December 2004

AIRCOM – Cingular

Model Tuning Guidance

December 2004

Agenda

•Model Calibration Experience

• Model Calibration Process

• Model Calibration Analysis

December 2004

December 2004

CW and Model Tuning References• 3GIS (Sweden) – UMTS, 6 models• Belgacom (Belgium) – GSM 900, 6 models• Swisscom (Switzerland) – GSM 900/1800

and UMTS, 9 models• Inventis (Switzerland) – GSM R, 3 models• Vodafone (Malta) – GSM 900, 2 models• Globul (Bulgaria) – GSM 900, GSM1800• Oniway (Portugal) – UMTS, 4 models• Inquam (Portugal) – CDMA2000• Blu (Italy) – GSM 1800• Nortel (UK) – GSM 1800• Ericsson (UK) – GSM 1800• Dolphin (Belgium, Uk) – Tetra models

• KPN Base (Belgium) – GSM 900/1800, 8 Models, 4 for each

•TMN (Portugal) – GSM900, 1 model

•Mascom (Botswana) – GSM 900

• CHT Taiwan

• Brazil – GSM 900/1800, 5 models

• India BPL – GSM 900, 1 Model

• AWS (USA) – GSM 1900, 2 models

•TCI (Iran) – GSM 900, 5 models

•ESAT Digifone (Ireland) – UMTS 3 models

•Safaricom (Kenya) GSM 900 – 2 models

•Lucent (Riyadh) GSM 900 – 1 model

• Claro (Brazil) GSM1800 – 3 models

•Globe (Phillipines) GSM900 – 3 models

December 2004

December 2004

CW Measurements and Model Calibration Process

Data Validation

NOYES

Site Selection Drive Route Definition

CW Survey Campaign

Data Post Processing

Calibration

ReportPass

Model?

Propagation Model

Requirements Identification

Data Validation

December 2004

Aim of Model Calibration

• Characterise the topology with network limits – identification of operating range for each model.

• Minimise Standard Deviation Error.

• Provide zero mean error

• Determine model parameters in accordance to realistic propagation effects existing within proposed regions.

• Make sure calibrated model corresponds well with the collected data – data is essential.

• Provide cost efficient Nominal Plan

December 2004

Site Selection

Height Distribution for Site Selection

-1

0

1

2

3

4

5

6

10 20 30 40 50 60 70 80 90 100 More

Height of Site

Freq

uenc

y

Frequency

More or 10 sites per model. Less number of sites can be considered if modelled geographical area is fairly small.

Within geographic region of model Spread of site heights representative of network sites heights within

modelled region Allow measurements in all clutter types Rooftop sites are preferred in a case test transmitter has to be mounted Ease of access No blocking objects in close vicinity Nothing unusual, we are characterising the majority of the network not the

minority

December 2004

CW Drive Route Definition• Distance

Must account for expected coverage propagation Must account for expected interference propagation

• Clutter Sufficient measurement in all local clutter types ( >1000 )

• Roads Avoid street canyons, tunnels, elevated roads, cuttings etc 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 variation Avoid large blocking objects as high building or long roof Long enough to ensure sufficient data is captured Check map data validity

December 2004

Omni Antenna with Transmitter attached through feeder.

In Vehicle, Receive

equipment attached to roof

mounted antenna

RF Signals

CW Measurements• Spectrum clearance

During CW survey allocated test frequency shouldn’t be use for other purposes 10-15KHz bandwidth monitoring Check restrictions on test frequency TX EIRP

• 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 Distance/Time triggering

December 2004

Sampling - Lee Criteria• 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.

December 2004

Slow fading vs Fast fading • Fast fading is fading due to multipath effect.• Fast 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

December 2004

Distance triggering vs time triggering• 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. • Sampling in time triggering is not a problem since Lee states just

minimum number of samples.• 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.

• Whenever possible choose distance triggering.

December 2004

Total driving route per model• 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.• 30000 data gives total driven distance of

30000x40λ=198km or 20km per site for 1800MHz range.

• If this distance is not achievable due to limitation in drivable roads it is recommended to have more than 10 sites per model.

• As stated before, in a case of modelling small geographical area with 3 sites, tuning can be performed with 10000 data or 22km per site.

• The more data the model is more realistic

December 2004

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) 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 Asset3g), Tx power, Tx antenna height,

coordinates. It 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 should be adjusted.

December 2004

CW Data Validation• Compare the site data (photographs, surrounding

clutter and terrain profile) to the Clutter and DTM layer of the map data provided.

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

• Filter out any invalid data that may cause anomalies in the calibration process

• Make sure that details relating to a site (EIRP, Location, Height, Antenna file) correspond to reports from CW Survey.

• Use Asset utilities to get visual representation of the received signal vs distance.

December 2004

Data filtering• 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

processing much easier

December 2004

Filtering example-Driving above Tx antenna

December 2004

Filtering example-Blocking object

December 2004

Displaying CW measurements in Asset Data Types-CW Measurements-

CW Signal To set up thresholds double click

on CW Signal and specify thresholds under Categories tab

The same goes for other options inside CW Measurements

December 2004

December 2004

Okumura-Hata• Okumura-Hata is a worldwide the most popular model in mobile

telecommunication• It is semi-empirical model.• Based on Okumura measurements in Tokyo in 1968 mathematical

model was published in 1980 by Hata.• Limitations:

Up to 2GHz No less than 1km Transmitter antenna always above mobile station antenna

December 2004

Okumura-Hata in Asset

• 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 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 height• Asset has 4 algorithms for calculating diffraction

December 2004

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.

