Atoll 3.1.2 Measurement Calibration

Measurement Module Atoll 3.1.2

© Forsk 2012 Confidential – Do not share without prior permission Slide 1

1. SPM Calibration Concepts

2. Guidelines for CW Measurement Surveys

3. Working With CW Measurements

4. Automatic Calibration Method

5. Analysing the Calibrated Model

6. Calibration Process Summary

Training Programme



Purpose of Model Calibration

Introduction to the Standard Propagation Model (SPM)

Requirements

Quality targets


Purpose of Model Calibration

The propagation model is the basis of cell planning in mobile networks

Reliability of cell planning is closely related to the propagation model accuracy

A good model calibration is therefore required

To obtain a propagation model consistent with the actual radio environment

To improve the accuracy of coverage predictions

To properly estimate interference

The model calibration process entails three main procedures:

Collecting CW (Continuous Wave) measurement data

• Site location

• Constructing test platform

• Drive test

Post-processing the CW measurement data

• Data filtering

Calibrating the model

© Forsk 2012 Slide 4 Confidential – Do not share without prior permission

Introduction to the Standard Propagation Model (SPM)

Standard Propagation Model (SPM)

Macrocell statistical propagation model

Well suited for predictions in the 150 to 3500 MHz band

Based on empirical formulas + set of parameters

Numerous elements considered in propagation

Frequency

Distance between TX and RX

Area type (urban, suburban, rural, etc.)

Geography (relief, vegetation, climate, etc.)

Effective height of TX/RX antennas

Default values in new projects !

Calibration is essential to accurately estimate

• Coverage predictions

• Interference


LOS hill,clutterRxeff7Rxeff6

Txeff54Txeff321model

Kclutter.fKH.logKH.K

Hlog.d.logKLoss nDiffractio.KH.logKd.logKKL

Requirements (1/2)

Accurate and recent geo data

DTM and clutter resolution ≤ 25m for urban areas

DTM and clutter resolution ≤ 50m for rural areas

Vector map with main roads

CW measurement surveys

Site selection (for each area type – frequency band)

• 8 recommended (6 minimum ) sites for calibration

• 2 sites for verification

Selection of different area types representative of the studied city

• All main clutter classes should be represented

CW surveys must be performed by stringently following guidelines


Requirements (2/2)

Drive Test data

Possible but not recommended !

Conversion to CW measurements is needed

Downsides

Real network is measured Interference

Several frequencies are measured

Directional antennas Accuracy of pattern (only a few points are relevant)

Low sampling rate for each measured station (Lee criterion can’t be met)

Signal measured over a short distance from the transmitter (model will not be calibrated for interference evaluation)

It is not recommended to use Drive Test data to calibrate a propagation model !


Quality Targets

Overall objective :

Minimize the error between the propagation model and the CW survey data

Quality targets for calibration sites

Global mean error on calibration sites < 1 dB

Global standard deviation on calibration sites < 8 dB

Mean error on each calibration site < 2.5 dB

Standard deviation on each calibration site < 8.5 dB

Quality targets for verification sites

Global mean error on verification sites < 2 dB

Global standard deviation on verification sites < 8.5 dB








Training Programme



Site Preselection criteria

Survey route criteria

Radio criteria


Site Preselection Criteria

Surrounding

Very representative of area type

Major clutter classes equally represented

No major obstruction within a radius of 150 to 200m from the CW sites

Low diffraction within a 10km radius (rural zones)

Enough roads all around the site

Inspection on site

Possibility to set up omnidirectional antenna

• No obstacle on any side

Panoramic photographs

Report site details: precise height, coordinates ...


Survey Route Criteria

Distance

Up to noise floor of the receiver

• Rural ± 10kms / Suburban ± 2kms / Urban ± 1km

Equal number of samples near and far in all directions

Clutter

Routes through major clutter classes

Avoid forests and lakes between transmitter and receiver

Maps

Supply vector maps of survey routes to import in Atoll

Check that survey routes and roads (vector data or scanned maps) match !


