Model Tuning
45
December 2004 AIRCOM – Cingular AIRCOM – Cingular Model Tuning Guidance Model Tuning Guidance Thursday 2 Thursday 2 th th December 2004 December 2004
description
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
Transcript of Model Tuning
Thursday 2Thursday 2thth December 2004December 2004
l i b r a t i o n
E x p e r i e
n c e
December 2004
C and Model Tuning Re!erencesC and Model Tuning Re!erences • 3GIS S!eden" # $M%S& ' models
• (el)acom (el)i*m" # GSM +00& ' models
• S!isscom S!it,erland" # GSM +00-./00 and $M%S& + models
• Inentis S!it,erland" # GSM 1& 3 models
• odaone Malta" # GSM +00& 2 models
• Glob*l (*l)aria" # GSM +00& GSM./00
• ni!ay Port*)al" # $M%S& 4 models
• In5*am Port*)al" # CDMA2000
• (l* Italy" # GSM ./00
• 6ortel $7" # GSM ./00
• Ericsson $7" # GSM ./00
• 7P6 (ase (el)i*m" # GSM +00-./00& / Models& 4 or eac8
•%M6 Port*)al" # GSM+00& . model
•Mascom (ots!ana" # GSM +00
• A=S $SA" # GSM .+00& 2 models
•%CI Iran" # GSM +00& ; models
•ESA% Di)ione Ireland" # $M%S 3 models
•Saaricom 7enya" GSM +00 # 2 models
•<*cent 1iyad8" GSM +00 # . model
• Claro (ra,il" GSM./00 # 3 models
•Globe P8illipines" GSM+00 # 3 models
s
Data #alidation
Aim o! Model CalibrationAim o! Model Calibration
• C8aracterise t8e topolo)y !it8 net!or9 limits # identiication o operatin) ran)e or eac8 model>
• Minimise Standard Deiation Error>
• Proide ,ero mean error
• Determine model parameters in accordance to realistic propa)ation eects existin) !it8in proposed re)ions>
• Ma9e s*re calibrated model corresponds !ell !it8 t8e collected data # data is essential>
• Proide cost eicient 6ominal Plan
'ite 'election'ite 'election
More or .0 sites per model> <ess n*mber o sites can be considered i modelled )eo)rap8ical area is airly small>
=it8in )eo)rap8ic re)ion o model
Spread o site 8ei)8ts representatie o net!or9 sites 8ei)8ts !it8in modelled re)ion
Allo! meas*rements in all cl*tter types
1ootop sites are preerred in a case test transmitter 8as to be mo*nted
Ease o access
6o bloc9in) ob?ects in close icinity
• Distance
• Clutter
• Roads
Mi, o! radial and tangential roads
• Miscellaneous
Do not )lan a ma) along the roads ith ground height abo(e the transmitter antenna3 Oumura5 6ata model can7t model this3
Good balance beteen measurements taen in 8O' and $8O' situations
Do not )lan a route across a big ater sur!ace1 i! site is on the one side o! the lae1 do not dri(e other lae side
Data in regions o! terrain slo)e (ariation
A(oid large blocing ob9ects as high building or long roo!
8ong enough to ensure su!!icient data is ca)tured
Chec ma) data (alidity
In #ehicle1 Recei(e
mounted antenna
1@ Si)nals
• ')ectrum clearance
During C sur(ey allocated test !re+uency shouldn7t be use !or other )ur)oses
/05/:;6< bandidth monitoring
• &+ui)ment con!iguration
Accurate Radiated "oer setting1 &iR" should be greater than 40d>m
Ra?A(eraged data
@se Omni antenna ith minimum (ertical beamidth o! /2 degrees
Directional antenna can be used but in )ost)roccessing e(erything beyond d>m should be dismissed
• Dri(ing
Distance?Time triggering
'am)ling 5 8ee Criteria'am)ling 5 8ee Criteria
• <ee Criteria # In order to eliminate ast adin) rom meas*rements& minim*m 3' samples s8o*ld be ta9en oer 40> A local mean s8o*ld be o*nd or t8e c8osen n*mber o samples>
• Common practice is to ta9e ;0 samples !8ic8 )ies one sample eery 0>/>
'lo !ading (s Bast !ading'lo !ading (s Bast !ading
• @ast adin) is adin) d*e to m*ltipat8 eect>
• @ast adin) is c8aracteri,ed by 1aylei)8 probability distrib*tion t8ereore canBt be modelled by lo) normal distrib*tion>
• @ast adin) is s*perimposed onto si)nal enelope slo! adin)" !8ic8 !e try to model>
• Slo! adin) is adin) d*e to terrain and cl*tter>
• Slo! adin) ollo!s lo) normal distrib*tion>
• 9*m*ra:ata is lo) normal distrib*tion
