AIRCOM
AIRCOM
Model Tuning Guidance
Model Tuning Guidance
Wednesday 13
y
thSeptember 2006
p
Model Tuning
To learn how to tune the ASSET Propagation Models To learn how to tune the ASSET Propagation Models
M d li
• Modeling
• Model Calibration Process • Model Calibration
• Typical Results • Model Validation • Recommendations
Modelling !
•
What is Modeling?
• The Purpose of a Model
M d l C it i
• Model Criteria
The Purpose of a Model
•
Characterise the topology with network limits – identification of operating range for each model.• Minimise Standard Deviation Error. P id
• Provide zero mean error
• Determine model parameters in accordance to realistic propagation effects existing within proposed regions
within proposed regions.
• Make sure calibrated model corresponds well with the collected data – data is essential.
The Purpose of a Model
▪ To predict the receiving signal strength from a Base Station (BTS)
▪ To help with the Radio Plan without the need for an individual CW measurement verification
▪ Most steps in the planning of a network are highly dependent on theMost steps in the planning of a network are highly dependent on the accuracy of the model. e.g.
C
▪ Coverage
▪ Traffic Analysis
▪ Frequency Planning ▪ Parameter Analysis
Model Criteria
▪ Accurate close to and far from the site
(DISTANCE INDEPENDENT)
▪ Accurate in hilly as well as flat areas
(TERRAIN INDEPENDENT)
▪ Accurate in Urban as well as in open areas
▪ Accurate in Urban as well as in open areas
(CLUTTER INDEPENDENT)
▪ Accurate for varying antenna heights
(ANTENNA INDEPENDENT)
▪ Applicable in different areas with similar characteristics
(AREA INDEPENDENT) (AREA INDEPENDENT)
▪ Have an overall RMS error of between 6 and 8 dB.
Okumura-Hata Model
• Okumura conducted propagation tests for land-mobile radio service in Japan.
mobile radio service in Japan.
• Curves were produced which allowed the estimation of field strength at different distances estimation of field strength at different distances from the transmitter
H t th l d Ok ’ k d
• Hata then analysed Okumura’s work and presented it in a mathematical formula.
Okumura-Hata in Asset
Asset uses slightly modified Okumura-Hata:Asset uses slightly modified Okumura-Hata:
▫ Ploss =K1 + K2*log(d) + K3*Hms + K4*log(Hms) + K5*log(Heff) + K6*log(Heff)*log(d) + K7*Ldiff + Lclutter
▫ d is distance in km between Tx antenna and mobile station
▫ d is distance in km between Tx antenna and mobile station
▫ Hms is mobile station height
▫ Heff is effective antenna height in metres
▫ Ldiff is a loss due to diffraction
▫ Lclutter is a clutter loss
▪ Asset has 4 algorithms for calculating effective antenna heightg g g
▪ Absolute
▪ Average
▪ Relative
▪ Slope
▪ Asset has 4 algorithms for calculating diffraction
▪ Epstein-PetersonEpstein Peterson
▪ Bullington
▪ Deygout
K parameters
▪ K1 and K2 Intercept and Slope. These factors correspond to a constant offset (in p p p ( dBm) and a multiplying factor for the log of the distance between the base station and mobile.
▪ K3 and K4 relate to the mobile height and how it affects the path loss. Since the MS height is normally fixed (e.g. 1.5m) these two terms in the equation become MS height is normally fixed (e.g. 1.5m) these two terms in the equation become constants. They only require calibration if you employ a variable mobile height.
▪ K5 and K6 are very important parameters since they relate to the effective base station antenna height, and how this affects the path loss. These values are difficult to calibrate without gathering data at a wide variety of base station heights. The to calibrate without gathering data at a wide variety of base station heights. The default Hata values are K5=-13.82 and K6=-6.55. If sufficient data has been gathered then these can be calibrated (one at a time) by an iterative process of incremental changes and reanalysis until the standard deviation of the error is minimized.
▪ K7 (Diffraction Parameter)
▪ Diffraction effects occur only where there is no line of sight (LOS) from the site to the mobile. Therefore, in order to determine the K7 parameter the survey data
needs to be filtered to exclude the LOS data needs to be filtered to exclude the LOS data.
▪ All K parameters must keep the same polarity as in the original Okumura Hata model
▫ K1, K2, K7 >0K1, K2, K7 0
▫ K3, K5, K6 <0
▪ Above step can be easily fulfil by determining the delta range under Auto tune window
General Principles.
Intercept
Models are generally based on the principle that the level (measured in dB) falls in a linear fashion with distance from the transmitter This is p
Offsets Caused By Clutter etc.
distance from the transmitter. This is represented by a term in the model of Klog(d) where K is the slope.
At some distance from the transmitter the level is set to a fixed value This
e
Level
Slope
the level is set to a fixed value. This takes the form of a “magic number” and is known as the intercept.
