AIRCOMAIRCOM

Model Tuning GuidanceModel Tuning Guidance

Wednesday 13th September 2006y p

Raju. Chukkana

Model Tuning

To learn how to tune the ASSET Propagation ModelsTo 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

• Propagation Models

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 regionswithin 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 CWmeasurement 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 theaccuracy 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.

▪ Have mean error of zero.

Okumura-Hata Model

• Okumura conducted propagation tests for land-mobile radio service in Japan.mobile radio service in Japan.

• Curves were produced which allowed theestimation of field strength at different distancesestimation of field strength at different distancesfrom the transmitter

H t th l d Ok ’ k d• Hata then analysed Okumura’s work andpresented it in a mathematical formula.

• It requires some correction factors

Okumura-Hata in AssetAsset 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▪ Japanese Atlas

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 becomeMS 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. Theto 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 dataneeds 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 theprinciple that the level (measured indB) falls in a linear fashion withdistance from the transmitter This isp

Offsets CausedBy Clutter etc.

distance from the transmitter. This isrepresented by a term in the model ofKlog(d) where K is the slope.

At some distance from the transmitterthe level is set to a fixed value This

e Le

vel

Slope

the level is set to a fixed value. Thistakes the form of a “magic number”and is known as the intercept.

An offset may be applied for effectivebase station antenna height or mobile

Rec

eive Slope base station antenna height or mobile

effective antenna height all along thepath.“Local” offsets may be applied to themodel at different points to reflect themodel at different points to reflect theeffects of different clutter types atdifferent points along the path or theeffects of a diffracted path i.e..shadowing by terrain or other

Distance from Base Station

g yobstructions.

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 predictionserror 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.

ASSET’s Propagation Models

CW Measurements and Model Calibration ProcessProcess

Site Selection Drive Route Definition

CW Survey

Propagation Model

Requirements Identification y

Campaign

Data Post Processing

Data Validation

Data Validation

NOYES

Calibration

NOYES

ReportPass

Model?

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 ofsignal level measurements taken atgvarious points within the coverage areaof a cell. In practice there would be overa thousand of these measurements.

It is possible to draw a straight linep gthrough this plot that will show theunderlying slope of the level/distancecharacteristic. To test the accuracy of theline that has been drawn it is necessaryto calculate the error at every

l (dB

m) to calculate the error at every

measurement point and hence a meanerror.

If the line that had been drawn was

ed L

eve If the line that had been drawn was

the blue one instead of the red one thereis obviously an error. If the mean error iscalculated, because there are bothpositive and negative errors, it will come

Mea

sure p g ,

to zero. To test the slope, therefore, theRMS error must be calculated.

Path Loss Intercept.The slope of the line is now fixed.p

It is possible to move the line up ordown on the plot. If this is done andthe mean error, between the lineand the actual measurements is

)

and the actual measurements, iscalculated it is possible to place theline so that there is close to zeromean error. The diagram shows ared 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

Leve

at a fixed distance from the basestation and to obtain a value indBm for the intercept point. Thispoint is shown marked in green onthe diagram

Mea

sur the diagram.

The slope and intercept valueshave now been calculated and maybe used in the propagation model.

Distance from Base Station

Clutter Values.The local variations in level may be due toclutter 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 presentat each sample site. This helps in decidingwhat sort of value to assign to each sort ofclutter.

el (

dBm

)

Having assigned clutter values, the modelmust be run and its predictions comparedwith the real measurements. The calculationof mean errors in different types of clutter

red

Leve

of mean errors in different types of clutterand the standard deviation of errors enablesthese values to be fine tuned. There is alsoan overall clutter weighting to be assigned.

Mea

sur

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

▪ An overall weighting factor must be found

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 groundH ob : is the ground height at the base stationH 0m : is the ground height at the mobileNote: The algorithm already takes into account the affect of earth curvature. TheEffective 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 locatedwhich the mobile is located.

