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Radio propagation channelRadio propagation channelModel tuning overviewModel tuning overview
29 Sep 2009
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Overview of propagation model used in planetOverview of propagation model used in planet
Input Scanner or CW drive test
Map, projection
Site configurations used (e.g, link budget, GPS)
Planet general model (PGM) Slope based Okumura-Hata type model
CRC- predict4 model Deterministic (i.e., map dependent, instead of survey), physical-optics based model
Universal model (UM) Additional license required
Unmasked and masked version
Unmasked means that antenna correction is done by planet prediction engine, instead ofUM calculation
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PGM overviewPGM overview (1)(1)
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PGM overviewPGM overview (2)(2)
Account for FSL
K1 (freq-dependent intercept),
K2 (slope)
K5 (multiplier for effective antenna height)
Effective antenna height gain
BTS ht gain side as K3
MS ht gain as K6
Clutter effect Weight factor K in last 1km to rx
Diffraction
multiply by K4 for non-LOS
Calculation based on Epstein-Peterson method for 3 diffracting edge
Use clutter height evaluate diffraction for non-LOS
Use Clutter separation as distance between last effective diffracting clutter obstruction to rxantenna
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PGM tuningPGM tuning
PGM only compute vertical diffraction
In DU/U environment where horizontal diffraction can be significant,PGM often over-estimate vertical diffraction loss
Compensate with clutter gain
PGM effective where BTS ht >= surrounding clutter
Using AMT
manual
Use Hata for K3 and K5, clutter offset = optimize
Optimize K1, K2 and K4
smart
Optimize K1 to K5 and CAL in one pass
Optional 2nd step
Fix tuned K and using clutter tuner to re-tune CAL or do manual
change
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CRCCRC--predict4predict4
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Huygen principle(vector summation of secondary
radiation sources)
Map pixel
Clutter effect specified asclear distance and obstacle
height to receiver
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Comparison of PGM and CRC predict4Comparison of PGM and CRC predict4
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PGM CRC predict
Application Urban to suburban Accurate clutter/terrain maps,secondary radiation sources
Advantage Fast, good for long distance
propagation
Detailed prediction along many
radials
Typical prediction
resolution (No of radial)
720 360
Weakness Needs more CW data to estimate
slope
Easy to tune, since accuracy
dependent on clutter/terrain,NOTDT data
Model Similar to COST231/Hata-
Okumura, slope-based model withvarious K parameters
Deterministic model based on
Physical optics to calculatediffraction over terrain/clutter
Receiver height Different value assigned to eachclutter
All mobile have same height for allclutter class
Auto tune tool Optimize K, clutter absorption/separation
Optimize clutter absorption property
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Before model tuningBefore model tuning
Add new sites
Setup link budget to get correct EIRP (e..g., PA power, pilot %, cable loss, rxantenna gain)
Add combined gain/loss = receiver antenna gain receiver cable loss,
to DL link budget for all sectors
Similar to manually adjust K1 in PGM
If scanner DT is used
Planet uses RSSI as CPICH RSCP/pilot power for CDMA based network
Allocate scanner record to sector and export as survey
If needed, combine multiple scanner log from same sector to 1 log
Create header for each survey data per sector
Filter survey data
Average survey data
Assign filtered/averaged survey data to associated sector
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Model tuning work flowModel tuning work flow (1) import survey(1) import survey
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Model tuning work flowModel tuning work flow (2) create header(2) create header
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Select sectorwhich survey
belongs to
Survey changes color
after header isgenerated
Site configuration is assignedto that survey
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Model tuning work flowModel tuning work flow (3) filter survey(3) filter survey
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Extract valid survey data formodel tuning
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Model tuning work flowModel tuning work flow (4) average survey(4) average survey
Remove fast Rayleigh fading
10~20
or about 2m for 2.5GHz Average by distance to avoid bias effect or 1/3 of map pixel or 5m
(use 2m since it is smaller)
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Model tuning work flowModel tuning work flow (5) assign to sector(5) assign to sector
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Used to compare prediction coverage with survey
data by calculating mean/std/RMS error
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Survey histogram (after filter and averaging)Survey histogram (after filter and averaging)
Dense urban sites
urban sites
suburban sites
Smooth monotonically rolloff onboth ends of dBm
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Clutter distribution (after filter and averaging)Clutter distribution (after filter and averaging)
urban sites
suburban sites
(recommend 2000~3000 sample per
clutter class for good model tuning,absolute minimum is 200~300 sample
per clutter class)
Dense urban sites
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Distance regression (after filter and averaging)Distance regression (after filter and averaging)
Dense urban sites
urban sites
suburban sites
DU model should has steepest slope(i.e., larger K2 magnitude) compared
to U and SU model
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Model tuning (1)Model tuning (1) createcreate untuneduntuned versionversion
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Clutter separation ~ 1 or 2 pixel distance, depending on environmentMost clutter have some diffraction loss (except water)
For PGM Diffraction loss is pre-calculated based on clutterseparation/height, only clutter absorption loss is tuned
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Model tuning (2)Model tuning (2) automatic model tuner (PGM)automatic model tuner (PGM)
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Use smart to tune all K values and CAL in one passOptional 2nd step after running AMT, run CAL tuner to tune
CAL only with fixed K values obtained from AMT
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Model tuning (3)Model tuning (3) verify tuned model (PGM)verify tuned model (PGM)
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Check model tuning report
K and CAL comparison BEFORE and AFTER
running AMT (automatic model tuner)
Check errorIf negative model error model is over-predicting (i.e.,predicted dBm is higher than survey)
CAL is only calculated if survey available in that clutterclass (if no survey, set to 0 by default)
If clutter separation is too short, diffraction loss calculatedwill be too high.If clutter has gain, it basically means clutter separation is
too low or clutter height too high uses clutter class with most sample as a reference to
compute K1 and compare with other clutter type give -/+clutter absorption loss
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Model tuningModel tuning (5)(5) sanity check for PGM modelssanity check for PGM models
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DU site with
PGM DU model
DU sites with
PGM U model
DU site with
PGM SU model
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Model tuningModel tuning (6)(6) predicted coverage with surveypredicted coverage with survey
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Legends for predicted coverage and
thematic map of survey are same