An offset may be applied for effective base station antenna height or mobile
Receive
Slope base station antenna height or mobile
effective antenna height all along the path.
“Local” offsets may be applied to the model at different points to reflect the model at different points to reflect the effects of different clutter types at different points along the path or the effects of a diffracted path i.e..
shadowing by terrain or other
Distance from Base Station
g y
Asset improvements
▪ K1 near and k2 near are designed to overcome Okumura-Hata limitation for close distances.
▪ Through Clutter Loss – takes into the account clutter profile along distance d from mobile station to base station.
▪ Advantages in improved accuracy/reduced standard deviation error and more realistic calculated predictions
Through Clutter Model Definition
Each clutter category is given Through Clutter Loss (dB/km) on the path between transmitter and receiver.
Through clutter losses are linearly weighted. The clutter nearest the mobile station has the highest effect.
CW Measurements and Model Calibration
Process
Process
Site Selection Drive Route
Definition CW Survey Propagation Model Requirements Identification y Campaign Data Post Processing Data ValidationValidationData
NO YES
Calibration
NO YES
Tuning A Model.
▪ Path Loss Slope.p ▪ Path Loss Intercept. ▪ Clutter Values.
▪ Diffraction Loss.
▪ Effective Antenna Height.
Eff ti G i Of M bil A t
▪ Effective Gain Of Mobile Antenna. ▪ Path Clutter.
Path Loss Slope.
The diagram represents a number of
signal level measurements taken atg
various points within the coverage area of a cell. In practice there would be over a thousand of these measurements.
It is possible to draw a straight linep g
through this plot that will show the underlying slope of the level/distance characteristic. To test the accuracy of the line that has been drawn it is necessary
to calculate the error at every
l (dBm)
to calculate the error at every
measurement point and hence a mean error.
If the line that had been drawn was
ed Leve
If the line that had been drawn was
the blue one instead of the red one there is obviously an error. If the mean error is calculated, because there are both positive and negative errors, it will come
Measur
e p g ,
to zero. To test the slope, therefore, the RMS error must be calculated.
Path Loss Intercept.
The slope of the line is now fixed.p
It is possible to move the line up or down on the plot. If this is done and the mean error, between the line and the actual measurements is
)
and the actual measurements, is calculated it is possible to place the line so that there is close to zero mean error. The diagram shows a red line with the correct offset and
el (dBm
red line with the correct offset and a blue line with an incorrect offset.
It is now possible to mark the plot at a fixed distance from the base
red Lev
e at a fixed distance from the base
station and to obtain a value in dBm for the intercept point. This point is shown marked in green on the diagram
Measu
r the diagram.
The slope and intercept values have now been calculated and may be used in the propagation model.
Clutter Values.
The local variations in level may be due to clutter at the mobile location.
In this slide the samples have been color coded to indicate the type of clutter present
)
coded to indicate the type of clutter present at each sample site. This helps in deciding what sort of value to assign to each sort of clutter.
el (dBm
)
Having assigned clutter values, the model must be run and its predictions compared with the real measurements. The calculation of mean errors in different types of clutter
red Lev
e of mean errors in different types of clutter
and the standard deviation of errors enables these values to be fine tuned. There is also an overall clutter weighting to be assigned.
Measu
Diffraction Loss.
▪ Drawing a Path Profile identifies diffracted paths ▪ Drawing a Path Profile identifies diffracted paths
▪ Diffraction problems are handled as single or multipleDiffraction problems are handled as single or multiple knife edges
Effective Antenna Height.
Relative Method (Effective Height)
The Relative method calculates the effective antenna height as follows:
H eff = H b+H ob-H 0m (for H 0b > H 0m)
H eff = H b (for H 0b < = H 0m)
Where:
H b : is the base station antenna height above ground
H ob : is the ground height at the base station
H 0m : is the ground height at the mobile
Note: The algorithm already takes into account the affect of earth curvature. The
Effective earth radius is set in the propagation model parameters. Here is an illustrative diagram of the Relative Method:
Path Clutter Factors.
▪ Clutter may be considered over a larger area than the point at which the mobile is located
which the mobile is located.
▪ Clutter Height may be added to Terrain Height to calculate ▪ Clutter Height may be added to Terrain Height to calculate
Site Selection
M 8 it d l L b f it b id d if
▫ More or 8 sites per model. Less number of sites can be considered if modelled geographical area is fairly small.