Radio Criteria (1)

Frequency

3 contiguous unused channels for GSM

1 unused carrier for UMTS

Only one channel must be measured

Interference must be checked before each drive

Equipment data

Antenna patterns + downtilt + azimuth (if not perfectly omnidirectional)

Antenna height + transmit power + transmission gain (antenna) and losses (feeder)

Receiver height + sensitivity + reception gain and losses


Radio Criteria (2)

Signal measurement

Lee criterion: at least 36 samples over 40λ (for f ≥ 900 MHz)

• Maximum vehicle speed depends on equipment’s sampling rate

Averaging samples over 40λ aims to remove fast fading effect !

Measurements after averaging

At least 5000 points per site

Typical number: between 10000 and 20000 points

Sampling Rate

at 900 MHz (samples per second)

Sampling Rate

at 2100 MHz (samples per second)

Max. Speed (km/h)

45 100 60

68 150 90

90 200 120

113 250 150








Training Programme


3. Working with CW Measurements

Creating a CW measurement path

By importing any ASCII format file

• Standard import as in Excel

• Option of importing any additional information related to CW measurement points

• Definition and storage of import configurations

By copying – pasting X,Y, measurement



CW Measurements: Table

List of all measurement points with their attributes and additional information

Standard content management and tools (filters, copy-paste, etc...)

Coordinates of points

Signal Measured

values

Altitude, Clutter classes and

heights, Distance, etc. read from the

Geo data



CW Measurements: Properties

The points can be displayed according to any data contained in the measurement Table

For predictions along the CW measurement path, you can either use Existing path loss matrices or recalculate them by choosing a specific Propagation model



CW Measurements: Calculations and Statistics

To calculate the predicted signal level of the reference (and any other optionally added)

transmitter along the considered path.

Note: This can also be run from top folders.

To compare statistics between measured and predicted signal levels.

Note: This can also be run from top folders.



CW Measurements: Filter (at Folder level)

Distance, Measurements

values and Azimuth filtering

Advanced filter on additional survey data

Clutter Classes filtering



CW Measurements: Filtering Assistant and Filtering Zones

Tool to filter the data path in an more

advanced way than in the Filter dialogue

available at the folder level (previous slide)

Tool to exclude some points from the measurement path according to a drawn polygon (all points within the polygon

will be filtered out)



CW Measurements: Smoothing

Smoothing can be used to limit fading effect

Smoothing keeps the number of measurement points unchanged

Smoothing cannot be used to average gross CW measurements

BEFORE

AFTER

Create a sliding window to smooth the measured signal strength



CW Measurements: Synchronise the Table, the Map and the CW Measurements Tool

Synchronisation: - Map - Table - CW Measurements Tool

Analysis of a specific CW

measurement path

Measured signal level

Predicted signal level

Display of any attribute related to a given path








Training Programme



CW measurements pre-processing

Calibration / verification stations

Initial model

Calibration wizard

Final model


CW Measurements Pre-processing

Correspondence between Measurements and Geo data

Projection checking

• Check that CW measurements and roads (from vector maps) match

Routes checking

• Check that CW measurements respect planned survey routes

Surrounding checking

• Check, with panoramic photographs, that there is no obstacle

• Option of setting an angle filter to avoid attenuation due to obstacles



Filtering

Available at the Folder level for each site

Will be applied to all the measurement paths in that folder

Distance,

Measurements values and Azimuth filtering

Advanced filter on additional survey data

Clutter Classes filtering

Will permanently remove the points outside the filter



Distance filtering (Min Distance / Max Distance)

Typical min value: 200 m (not representative of mean propagation)

Typical max value: 10 km (rural area)

Signal filtering (Min Measurement / Max Measurement)

Filtering out the measurements above the receiver overload: typical value -40 dBm

Filtering out the measurements below the “receiver sensitivity + target standard deviation” typical value: -120 + 8 = -112 dBm

• In order to avoid noise saturation effect in statistical results

Azimuth filtering

To remove points in a certain angle

Filtering assistant

In addition to the Filter located at the Folder level, you can define more precise filtering depending on the CW measurement file



Filtering assistant (1/2)

Display of M = f ( 10log(D) )

Selection rectangle simultaneous Signal/Distance filtering

Signal/Distance filtering

according to the selection rectangle

Selection Rectangle Azimuth

filtering on the measurement

points

Possibility to keep the selected points or to exclude them



Filtering assistant (2/2)

Remaining points after the Distance, Signal level, Azimuth

and Clutter classes filtering

Remove all previous filters applied



Final filtering (1/2)

Display each CW measurement according to their Measured signal level

Check that propagation loss is spatially homogeneous



Final filtering (2/2)

Removing which points?

