45774201 Model Tuning Presentation Procedure Compatibility Mode

AIRCOM Model Tuning Guidance Wednesday y 13th September p 2006 Raju. Chukkana

Model Tuning To learn how to tune the ASSET Propagation Models

• Modeling M d li

• Model Calibration Process • Model Calibration • Typical Results • Model Validation • Recommendations

Modelling !

•

What is Modeling?

• The Purpose of a Model • Model M d lC Criteria it i • Propagation Models

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

• Minimise Standard Deviation Error. • Provide P id zero mean error • Determine model parameters in accordance to realistic propagation effects existing within proposed regions. 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 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

(CLUTTER INDEPENDENT)

▪ Accurate for varying antenna heights

(ANTENNA INDEPENDENT)

▪ Applicable in different areas with similar characteristics

(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 landmobile radio service in Japan. • Curves were produced which allowed the estimation of field strength at different distances from the transmitter • Hata H t th then analysed l d Okumura’s Ok ’ work k presented it in a mathematical formula. • It requires some correction factors

and d

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 g for calculating g effective antenna height g

▪

Absolute

▪

Average

▪

Relative

▪

Slope

▪

Asset has 4 algorithms for calculating diffraction

▪

Epstein Peterson Epstein-Peterson

▪

Bullington

▪

Deygout

K parameters ▪ K1 and K2 Intercept p and Slope. p These factors correspond p to a constant offset ((in ▪ ▪

▪ ▪ ▪

▪

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 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 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. data All K parameters must keep the same polarity as in the original Okumura Hata model ▫ K1, K2, K7 >0 0 ▫ K3, K5, K6 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. located

▪ Clutter Height may be added to Terrain Height to calculate

obstruction losses.

Site Selection ▫ More M or 8 sites it per model. d l Less L number b off sites it can be b considered id d if

modelled geographical area is fairly small. ▫ Within geographic region of model Height Distribution Site Selection ▫ Spread of site heights representative of network sitesforheights within

modelled region

6

▫ Allow o measurements easu e e ts in a all c clutter utte types 5

▫ Rooftop sites are preferred in a case test transmitter has to be 4

▫ Ease of access

3

Frrequency

mounted

Frequency 2

▫ No blocking objects in close vicinity

1

0

▫ Nothing g unusual, we are characterising g the majority j y of the network not 10

the minority

20

30

40

50

60

70

80

-1 Height of Site

▫ Add Panoramic photographs at every 45 degree interval

90

100

More

CW Drive Route Definition

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

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 ariation ▫ Avoid large blocking objects as high building or long roof ▫ Long enough to ensure sufficient data is captured ▫ Check map data validity

CW Measurements Spectrum p 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

E i Equipment t configuration fi ti

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 canyons, tunnels tunnels, elevated roads roads, cuttings etc ▫ Distance/Time triggering

Omni Antenna with Transmitter attached through feeder.

In Vehicle, Receive equipment attached to roof mounted

Sampling - Lee Criteria Lee C L Criteria it i – In I order d to t eliminate li i t ffastt fading f di from f measurements, t 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 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 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 triggering Di t Distance triggering ti i allows ll us tto easily il apply l L Lee criterion. it i Time triggering is very difficult to follow Lee criterion due to change in p drive vehicle speed. Sampling in time triggering is not a problem since Lee states just minimum number of samples. Averaging A i over 40 λ is i problem bl tto iimplement l t iin ti time ttriggering i i since i there is not constant number of samples over 40 λ caused by speed variation. Whenever possible choose distance triggering.

Total driving route per model IIn order d for f model d l tto be b realistic, li ti statistically t ti ti ll sufficient ffi i t number b off d data t need to be collected. practise is to have at least 30000 data. Aircom p If this distance is not achievable due to limitation in drivable roads it is recommended to have more than 8 sites per model. As stated A t t d before, b f in i a case off modelling d lli smallll geographical hi l area with ith less sites, tuning can be performed with 10000 data per site. The more data the model is more realistic

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 E d data t fil file mustt have h h header d fil file with ith id identical ti l name b butt with ith extension t i .hd. 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 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 data. If vectors are not aligned with measurements 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 ayer of the map data provided.

Check the driven routes against vectors within the map data.

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

Make sure that details relating to a site (EIRP (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. 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

Filtering example-Driving above Tx antenna

Filtering example-Blocking object

Displaying CW measurements in Asset ▫ Data Types-CW Measurements-

g 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

CW Window ▪ 3g/Asset-Tools-Model Tuning ▪ Click Cli k Add tto add dd measurements t

file from its destination, they mast have extension .hd

▪ Highlight Hi hli ht Sit Site ID and d click li k

Remove button to remove particular file

Model setting ▪ Tools-Model Tuning-Options ▪ Select S l t th the resolution l ti off mapping i

data

▪ Select the model as a start

ttuning i model. d l It is i recommended d d to use default model

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

Auto Tune ▪ Tools-Model Tuning-Auto Tune ▪ Set S t up deltas d lt ▪ 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

Default K parameters

Overview of Model Calibration ▪ ▪ ▪

There must be project set up (map data data, antennas antennas, sites sites, propagation model) in order to start tuning Load CW data Make appropriate filtering filtering, usually: ▫ ▫

▪ ▪ ▪ ▪

.

-110dBm to -40dBm 125m to 10000

Start with the default values for k parameters Do Auto Tune Tryy all combination of effective antenna height g and diffraction 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. 10k Apply found k parameters.

▪ Tune model again with k5,k6 and k7 locked and filter out

di distances above b 700 700m.

▪ Result will be k1near and k2 near. ▪ 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 data data.

▪ Estimation is done relative to the clutter offsets with sufficient

data.

▪ Clutter offsets must be realistic relative to each other. ▪ Water W t will ill h have th the smallest ll t offset ff t while hil b building ildi and d forest f t the th

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

ranges. 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 i outside t id the th above b specified ifi d values, l ttry with ith

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

Live sites signal Vs Predicted signal Comparison Plot

Over shoot signal from DXB3208 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.