▫ Within geographic region of model
Height Distribution for Site Selection
5 6
▫ Spread of site heights representative of network sites heights within modelled region
▫ Allow measurements in all clutter types
2 3 4 re q ue nc y Frequency
o easu e e ts a c utte types
▫ Rooftop sites are preferred in a case test transmitter has to be mounted Ease of access 0 1 2 10 20 30 40 50 60 70 80 90 100 More F r ▫ Ease of access
▫ No blocking objects in close vicinity
▫ Nothing unusual, we are characterising the majority of the network not
-1
Height of Site
g g j y
the minority
CW Drive Route Definition
istance istance
▫ Must account for expected coverage propagation ▫ Must account for expected interference propagation lutter
▫ Sufficient measurement in all local clutter types ( >1000 ) oads
▫ Avoid street canyons, tunnels, elevated roads, cuttings etc Mix of radial and tangential roads
▫ Mix of radial and tangential roads Miscellaneous
▫ Do not plan a map along the roads with ground height above the transmitter antenna. Okumura- Hata model can’t model this. ▫ Good balance between measurements taken in LOS and NLOS
situations
▫ Do not plan a route across a big water surface, if site is on the one side of the lake, do not drive other lake side
Data in regions of terrain slope ariation ▫ Data in regions of terrain slope variation
▫ Avoid large blocking objects as high building or long roof ▫ Long enough to ensure sufficient data is captured
CW Measurements
Spectrum clearancep
▫ During CW survey allocated test frequency shouldn’t be use for other purposes ▫ 10-15KHz bandwidth monitoring
▫ Check restrictions on test frequency TX EIRP
E i t fi ti RF Signals
Equipment configuration
▫ Accurate Radiated Power setting, EiRP should be greater than 40dBm ▫ Raw/Averaged data
▫ Use Omni antenna with minimum vertical beamwidth of 12 degrees
▫ Directional antenna can be used but in postproccessing everything beyond 3dBm should be dismissed Driving
▫ Do not drive out of RX noise floor
▫ Avoid street canyons tunnels elevated roads cuttings etc
In Vehicle,
▫ Avoid street canyons, tunnels, elevated roads, cuttings etc ▫ Distance/Time triggering Omni Antenna with Transmitter attached through feeder. In Vehicle, Receive equipment attached to roof mounted antenna
Sampling - Lee Criteria
L C it i I d t li i t f t f di f t
Lee Criteria – In order to eliminate fast fading from measurements, minimum 36 samples should be taken over 40λ. A local mean should be found for the chosen number of samples.
Common practice is to take 50 samples which gives one sample every 0.8λ.
50 samples should be averaged and give the local mean 50 samples should be averaged and give the local mean.
Slow fading vs Fast fading
▪ Fast fading is fading due to multipath effect.
▪ Fast fading is characterized by Rayleigh probability distribution therefore can’t beFast fading is characterized by Rayleigh probability distribution therefore can t be modelled by log normal distribution.
▪ Fast fading is superimposed onto signal envelope (slow fading) which we try to model.
▪ Slow fading is fading due to terrain and clutter.
▪ Slow fading follows log normal distribution.
▪ Okumura-Hata is log normal distribution
Distance triggering vs time triggering
Di t t i i ll t il l L it i
Distance triggering allows us to easily apply Lee criterion.
Time triggering is very difficult to follow Lee criterion due to change in drive vehicle speed. p
Sampling in time triggering is not a problem since Lee states just minimum number of samples.
A i 40 λ i bl t i l t i ti t i i i
Averaging over 40 λ is problem to implement in time triggering since there is not constant number of samples over 40 λ caused by speed variation.
Total driving route per model
I d f d l t b li ti t ti ti ll ffi i t b f d t
In order for model to be realistic, statistically sufficient number of data need to be collected.
Aircom practise is to have at least 30000 data.p
If this distance is not achievable due to limitation in drivable roads it is recommended to have more than 8 sites per model.
A t t d b f i f d lli ll hi l ith
As stated before, in a case of modelling small geographical area with less sites, tuning can be performed with 10000 data per site.
Data Post processing
Depends on customer requirements:
▫ Averaged Measurements – post processing involves simple conversion into Signia format supported by Enterprise
▫ Signia data file ( .dat ) contains longitude, latitude (decimal degrees) and received level (dBm)
E d t fil t h h d fil ith id ti l b t ith t i hd
▫ Every data file must have header file with identical name but with extension .hd.
▫ Header file must have antenna type (identical name to one in Asset), Tx power, Tx antenna height, coordinates.
▫ It is common practice to include all gains and losses under Tx power value and leave other fieldsIt is common practice to include all gains and losses under Tx power value and leave other fields relevant to gain/losses in the header blank. Therefore in a Tx field usually is put:
Tx – Ct +Atg –Arg+Crl where Tx-Tx power(dBm),
Ct-cable loss between transmitter and antenna (dB), Atg-transmitting antenna gain (dBi)
Arg-receiving antenna gain (dBi)
Crl-cable loss between receiver and receiving antenna (dB)
It is important to get the projection system correctly so collected samples are lined up with the vectors in map data If vectors are not aligned with measurements during post process this should vectors in map data. If vectors are not aligned with measurements, during post process this should be adjusted.
