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ATOLL MEASUREMENT MODULE
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Training Programme
1. SPM Calibration Concepts
. u e nes or easurement urveys
3. Working with CW Measurements
4. Automatic Calibration Method
5. Analysing the Calibrated Model
6. Calibration Process Summary
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1. SPM Calibration Concepts
Goal
equ rements
Quality target
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Goal
SPM
Macrocell statistical propagation model
Based on empirical formulas + set of parameters
Propagation depends on
Frequency
Area type (urban, suburban, rural, etc.)
Geography (relief, vegetation, climate, etc.)
Calibration is essential to accurately estimate
Coverage predictions
Interferences
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Requirements (1)
Accurate and recent geo data
DTM and clutter resolution
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Requirements (2)
Drive Test data
Possible but not recommended
Conversion to CW measurements is needed
Downside
Real network is measured interferences
Directional antennas: accuracy of pattern, only a few points are relevant
Several frequencies are measured
Low sampling rate for each measured station (lee criterion cant be met)
Signal measured over a short distance from the transmitter (model will not be calibrated forinterference evaluation)
It is not recommended to use Drive Test data to calibrate a model
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Quality Target
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
Verification sites
Global mean error on verification sites < 2 dB
Global standard deviation on verification sites < 8.5 dB
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Training Programme
1. SPM Calibration Concepts
. u e nes or easurement urveys
3. Working with CW Measurements
4. Automatic Calibration Method
5. Analysing the Calibrated Model
6. Calibration Process Summary
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2. Guidelines for CW Measurement Surveys
Site Preselection criteria
urvey route cr ter a
Radio criteria
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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
nspec on on s e
Possibility to rig omnidirectional antenna (no obstacle on any side)
Panoramic photographs
Report site details: precise height, coordinates
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Survey Route Criteria
Distance
Up to noise floor of the receiver
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 match roads (vector data or scanned maps)
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Radio Criteria (1)
Frequency
3 contiguous unused channels for GSM
1 unused carrier for UMTS
Only one channel must be measured
Interferences must be checked before each drive
Equipment data
ntenna patterns + ownt t + az mut not per ect y omn rect ona
Antenna height + transmit power + transmission gain and losses
Receiver height + sensitivity + reception gain and losses
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Radio Criteria (2)
Signal measurement
Lee criterion: at least 36 samples over 40(for f 900 MHz)
Maximum vehicle speed depends on equipments sampling rate
Sampling Rateat 900 MHz
(samples per second)
Sampling Rateat 2100 MHz
(samples per second)
Max. Speed (km/h)
45 100 60
68 150 90
90 200 120
113 250 150
Measurements after averaging
At least 5000 points per site
Typical number: between 10000 and 20000 points
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Training Programme
1. SPM Calibration Concepts
. u e nes or easurement urveys
3. Working with CW Measurements
4. Automatic Calibration Method
5. Analysing the Calibrated Model
6. Calibration Process Summary
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3. Working with CW Measurements
Creating a CW measurement path
B co in astin X Y measurement
By importing any ASCII format file
Standard import as in excel
Option of importing any additionalinformation related to CW measurementpoints
configurations
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3. Working With CW Measurements
CW Measurements: Table
List of all measurement points with their attributes and additional information
Standard content management and tools (filters, copy-paste, etc...)
Coordinatesof points
SignalMeasured
values
Altitude, Clutterclasses and
heights,Distance, etc.read from the
Geo data
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3. Working With CW Measurements
CW Measurements: Properties
The points can bedisplayed according to any
data contained in themeasurement Table
For predictions along the CW measurementpath, you can either use Existing path lossmatrices or recalculate them by choosing a
specific Propagation model
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3. Working With CW Measurements
CW Measurements: Calculations and Statistics
To calculate the predictedsignal level of the reference
an any o er op ona y
added) transmitter along theconsidered path.
Note: This can also be run
from top folders.