▪ Clutter Height may be added to Terrain Height to calculate▪ Clutter Height may be added to Terrain Height to calculate obstruction losses.

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 modelHeight 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

requ

ency

Frequency

o easu e e ts a c utte types▫ Rooftop sites are preferred in a case test transmitter has to be

mountedEase of access

0

1

2

10 20 30 40 50 60 70 80 90 100 More

Fr▫ 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 ythe minority

▫ Add Panoramic photographs at every 45 degree interval

CW Drive Route Definitionistanceistance▫ 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 sideData 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▫ Check map data validity

CW MeasurementsSpectrum 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 SignalsEquipment 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 CriteriaL C it i I d t li i t f t f di f tLee 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 mean50 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

L L

Distance triggering vs time triggeringDi t t i i ll t il l L it iDistance triggering allows us to easily apply Lee criterion.Time triggering is very difficult to follow Lee criterion due to change in drive vehicle speed. pSampling 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 iAveraging 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.Whenever possible choose distance triggering.

Total driving route per modelI d f d l t b li ti t ti ti ll ffi i t b f d tIn order for model to be realistic, statistically sufficient number of data need to be collected.Aircom practise is to have at least 30000 data.pIf 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 ithAs stated before, in a case of modelling small geographical area with less sites, tuning can be performed with 10000 data per site.The more data the model is more realistic

Data Post processingDepends 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 whereTx-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 shouldvectors 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 DTMlutter and terrain profile) to the Clutter and DTM ayer of the map data provided.

Check the driven routes against vectors within theCheck the driven routes against vectors within the map data.

ilter out any invalid data that may cause anomaliesilter out any invalid data that may cause anomalies n the calibration process

Make sure that details relating to a site (EIRPMake 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 filteringFilter clutter types that have less than 500 bins Clutter offsets or themFilter 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 postHaving more than one file per site makes filtering during post processing much easier

Filtering example-Driving above Tx antenna

Filtering example-Blocking object

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 TuningCli k Add t dd t▪ Click Add to add measurements file from its destination, they mast have extension .hdHi hli ht Sit ID d li k▪ Highlight Site ID and click Remove button to remove particular file

Model setting

▪ Tools-Model Tuning-OptionsS 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 dtuning model. It is recommended to use default model

Filter seting

▪ Tools-Model Tuning-Options-FilterFilter

▪ 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

directional antennas were used

Auto Tune

▪ Tools-Model Tuning-Auto TuneS 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 parametersThrough clutter offsets and▪ Through clutter offsets and clutter offsets are under Clutter tab

Default K parameters

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 dataMake 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 kauto 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 700distances 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 find the best fit.

Clutter offset

▪ Some through clutter offsets and clutter offsets need to be estimated due to insufficient dataestimated 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

▪ If mean error is ∆ change k1 to k1+ ∆

Model analyses

▪ Make statistical analyses for ME and SD for different distance rangesranges.

▪ 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 fromDXB3208 and DXB3005

Live sites signal Vs Predicted signal Comparison Plot

Over shoot signal fromDXB3208 and DXB3005

Dxb3217 Live sites signal Vs Predicted signal Comparison Plot

Dxb3218 Live sites signal Vs Predicted signal Comparison Plot

Dxb3005 Live sites signal Vs Predicted signal Comparison Plot

Dxb3208 Live sites signal Vs Predicted signal Comparison Plot

Dxb3209 Live sites signal Vs Predicted signal Comparison Plot

Dubai Dense Urban Validation of Tuned Model-Site 8

Dubai Residential Validation of Tuned Model-Site 8

Abu Dhabi Dense Urban Validation of Tuned Model-Site 8

Abu Dhabi Residential Validation of Tuned Model-Site 8

Recommendations• Apply the model on Macro cell sites as opposed

to Micro cell or Minicell

• Update clutter classes regularly

• A Generic Model could be applied

• REMEMBER: Models are NOT perfect, Optimisation will always be required.