December 2004

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.

December 2004

Overview of Model Calibration

• There must be project set up (map data, antennas, sites, propagation model) in order to start tuning

• Load CW data• 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 algorithms and

determine which one gives the lowest standard deviation• Take note of second and third best .

December 2004

CW Window• 3g/Asset-Tools-Model Tuning• Click Add to add measurements file

from its destination, they mast have extension .hd

• Highlight Site ID and click Remove button to remove particular file

December 2004

Model setting• Tools-Model Tuning-Options• Select the resolution of mapping

data• Select the model as a start tuning

model. It is recommended to use default model

December 2004

Filter seting• Tools-Model Tuning-Options-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 directional

antennas were used

December 2004

Auto Tune• Tools-Model Tuning-Auto Tune• 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 by

clicking apply new parameters• Through clutter offsets and clutter

offsets are under Clutter tab

December 2004

K parameters• K3 and K4 are not altered. This is because they relate to mobile

height which in a typical cellular system is constant making these coefficients redundant.

• K7 is the diffraction parameter. It can be determined by tuning just NLOS data.

• All K parameters must keep the same polarity as in the original Okumura Hata model K1, K2, K7 >0 K3, K5, K6 <0

• Above step can be easily fulfil by determining the delta range under Auto tune window

December 2004

Default K parameters

December 2004

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 parameters.• Tune model again with k5,k6 and k7 locked and filter out distances

above 700m.• Result will be k1near and k2 near.• If standard deviation is still bad try with other distances until you find

the best fit.

December 2004

Clutter offset• Some through clutter offsets and clutter offsets need to be estimated

due to insufficient data.

• Estimation is done relative to the clutter offsets with sufficient data.

• Clutter offsets must be realistic relative to each other.

• Water will have the smallest offset while building and forest the highest.

December 2004

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 Δ change k1 to k1+ Δ

December 2004

December 2004

Model analyses• Make statistical analyses for ME and SD for different distance ranges.• In the range of interest, typically 1km to 4km, following requirements

should be fulfilled -1 < ME < 1 SD < 8

• 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.

December 2004

Example-Coastal Urban 900MHz15m resolution map

• Area considered: densely populated coastal cities.

• Used frequency: 935.2MHz

• Total of 10 sites were included in tuning process with 80260 points.

• Signal strenght threshold set to –40 to -110 dBm.

• Distance used for tuning from 125m to 10km.

December 2004

Data Analysis for Coastal Urban 15mDistribution of bins per signal level

-5000

0

5000

10000

15000

20000

25000

30000

-130-(-120)

-120-(-110)

-110-(-100)

-100-(-90)

-90-(-80)

-80-(-70)

-70-(60)

-60-(-50)

-50-(-40)

-40-(-30)

Signal level (dBm)

Num

ber o

f bin

s

Distribution of bins per distance

373 10302899

19351

29598

17791

891 0

8700

0

5000

10000

15000

20000

25000

30000

35000

0-0.125 0.125-0.25 0.25-0.5 0.5-1 1-2 2-4 4-8 8-16 >16

Distance (km)

Num

ber o

f bin

s

Distribution of bins per clutter type

10574

4 0

14421

26856

9056

30101062

5062

9995

189 28 0 30

5000

10000

15000

20000

25000

30000

open se

awater

residen

tial

mean ur

ban

dens

e urba

n

build

ing

villag

es

industr

ial

open

in urba

nfor

est

parks

dens

e urba

n high

swam

p

Clutter type

Num

ber o

f bin

s

December 2004

Statistical Breakdown for Coastal Urban 15m

No. of Bins

Mean Error

Standard Deviation Actual

Calibration whole range 80260 0 6.8

125~250 1030 -0.5 8.1

250~500 2899 -1.1 8

500~1km 8700 -1.4 7.7

1km~2km 19351 -0.1 7.4

2km~4km 29598 0.9 6.6

4km~8km 17791 -0.4 5.4

8km~16km 891 -1.6 5.2

December 2004

Statistical Breakdown for ME and SD

Standard deviation distribution

0

1

2

3

4

5

6

7

8

9

0.125-0.250 0.5-1 2-4 8-16

Distance (km)

Stan

dard

dev

iatio

n

Mean error vs distance

-2

-1.5

-1

-0.5

0

0.5

1

1.5

0.125-0.2500.25-0.5 0.5-1 1-2 2-4 4-8 8-16

Distance (km)

Mea

n er

ror

December 2004

Validation of Tuned Model-Site 1

Apoview siteNo. of Bins

Mean Error

Standard Deviation Actual

Calibration whole range 10668 -1 6.1

125~250 53 4.3 5.6

250~500 368 0.4 7.5

500~1km 1153 -2.7 7.3

1km~2km 2324 -1.5 6.3

2km~4km 4383 0.4 5.9

4km~8km 2343 -2.4 5.1

8km~16km 44 -2.4 4.1

December 2004

Coverage plot – Site 1

December 2004

Validation of Tuned Model-Site 2

Banawa site

No. of Bins

Mean Error

Standard Deviation

Actual

Calibration whole range 6354 0.1 6.4

125~250 95 11.6 5.2

250~500 42 2.7 5.7

500~1km 252 -1.8 7.7

1km~2km 1620 -0.9 6.3

2km~4km 3228 1 6.4

4km~8km 1041 -1.6 4.8

8km~16km 76 -2.9 3.8

December 2004

Coverage plot – Site 2