To compare statisticsbetween measured and
predicted signal levels.
Note: This can also be
run from top folders.
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3. Working With CW Measurements
CW Measurements: Filter (at the Folder level)
Distance, Measurements
Advanced filter on additional survey data Clutter Classes filtering
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3. Working With CW Measurements
CW Measurements: Filtering Assistant and Filtering Zones
Tool to filter the datapath in an more
advanced way than inthe 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)
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3. Working With CW Measurements
CW Measurements: Smoothing BEFORE
AFTER
Create a sliding window to smooth
the measured signal strength
Smoothing can be used to limit fading effect
Smoothing keeps the number of measurement pointsunchanged
Smoothing cannot be used to average gross CWmeasurements
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3. Working With CW Measurements
CW Measurements: Synchronise the Table, the Mapand the CW Measurements Tool
Synchronisation:
- Map
- Table
- CW Measurements Tool
Analysis of aspecific CW
measurement
path
Predicted signal level
Display of any attributerelated to a given path
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Training Programme
1. SPM Calibration Concepts
. u e nes or easurement urveys
3. Working with CW Measurements
4. Automatic Calibration Method
5. Analysing the Calibrated Model
6. Calibration Process Summary
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4. Automatic Calibration Method
CW measurements pre-processing
Initial model
Calibration wizard
Final model
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CW Measurements Pre-processing
Correspondence between Measurements and Geo data
Projection checking
Check that CW measurements match roads
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
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CW Measurements Pre-processing
Filtering
Available at the Folder level for each site
Will be applied to all the measurement paths in that folder
Distance, Measurementsvalues and Azimuth filtering
Will permanently removethe points outside the filter
Advanced filter on additional survey data
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CW Measurements Pre-processing
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 deviationtypical 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 filteringdepending on the CW measurement file
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CW Measurements Pre-processing
Filtering assistant (1/2)
Display of M = f ( 10log(D) )
Selection rectangle simultaneous Signal/Distance filtering Possibility to keepthe selected points
Signal/Distance
filtering
according to the
selectionrectangle
SelectionAzimuth
filtering on the
or to exclude them
ec ang e
measurement
points
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CW M t P i
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CW Measurements Pre-processing
Final filtering (1/2)
Display each CW measurement according to their Measured signal level
Check that propagation loss is spatially homogeneous
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CW M t P i
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CW Measurements Pre-processing
Final filtering (1/2)
Removing which points?
Sudden drop of signal level
How?
Delete from the CW measurement table
Draw Filtering zones
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Calibration / Verification Stations
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Calibration / Verification Stations
Display measurement routes after pre-processing
Calibration stations
a ons so a measuremen s cover e w o e 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 8 measured stations 6 for calibration; 2 for verification
If < 8 measured stations all stations used for calibration verification performed with same stations
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Initial Model
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Initial Model
General SPM formula
( ) ( ) ( ) ( )( ) ( ) ( ) LOShill,clutterRxeff7Rxeff6
Txeff54Txeff321model
Kclutter.fKH.logKHK
Hlogd.logKLossnDiffractioKH.logKd.logKKL++++
++++=
.
..
