Radio propagation channelRadio propagation channelModel tuning overviewModel tuning overview

29 Sep 2009

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 of

UM calculation

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PGM overview PGM overview –– (1)(1)

PGM overview PGM 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 rx

antenna

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

CRCCRC--predict4 predict4

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Huygen principle (vector summation of secondary radiation sources)

Map pixel

Clutter effect specified as clear distance and obstacle height to receiver

Comparison of PGM and CRC predict4Comparison of PGM and CRC predict4

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PGM CRC predictApplication 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, NOT DT data

Model Similar to COST231/Hata-Okumura, slope-based model with various K parameters

Deterministic model based on Physical optics to calculate diffraction over terrain/clutter

Receiver height Different value assigned to each clutter

All mobile have same height for all clutter class

Auto tune tool Optimize K, clutter absorption/ separation

Optimize clutter absorption property

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

Model tuning work flow Model tuning work flow –– (1) import survey(1) import survey

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Model tuning work flow Model tuning work flow –– (2) create header(2) create header

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Select sector which survey belongs to

Survey changes color after header is generated

Site configuration is assigned to that survey

Model tuning work flow Model tuning work flow –– (3) filter survey(3) filter survey

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Extract valid survey data for model tuning

Model tuning work flow Model tuning work flow –– (4) average survey(4) average survey

• Remove fast Rayleigh fading à 10~20 λ or about 2m for 2.5GHz

• Average by distance to avoid bias effect à ½ or 1/3 of map pixel or 5m (use 2m since it is smaller)

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Model tuning work flow Model 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

Survey histogram (after filter and averaging)Survey histogram (after filter and averaging)

Dense urban sites

urban sites

suburban sites

Smooth monotonically rolloff on both ends of dBm

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

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

Model tuning (1) Model tuning (1) –– create create untuneduntuned versionversion

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•Clutter separation ~ 1 or 2 pixel distance, depending on environment•Most clutter have some diffraction loss (except water)•For PGM à Diffraction loss is pre-calculated based on clutter separation/height, only clutter absorption loss is tuned

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 pass•Optional 2nd step à after running AMT, run CAL tuner to tune CAL only with fixed K values obtained from AMT

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 error•If negative model error àmodel is over-predicting (i.e., predicted dBm is higher than survey)•CAL is only calculated if survey available in that clutter class (if no survey, set to 0 by default)•If clutter separation is too short, diffraction loss calculated will 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 à give -/+ clutter absorption loss

Model tuning (4) Model tuning (4) –– check error between survey check error between survey vsvsprediction based on tuned modelprediction based on tuned model

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Rerun prediction using tuned model, and check error for each sector

Rule of thumb à <3dB mean, <9dB std

Model tuning Model 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

Model tuning Model 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