AIRCOM - Cingular Model Tuning Guidance

 

 AIRCOM – Cingu Cingular  lar  Model Tuning Guidance

December 2004  

 Agenda •Model Calibration Experience • Model Calibration Process • Model Calibration Analysis

December 2004  

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December 2004  

CW and Model Tuning References • • • • • • • • • • • • •

3GIS (Sweden) – UMTS, 6 models Belgacom (Belgium) – GSM 900, 6 models

•TMN (Portugal) – GSM900, 1 model •Mascom (Botswana) – GSM 900 Swisscom (Switzerland) – GSM 900/1800 and • CHT Taiwan UMTS, 9 models Inventis (Switzerland) – GSM R, 3 models • Brazil – GSM 900/1800, 5 models Vodafone (Malta) – GSM 900, 2 models • India BPL – GSM 900, 1 Model Globul (Bulgaria) – GSM 900, GSM1800 • AWS (USA) – GSM 1900, 2 models Oniway (Portugal) – UMTS, 4 models •TCI (Iran) – GSM 900, 5 models Inquam (Portugal) – CDMA2000 •ESAT Digifone (Ireland) – UMTS 3 models Blu (Italy) – GSM 1800 •Safaricom (Kenya) GSM 900 – 2 models Nortel (UK) – GSM 1800 Ericsson (UK) – GSM 1800 •Lucent (Riyadh) GSM 900 – 1 model Dolphin (Belgium, Uk) – Tetra models KPN Base (Belgium) – GSM 900/1800, 8 Models, 4 for each

• Claro (Brazil) GSM1800 – 3 models •Globe (Phillipines) GSM900 – 3 models

December 2004  

 o c e s s  P r o  P

December 2004  

CW Measurements and Model Calibration Propagation Model Requirements Identification

ProcessDrive Route

Site Selection

Definition CW Survey Campaign Data Post Processing Data Data Validation Validation

Calibration

NO

 YES Report

Pass Model?

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 

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

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Within geographic region of model

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Distribution for Site Selection Spread of site heights representative of networkHeight sites heights within

modelled region  Allow measurements in all clutter types

1

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1

Rooftop sites are preferred preferred in a case test transmitter has to be mou mounted nted 1

Ease of access

  y 1   c   n   e   u   q   e   r    F 1

Frequency

No blocking objects in close vicinity Nothing unusual, we are characterising the majority of the network not the minority 1

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1

1 1

1 1

1 1

1 1

1 1

1 1

1 1

-1 Height of Site

1 1

1 1

1 1 1

More

December 2004  

CW Drive Route Definition •

•

Distance 

Must account for expected coverage propagation



Must account for expected interference propagation

Clutter  

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Roads 

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•

Sufficient measurement in all local clutter types ( >1000 )

 Avoid street street canyons, canyons, tunnels, tunnels, elevated 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.

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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

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Data in regions of terrain slope variation  Avoid large large blocking blocking objects objects as high building building or long roo roof  f 

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Long enough to ensure sufficient data is captured

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Check map data validity

December 2004  

CW Measurements •

•

Spectrum clearance  During CW survey allocated test frequency shouldn’t be use for other purposes 

10-15KHz bandwidth monitoring

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Check restrictions on test frequency TX EIRP

Equipment configuration 

RF Signals  Accurate  Accura te Radiated Radiated Power setting, EiRP EiRP should should be gr greater eater than 40dBm

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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 

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Do not drive out of RX noise floor   Avoid street street canyons, canyons, tunnels, tunnels, elevated elevated roads, cuttings etc Distance/Time triggering

Omni Antenna with Transmitter  attached through feeder.

In Vehicle, Receive equipment attached to roof  mounted antenna

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. d istribution. Okumura-Hata is log normal distribution

L

L

December 2004  

Distance triggering vs time triggering • • • •

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Distance triggering allows us to easily apply Lee criterion. Time triggering is very difficult to follow f ollow 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 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 

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20km per site for 1800MHz range.

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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.

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The more data the model is more realistic

December 2004  

Data Post processing •

Depends on customer requirements:   Averaged  Average d Measuremen Measurements ts – post processing processing invol involves ves simple simple conversion conversion into Signia format supporte supported d by Enterprise 

Signia data file ( .dat ) contains longitude, latitude latit ude (decimal degrees) and received level (dBm)

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Every data file must have header file with identical name but with extension .hd.

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Header file must have antenna type (identical name to one in Asset3g), Tx power, Tx antenna height, coordinates.

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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 where Tx-Tx power(dBm), Ct-cable loss between transmitter and antenna (dB),  Atg-transmitting  Atg-trans mitting an antenna tenna ga gain in (dBi)  Arg-receiving  Arg-rece iving antenna 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

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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 t han 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.

