 9.5 0.6233TOTAL 31118 -0.0 7.6 7.6 0.6382

Clutter Type # bins Mean Error RMS Error STD Error Corr. coeff.OPEN 7751 -0.0 8.6 8.6 0.5370

SEMIOPEN_AREA 33 -0.0 7.7 5.7 0.5037PARKS 54 0.0 8.1 8.2 0.7414

VILLAGES 121 0.0 4.4 4.4 -0.2765INDUSTRIAL_ZONES 459 0.0 7.2 7.2 0.5067

SUBURBAN 2229 -0.0 7.2 7.1 0.5747LOW_URBAN 197 -0.0 6.5 6.5 0.2949

MEDIUM_URBAN 3065 -0.0 6.3 6.3 0.7474DENSE_URBAN 17209 0.0 7.4 7.4 0.6487


Calibration Process Assessment of Model Performance and Calibrated Model Parameters

Its important to perform the visual comparison in planning tool like Assest. For this, a cell is selected, additional verification drive-test was conducted too as shown below:

(Urban 1800)-KAT089Y prediction against real drive-test presentationParameter definition Value

K1 145.0

K2 54.0

K1 near 0.0

K2 near 0.0

Near distance 0.0

K3 -2.88

K4 0.0

K5 -13.82

K6 -6.55

K7 0.35

Clutter type Offset

OPEN 1.4

SEMIOPEN_AREA 1.7

DENSE_FOREST 3.0

VILLAGES 0.6


SUBURBAN 1.7

LOW_URBAN -0.05

MEDIUM_URBAN 3.8

DENSE_URBAN 7.6



(Sub Urban 1800MHz)-KAT089Y prediction against real drive-test presentation


K1 143.2

K2 48.5

K1 near 0.0

K2 near 0.0

Near distance 0.0

K3 -2.88

K4 0.0

K5 -13.82

K6 -6.55

K7 0.35

Clutter type Offset

OPEN 2.6

SEMIOPEN_AREA 5.1

PARKS -9.4

SPARSE_FOREST 3

DENSE_FOREST -2.6

VILLAGES 2.8


SUBURBAN 3.9

LOW_URBAN 7.3

MEDIUM_URBAN 0.7

DENSE_URBAN 5.6



(Urban 900MHz)- KAT107A prediction against real drive-test presentation


K1 147.7

K2 48.5

K1 near 0.0

K2 near 0.0

Near distance 0.0

K3 -2.55

K4 0.0

K5 -13.82

K6 -6.55

K7 0.25

Clutter type Offset

OPEN 1.0

SEMIOPEN_AREA -3.4

PARKS 3.5

VILLAGES -4.2


SUBURBAN 0.3

LOW_URBAN 1.0

MEDIUM_URBAN 2.9

DENSE_URBAN 7.2



(Sub Urban 900MHz)-KAT223B prediction against real drive-test presentation


K1 147.7

K2 48.5

K1 near 0.0

K2 near 0.0

Near distance 0.0

K3 -2.55

K4 0.0

K5 -13.82

K6 -6.55

K7 0.25

Clutter type Offset

OPEN 1.0

SEMIOPEN_AREA -3.4

PARKS 3.5

VILLAGES -4.2


SUBURBAN 0.3

LOW_URBAN 1.0

MEDIUM_URBAN 2.9

DENSE_URBAN 7.2


Conclusion4 Prediction Models- 2 for GSM and 2 for DCS in Urban and Sub-Urban produced

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