Let K6 = 0
Others will be calibrated
Effective antenna height
Choose method according to terrain reliefModify 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 methodHilly terrain correction 0-NO
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Initial Model
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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
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Initial Model
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Initial Model
Heights of Clutter taken or not into account inDiffraction
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 clutterclasses 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 definedin your clutter classes file (0-NO), but instead add aLoss per Clutter class type
Receiver on top of clutter
By default NoOnly useful for fixed receivers
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Initial Model
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Initial Model
Clutter Classes Losses can be calibrated
You need to define the max distance from the Receiver (towards the Transmitter) for whichthe different clutter classes will be considered
Choice between 4 t es of Wei htin functions Uniform Trian ular Lo arithmic
Exponential)
( ) =
=
n
iiiwLclutterf
1
wi=f(d'i) Uniform
Triangular
Logarithmic
Exponential
d'i
wi
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Initial Model
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Initial Model
Reference model
Create a Reference model containing all the previous settings
,
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
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Calibration Wizard
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Calibration Wizard
Automatic calibration overview
Algorithm based on solving a least-squares problem
Calculation of the best solution in terms of root mean square : M22RMS +=
,
First Step
Selection of calibration stations
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Calibration Wizard
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Calibration Wizard
Second step (1/2)
Selection of the Parameters to calibrate
Possibility to modify their ranges
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Calibration Wizard
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Calibration Wizard
Second step (2/2)
Recommended ranges
Constant Min Max
K1 0 100
K2 20 70
K3 -20 20
K4 0 1
K5 -10 0
K7 -1
It is recommended to leave K6 to 0
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Calibration Wizard
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Calibration Wizard
Final step
Display of Before and After Parameters values and Statistics
Commit will update the model you are calibrating with the new values of Ki, height anddiffraction methods as well as the Clutter Losses
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Final Model
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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 lossDense Urban from 4 to 5
Woodland from 2 to 3
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
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Final Model
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Extrapolate non-calibrated clutter losses (2/2)
Centre clutter losses
Relative difference between clutters kept unchanged
Example:
Apply scaling factor
After calibration, model centred on suburban:K1=17.4 Losses: Dense Urban = 6.5
Wood = 5.7Urban = 3.5Suburban = 0
After centring, new values:K1=20.9 Losses: Dense Urban = 3
Wood = 2.2Urban = 0Suburban = -3.5
apt typ ca osses or ca rate ones com ng rom ot er mo e to t e ca rate mo e
Typical Losses
MyModel Losses
Urban
0
0calibrated
4.5
3calibrated
Dense UrbanOpen
-12
-8Extrapolated
C
e
n
t
r
e
d
Define Scaling Factor
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Training Programme
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g g
1. SPM Calibration Concepts
. u e nes or easurement urveys
3. Working with CW Measurements
4. Automatic Calibration Method
5. Analysing the Calibrated Model
6. Calibration Process Summary
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5. Analysing The Calibrated Model
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y g
Statistics (1/2)
Apply the new calibrated propagation model to your CW sites
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5. Analysing The Calibrated Model
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Statistics (2/2)
Check the Quality Targets (Std Deviation and Mean Error values) on the Calibration andVerification sites
Statistics available Globall
per Clutter class, per Transmitter andper Measurement path
Possibility to run theStatistics on all the
Measurement paths,
or on specific ones
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5. Analysing The Calibrated Model
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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 thevariables lo D lo Heff Diff etc. with the lobal Error
Check if the Correlation values are between -0,1 and +0,1
Commit will apply the Correction values to the corresponding Ki valuesNotes: This will not take into account the Ki Ranges
The calibration wizard will
attempt to bring thecorrelation 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
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5. Analysing The Calibrated Model
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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 Predictedvalues P
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5. Analysing The Calibrated Model
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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
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5. Analysing The Calibrated Model
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CW measurement and Profile windows
Analysis along the path
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Training Programme
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1. SPM Calibration Concepts
. u e nes or easurement urveys
3. Working with CW Measurements
4. Automatic Calibration Method
5. Analysing the Calibrated Model
6. Calibration Process Summary
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Calibration Process Summary
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Before starting...
Check Geographical Database quality & accuracy (DTM, clutter, vectors...)
Define environments (hilly, flat / urban, rural...) to specify the required number of propagationmodels 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
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Calibration Process Summary
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Run the automatic calibration
Display statistics and compare results with target values (Std deviation and Mean
for calibration sites: Global and Individual checkingfor verification sites: Global checking
Extrapolate non-calibrated clutter losses
Analyse calibrated model
Display statistics
h k rr la i n
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 networks transmitters (Transmitter Properties\Propagation tab)
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THANK YOU!
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