AIRCOM - Cingular Model Tuning Guidance

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 Propagation Model Requirements Identification

Drive Route Definition

Site Selection

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 of sites can be considered if modelled geographical area is fairly small. Within geographic region of model Distribution for Site Selection Spread of site heights representative of networkHeight sites heights within modelled region 6



Allow measurements in all clutter types



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



Ease of access



5

No blocking objects in close vicinity

Frequency

4

3 Frequency 2

1



Nothing unusual, we are characterising the majority of the network not the minority 0

10

20

30

40

50

60

70

80

90

100

More

-1 Height of Site

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

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 

RF Signals 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

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. 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 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 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 MeasurementsCW 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

•

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 seting • • • •

Tools-Model Tuning-Options-Filter

• •

If you tune k7 click just NLOS

Set up distance filtering Set up signal level filtering Filter out clutter types with insufficient data by highlighting them 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 clutter offsets are under Clutter tab

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 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.

• 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 16

0 0.25-0.5

0.5-1

1-2

2-4

4-8

wa te re sid r en m tia ea l n ur de ba ns n e ur ba n bu ild in g vil la ge s in du op st ria en l in ur ba n fo re st de ns p a e rk ur s ba n hi gh sw am p

19351

20000

se a

25000

op en

Number of bins

4

29598

30000

9995

9056

10000

Clutter type

Distance (km)

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 Mean error vs distance

Standard deviation distribution

1.5

9 8

1

Mean error

0.5

0 0.125 -0.5

0 -0.25

0.5 0.25-

0.5-1

1-2

2-4

4-8

8-16

Standard deviation

7 6 5 4 3 2

-1

1 -1.5

0

-0.2 0.125

-2 Distance (km)

50

0.5-1

2-4

8-16

Distance (km )

December 2004

Validation of Tuned Model-Site 1 Apoview site

Calibration whole range

No. of Bins

Mean Error

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

December 2004

Coverage plot – Site 1

December 2004

Validation of Tuned Model-Site 2 No. of Bins

Mean Error

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

Banawa site Calibration whole range

Standard Deviation Actual

December 2004

Coverage plot – Site 2

December 2004