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Unusually weak signal level can be caused by driving behind blocking object.

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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 MeasurementsCW Signal

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To set up thresholds double click on CW Signal and specify thresholds under Categories tab

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The same goes for other options inside CW Measurements

December 2004  

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December 2004  

Okumura-Hata •

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

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It is semi-empirical model.

• •

Based on Okumura in Tokyo in 1968 mathematical model was publishedmeasurements in 1980 by Hata. Limitations: 

Up to 2GHz

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No less than 1km

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Transmitter antenna always above mobile station antenna

December 2004  

Okumura-Hata in Asset •

• •

 Asset uses slightly modified Okumura-Hata: Okumura-Hata: 

Ploss =K1 + K2*log(d) + K3*Hms + K4*log(Hms) + K5*log(Heff) + K6*log(Heff)*log(d) K6*log(Heff )*log(d) + K7*Ldiff + Lclutter 

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d is distance in km between Tx antenna and mobile station

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Hms is mobile station height

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Heff is effective antenna height in metres

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Ldiff is a loss due to diffraction

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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.

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Through Clutter Loss – takes into the account clutter profile along distance d from mobile station to base station.

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 Advantages in improved accuracy/reduced standard deviation 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.

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Through clutter losses are linearly lin early 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

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Load CW data

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Make appropriate filtering, usually: 

-110dBm to -40dBm

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125m to 10000

• • •

Start with the default values for k parameters

•

Take note of second and third thi rd best

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Do Auto Tune Try all combination of effective antenna height and diffraction algorithms and determine which one gives the lowest standard deviation

December 2004  

CW Window • •

3g/Asset-Tools-Model Tuning

•

Highlight Site ID and click Remove button to remove particular file

Click Add to add measurements file from its destination, they mast have extension .hd

December 2004  

Model setting • •

Tools-Model Tuning-Options

•

Select the model as a start tuning model. It is recommended to use default model

Select the resolution of mapping data

December 2004  

Filter setting • • •

Tools-Model Tuning-Options-Filter 

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Filter out clutter with insufficient data types by highlighting them

• •

If you tune k7 click just NLOS

Set up distance filtering Set up signal level filtering

Click antenna button if directional antennas were used

December 2004  

Auto Tune • • •

Tools-Model Tuning-Auto Tune

• • •

Click Auto Tune under Tools tab

•

Through clutter offsets and offsets are under Clutter tabclutter 

Set up deltas Click fix box next to the k factor you don’t want to tune Wait for results You can apply new parameters by clicking apply new n ew parameters

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.

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K7 is the diffraction parameter. It can be determined by tuning just NLOS data.  All K parameters must keep the the same polarity as in the original Okumura Hata model

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K1, K2, K7 >0

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•

K3, K5, K6 11

1

1 1

1 1

1

   l    t    l    h   p   a    t  e  r    t    k  s    i  g     i  a    b  a  n    b  a  n    i  n  g    g    e  s    t  r    i  a    b  a  n   r  e  s   m   s  e   a   r    f  o   a  r    h   r   r      l  d    i   a   u  s    i    l   p   w    i  d  e  n   u   u   u    l   n   w  a         u   v   a   s   n   d   e   n    b    i    s    b    i  n   e  a   e  n  s   r  e   u  r   e  n   m   e     d   s   o  p   n   d  e Clutter type

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December 2004  

Statistical Breakdown for Coastal Urban 15m  No. of Bins Bins

Mean Error

Standard Standa rd Deviation 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

Mean error vs distanc 1 .1

1 1

1

1 1 .1

  r 1   o   r   r   e  1  1  1. 1   1   n . 1  1   a  1   e    M -1.1

 1 1 1  1 1 .

-

.

 1 1 1 .

-

 1 1 -

-1 1

-1 1

1  -1 1

  n   o    i    t   a    i   v   e    d    d   r   a    d   n   a    t    S

1 1 1 1 1

-1

1

-1.1

1

 1  1  1 1 1  1  1 1 .

-1

Distance (km)

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.

 1 1 1 .

-

-1 1

Distance (km)

 1  1 1 -

December 2004  

Validation of Tuned Model-Site 1 Bins Mean Error  No. of Bins Error

Apoview site

Standard Deviation Standard Deviation Actual

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

Calibration whole range

December 2004  

Coverage plot – Site 1

December 2004  

Validation of Tuned Model-Site 2  No. of Bin Binss

Mean Error

Banawa site

Standard Standa rd Deviation Deviation Actual

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

Calibration whole range

December 2004  

Coverage plot – Site 2