• Sudden drop of signal level

• Suspicious areas ( Waveguide effect!)

How?

• Delete from the CW measurement table

• Draw Filtering zones


Calibration / Verification Stations

Calibration stations

Stations so that measurements cover the whole area

Avoid keeping stations with a lot of common points

Verification stations

Stations so that measurements are inside covered area (not at edges!)

Major part of their covered areas are also covered by calibration stations

How many ?

If 7-8 measured stations:

• 6 for calibration; 1-2 for verification

If < 7 measured stations:

• All stations used for calibration

• Verification performed with same stations


Initial Model

General SPM formula

Ki values

Let K6 = 0

Others will be calibrated

Effective antenna height

Choose method according to terrain relief

Modify height from transmitter properties

Can be selected by the calibration process

Recommendation if terrain is hilly:

“Enhanced slope at receiver” method

Hilly terrain correction “1-YES”

Recommendation if terrain is flat:

“Height above average profile” method

Hilly terrain correction “0-NO”

LOS hill,clutterRxeff7Rxeff6

Txeff54Txeff321model

Kclutter.fKH.logKH.K

Hlog.d.logKLoss nDiffractio.KH.logKd.logKKL


Initial Model

Max distance

Forced to 0 during calibration

If >0 no continuity ensured

KClutter

= 1 is recommended

Multiplying factor of clutter losses

Minimum loss

= Free space loss

Avoid unrealistic values

Profiles

Radial optimisation

• Quicker


Initial Model

Heights of Clutter taken or not into account in Diffraction:

If you have a Clutter Heights file

• Then put “1-YES” in the box

If you only have a Clutter classes File

• 2 approaches:

• If Clutter Classes file has a very fine resolution

• You can put “1-YES” and the tool will take into account the average heights defined in your clutter classes file

• You should keep all the losses per clutter class to Zero

• If Clutter Classes file resolution is low

• Do not take into account the average heights defined in your clutter classes file (“0-NO”), but instead add a Loss per Clutter class type

Receiver on top of clutter

By default “No”

Only useful for fixed receivers


Initial Model

Clutter Classes Losses can be calibrated

You need to define the Max distance from the Receiver (towards the Transmitter) for which the different clutter classes will be considered (typically 5X the clutter resolution)

Choice between 4 types of Weighting functions (Uniform, Triangular, Logarithmic, Exponential)

n

i

iiwLclutterf1

wi=f(d'i)

d'i

wi

Uniform

Triangular

Logarithmic

Exponential


Initial Model

Reference model

Create a Reference model containing all the previous settings

Duplicate this Reference model for each calibration, and give it a relevant name

When duplicated, choose an appropriate name and pay specific attention to:

• Methods used for Diffraction and Effective Antenna Height calculation

• Value of Kclutter

• Hilly terrain correction

• Heights of Clutter considered or not in Diffraction

• Clutter Range and associated Weighting function

Start from the Reference model for each calibration trial


Calibration Wizard

Automatic calibration overview

Algorithm based on solving a least-squares problem

Calculation of the best solution in terms of root mean square :

Simple, fast and reproducible procedure

First Step

Selection of calibration stations

Mδ22RMS


Calibration Wizard

Second step (1/2)

Selection of the Parameters to calibrate

Possibility to modify their ranges


Calibration Wizard

Second step (2/2)

Recommended ranges

It is recommended to leave K6 to 0

Constant Min Max

K1 0 100

K2 20 70

K3 -20 20

K4 0 1

K5 -10 0

K7 -10 0


Calibration Wizard

Final step

Display of “Before” and “After” parameters values and statistics (Mean error, Standard Deviation, RMS)