8 8
Distance triggering (s time triggeringDistance triggering (s time triggering
• Distance tri))erin) allo!s *s to easily apply <ee criterion>
• %ime tri))erin) is ery diic*lt to ollo! <ee criterion d*e to c8an)e in drie e8icle speed>
• Samplin) in time tri))erin) is not a problem since <ee states ?*st minim*m n*mber o samples>
• Aera)in) oer 40 is problem to implement in time tri))erin) since t8ere is not constant n*mber o samples oer 40 ca*sed by speed ariation>
• =8eneer possible c8oose distance tri))erin)>
Total dri(ing route )er modelTotal dri(ing route )er model
• In order or model to be realistic& statistically s*icient n*mber o data need to be collected>
• Aircom practise is to 8ae at least 30000 data>
• 30000 data )ies total drien distance o
0000,40/EFm or
20m )er site !or /F00M6< range3
• I t8is distance is not ac8ieable d*e to limitation in driable roads it is recommended to 8ae more t8an .0 sites per model>
• As stated beore& in a case o modellin) small )eo)rap8ical area !it8 3 sites& t*nin) can be perormed !it8 .0000 data or 229m per site>
• %8e more data t8e model is more realistic
Data "ost )rocessingData "ost )rocessing • De)ends on customer re+uirements
A(eraged Measurements – )ost )rocessing in(ol(es sim)le con(ersion into 'ignia !ormat su))orted by &nter)rise
'ignia data !ile - 3dat contains longitude1 latitude -decimal degrees and recei(ed le(el -d>m
&(ery data !ile must ha(e header !ile ith identical name but ith e,tension 3hd3
6eader !ile must ha(e antenna ty)e -identical name to one in Assetg1 T, )oer1 T, antenna height1 coordinates3
It is common )ractice to include all gains and losses under T, )oer (alue and lea(e other !ields rele(ant to gain?losses in the header blan3 There!ore in a T, !ield usually is )ut
T, – Ct HAtg –ArgHCrl here
T,5T, )oer-d>m1
Ct5cable loss beteen transmitter and antenna -d>1
Atg5transmitting antenna gain -d>i
Arg5recei(ing antenna gain -d>i
Crl5cable loss beteen recei(er and recei(ing antenna -d>
December 2004
C Data #alidationC Data #alidation • Com)are the site data -)hotogra)hs1 surrounding
clutter and terrain )ro!ile to the Clutter and DTM layer o! the ma) data )ro(ided3
• Chec the dri(en routes against (ectors ithin the ma) data3
• Bilter out any in(alid data that may cause anomalies in the calibration )rocess
• Mae sure that details relating to a site -&IR"1 8ocation1 6eight1 Antenna !ile corres)ond to re)orts !rom C 'ur(ey3
Data !ilteringData !iltering
• @ilter cl*tter types t8at 8ae less t8an ;00 bins> Cl*tter osets or t8em !ill be estimated later in t8e model t*nin) process>
• @ilter o*t any ile !8ic8 s8o!s extreme in si)nal leel>
• $n*s*ally 8i)8 si)nal leel at ar distance can be ca*sed by relection oer bi) !ater s*race& or driin) alon) ro*te !8ic8 is 8i)8er t8an antenna>
• $n*s*ally !ea9 si)nal leel can be ca*sed by driin) be8ind bloc9in) ob?ect>
• 9*m*ra #:ata canBt model aboe sit*ations& t8ereore t8ese data m*st be iltered o*t>
• =it8 care*l ro*te plannin) ilterin) can be aoided>
Data Ty)es5C Measurements5 C 'ignal
To set u) thresholds double clic on C 'ignal and s)eci!y thresholds under Categories tab
The same goes !or other o)tions inside C Measurements
n i n )
Oumura56ataOumura56ata
• 9*m*ra:ata is a !orld!ide t8e most pop*lar model in mobile telecomm*nication
• It is semiempirical model>
• (ased on 9*m*ra meas*rements in %o9yo in .+'/ mat8ematical model !as p*blis8ed in .+/0 by :ata>
• <imitations
Oumura56ata in AssetOumura56ata in Asset
• Asset *ses sli)8tly modiied 9*m*ra:ata "loss ;/ H ;2log-d H ;6ms H ;4log-6ms H ;:log-6e!! H
;Jlog-6e!!log-d H ;K8di!! H 8clutter
d is distance in m beteen T, antenna and mobile station
6ms is mobile station height
6e!! is e!!ecti(e antenna height in metres
8di!! is a loss due to di!!raction