CW Data Validation
Compare the site data (photographs, surrounding lutter and terrain profile) to the Clutter and DTM lutter and terrain profile) to the Clutter and DTM ayer of the map data provided.
Check the driven routes against vectors within the Check the driven routes against vectors within the map data.
ilter out any invalid data that may cause anomalies ilter out any invalid data that may cause anomalies
n the calibration process
Make sure that details relating to a site (EIRP Make sure that details relating to a site (EIRP,
ocation, Height, Antenna file) correspond to reports rom CW Survey.
se Asset utilities to get visual representation of the eceived signal vs distance.
Data filtering
Filter clutter types that have less than 500 bins Clutter offsets or them Filter clutter types that have less than 500 bins. Clutter offsets or them will be estimated later in the model tuning process.
Filter out any file which shows extreme in signal level.
Unusually high signal level at far distance can be caused by reflection over big water surface, or driving along route which is higher than
antenna.
Unusually weak signal level can be caused by driving behind blocking object.
Okumura –Hata can’t model above situations, therefore these data , must be filtered out.
With careful route planning filtering can be avoided.
Having more than one file per site makes filtering during post Having more than one file per site makes filtering during post processing much easier
Displaying CW measurements in Asset
▫ Data Types-CW Measurements-CW Signalg
▫ To set up thresholds double click on CW Signal and specify
thresholds under Categories tab
▫ The same goes for other options inside CW Measurements
CW Window
▪ 3g/Asset-Tools-Model Tuning Cli k Add t dd t
▪ Click Add to add measurements file from its destination, they mast have extension .hd Hi hli ht Sit ID d li k
▪ Highlight Site ID and click Remove button to remove particular file
Model setting
▪ Tools-Model Tuning-Options S l t th l ti f i
▪ Select the resolution of mapping data
▪ Select the model as a start
t i d l It i d d
tuning model. It is recommended to use default model
Filter seting
▪ Tools-Model Tuning-Options-Filter
Filter
▪ Set up distance filtering
▪ Set up signal level filtering
▪ Filter out clutter types with
insufficient data by highlighting them
▪ If you tune k7 click just NLOS
▪ Click antenna button if
Auto Tune
▪ Tools-Model Tuning-Auto Tune S t d lt
▪ Set up deltas
▪ Click fix box next to the k factor you don’t want to tune
▪ Click Auto Tune under Tools tab
▪ Wait for results
You can apply new parameters
▪ You can apply new parameters by clicking apply new
parameters
Through clutter offsets and
▪ Through clutter offsets and clutter offsets are under Clutter tab
Overview of Model Calibration
▪ There must be project set up (map data antennas sites propagation
▪ There must be project set up (map data, antennas, sites, propagation model) in order to start tuning
▪ Load CW data
Make appropriate filtering usually:
▪ Make appropriate filtering, usually:
▫ -110dBm to -40dBm
▫ 125m to 10000
▪ Start with the default values for k parameters
▪ Do Auto Tune
▪ Try all combination of effective antenna height and diffraction y g algorithms and determine which one gives the lowest standard deviation
▪ Take note of second and third best .
k1,k2 near calibration
▪ If model is not good close to the site, for example up to 700m, auto tune the model from 700m to 10k Apply found k
auto tune the model from 700m to 10k. Apply found k parameters.
▪ Tune model again with k5,k6 and k7 locked and filter out
di b 700
distances above 700m.
▪ Result will be k1near and k2 near.
If standard deviation is still bad try with other distances until you ▪ If standard deviation is still bad try with other distances until you
Clutter offset
▪ Some through clutter offsets and clutter offsets need to be estimated due to insufficient data
estimated due to insufficient data.
▪ Estimation is done relative to the clutter offsets with sufficientEstimation is done relative to the clutter offsets with sufficient data.
▪ Clutter offsets must be realistic relative to each other.
W t ill h th ll t ff t hil b ildi d f t th
▪ Water will have the smallest offset while building and forest the highest.
Adjusting ME
▪ Mean error is usually altered after estimation of clutter offsets. ▪ ME can be easily bring back to 0 by changing k1
Model analyses
▪ Make statistical analyses for ME and SD for different distance ranges
ranges.
▪ In the range of interest, typically 1km to 4km, following requirements should be fulfilled
▫ -1 < ME < 1
▫ SD < 8
If ME SD i t id th b ifi d l t ith
▪ If ME or SD is outside the above specified values, try with
changing the dual slope distance or take the second best model from the initial tuning.
Live sites signal Vs Predicted signal Comparison Plot
Sites Details
Over shoot signal from DXB3208 and DXB3005
Live sites signal Vs Predicted signal Comparison Plot
Over shoot signal from DXB3208 and DXB3005