Commit will update the model you are calibrating with the new values of Ki, height and diffraction methods as well as the Clutter Losses


Final Model

Extrapolate non-calibrated clutter losses (1/2)

Non-calibrated clutter classes must not have their clutter losses left to 0

• Could lead to high error where these classes are present

Must be extrapolated from

• Calibrated clutter losses (from other propagation model)

• Typical losses (here centred on the Urban class)

Clutter class Typical loss

Dense Urban from 4 to 5

Woodland from 2 to 3

Urban 0

Suburban from -5 to -3

Industrial from -5 to -3

Open in urban from -6 to -4

Open from -12 to -10

Water from -14 to -12


Final Model

Extrapolate non-calibrated clutter losses (2/2)

Centre clutter losses

• Relative difference between clutters kept unchanged

• Use K1 to balance

Example:

Apply scaling factor

• Adapt typical losses (or calibrated ones coming from other model) to the calibrated model

After calibration, model centred on suburban: K1=17.4 Losses: Dense Urban = 6.5 Wood = 5.7 Urban = 3.5 Suburban = 0

Typical Losses

“MyModel” Losses

Urban

0

0 calibrated

4.5

3 calibrated

Dense Urban Open

-12

-8 Extrapolated

C

e

n

t

r

e

d

Define Scaling Factor

After centring, new values: K1=20.9 Losses: Dense Urban = 3 Wood = 2.2 Urban = 0 Suburban = -3.5








Training Programme


5. Analysing The Calibrated Model

Statistics (1/2)

Apply the new calibrated propagation model to your CW sites



Statistics (2/2)

Check the Quality Targets (Std Deviation and Mean Error values) on the Calibration and Verification sites

Statistics available

• Globally,

• per Clutter class,

• per Transmitter, and per Measurement path

Possibility to run the Statistics on all the

Measurement paths, or on specific ones



Correlation (to be checked on the Calibration sites)

Through the Assisted Calibration Wizard

Displays, for each parameters to be calibrated (K1, K2, K3, etc.), the correlation of the variables log(D), log(Heff), Diff, etc. with the global Error

Check if the Correlation values are between -0,1 and +0,1

Commit will apply the Correction values to the corresponding Ki values Notes: This will not take into account the Ki Ranges

The calibration wizard will attempt to bring the correlation as close to zero as possible. The

results will be a correction value that will be added or

subtracted to the initial Ki value in the model



Display Error

Recalculate the Predicted signal values (P) according to the calibrated propagation model

Display the Error (P – M) between the CW Measurements values (M) and the Predicted values (P)



Display CW Measurements & associated Signal Level study

Use the same shading on both displays to be able to compare them

For each site, one by one Check the global behaviour of calibrated model



CW measurement and Profile windows

Analysis along the path




3. Post-process the CW Measurements Data




Training Programme


Calibration Process Summary

Before starting...

Check Geographical Database quality & accuracy (DTM, clutter, vectors...)

Define environments (hilly, flat / urban, rural...) to specify the required number of propagation models to be calibrated

Measurements preparation

Sites selection

Survey roads

Fulfil radio criteria

Make & Average measurements

Create Transmitters used for measurements in the Atoll document

With exact configuration (coordinates, antenna type & height, EIRP, losses)

Analyse & Filter measurements ( Pre-processing)

Keep representative points and remove suspicious ones

Choice of calibration / verification sites © Forsk 2012 Confidential – Do not share without prior permission Slide 53

Calibration Process Summary

Run the automatic calibration

Display statistics and compare results with target values (Std deviation and Mean error)

for calibration sites: Global and Individual checking

for verification sites: Global checking

Extrapolate non-calibrated clutter losses

Analyse calibrated model

Display statistics

Check correlation

Maps displaying Error(P-M), Measurements & Signal Level Study, etc.

Apply the calibrated model

Apply resulting standard deviation per clutter in the clutter class description

Apply the calibrated model to network’s transmitters (Transmitter Properties\Propagation tab)


Thank you


Documents

Atoll 3.1.2 Measurement Calibration