8clutter is a clutter loss
• Asset 8as 4 al)orit8ms or calc*latin) eectie antenna 8ei)8t
• Asset 8as 4 al)orit8ms or calc*latin) diraction
Asset im)ro(ementsAsset im)ro(ements
• 7. near and 92 near are desi)ned to oercome 9*m*ra:ata limitation or close distances>
• %8ro*)8 Cl*tter <oss # ta9es into t8e acco*nt cl*tter proile alon) distance d rom mobile station to base station>
Through Clutter Model De!initionThrough Clutter Model De!inition
• Eac8 cl*tter cate)ory is )ien %8ro*)8 Cl*tter <oss d(-9m" on t8e pat8 bet!een transmitter and receier>
O(er(ie o! Model CalibrationO(er(ie o! Model Calibration
• %8ere m*st be pro?ect set *p map data& antennas& sites& propa)ation model" in order to start t*nin)
• <oad C= data
• Ma9e appropriate ilterin)& *s*ally 5//0d>m to 540d>m
/2:m to /0000
• Start !it8 t8e dea*lt al*es or 9 parameters
• Do A*to %*ne
• %ry all combination o eectie antenna 8ei)8t and diraction al)orit8ms and determine !8ic8 one )ies t8e lo!est standard deiation
• %a9e note o second and t8ird best
>
C indoC indo
• 3)-Asset%oolsModel %*nin)
• Select t8e resol*tion o mappin) data
• Set *p distance ilterin)
• Set *p si)nal leel ilterin)
• @ilter o*t cl*tter types !it8 ins*icient data by 8i)8li)8tin) t8em
• I yo* t*ne 9 clic9 ?*st 6<S
• Clic9 antenna b*tton i directional antennas !ere *sed
• Set *p deltas
• Clic9 ix box next to t8e 9 actor yo* donBt !ant to t*ne
• Clic9 A*to %*ne *nder %ools tab
• =ait or res*lts
• Fo* can apply ne! parameters by clic9in) apply ne! parameters
December 2004
; )arameters; )arameters
• 73 and 74 are not altered> %8is is beca*se t8ey relate to mobile 8ei)8t !8ic8 in a typical cell*lar system is constant ma9in) t8ese coeicients red*ndant>
• 7 is t8e diraction parameter> It can be determined by t*nin) ?*st 6<S data>
• All 7 parameters m*st 9eep t8e same polarity as in t8e ori)inal 9*m*ra :ata model
;/1 ;21 ;K .0
;1 ;:1 ;J L0
/12 near calibration/12 near calibration
• I model is not )ood close to t8e site& or example *p to 00m& a*to t*ne t8e model rom 00m to .09> Apply o*nd 9 parameters>
• %*ne model a)ain !it8 9;&9' and 9 loc9ed and ilter o*t distances aboe 00m>
• 1es*lt !ill be 9.near and 92 near>
Clutter o!!setClutter o!!set
• Some t8ro*)8 cl*tter osets and cl*tter osets need to be estimated d*e to ins*icient data>
• Estimation is done relatie to t8e cl*tter osets !it8 s*icient data>
• Cl*tter osets m*st be realistic relatie to eac8 ot8er>
Ad9usting M&Ad9usting M&
• Mean error is *s*ally altered ater estimation o cl*tter osets>
• ME can be easily brin) bac9 to 0 by c8an)in) 9.
• I mean error is c8an)e 9. to 9.H
Model analysesModel analyses
• Ma9e statistical analyses or ME and SD or dierent distance ran)es>
• In t8e ran)e o interest& typically .9m to 49m& ollo!in) re5*irements s8o*ld be *lilled
. ME .
SD /
• Area considered densely pop*lated coastal cities>
• $sed re5*ency +3;>2M:,
• %otal o .0 sites !ere incl*ded in t*nin) process !it8 /02'0 points>
• Si)nal stren)8t t8res8old set to #40 to ..0 d(m>
• Distance *sed or t*nin) rom .2;m to .09m>
December 2004
December 2004
'tatistical >readon !or Coastal @rban /:m'tatistical >readon !or Coastal @rban /:m
No. of Bins
2"#$"# 2+;+/ 0>+ '>'
$"#%"# .+. 0>4 ;>4
December 2004
Apoview site
1"#2"# 2324 .>; '>3
2"#$"# 43/3 0>4 ;>+
#alidation o! Tuned Model5'ite 2#alidation o! Tuned Model5'ite 2
Banawa site
2"#$"# 322/ . '>4
l i b r a t i o n
E x p e r i e
n c e
December 2004
C and Model Tuning Re!erencesC and Model Tuning Re!erences • 3GIS S!eden" # $M%S& ' models
• (el)acom (el)i*m" # GSM +00& ' models
• S!isscom S!it,erland" # GSM +00-./00 and $M%S& + models
• Inentis S!it,erland" # GSM 1& 3 models
• odaone Malta" # GSM +00& 2 models
• Glob*l (*l)aria" # GSM +00& GSM./00
• ni!ay Port*)al" # $M%S& 4 models
• In5*am Port*)al" # CDMA2000
• (l* Italy" # GSM ./00
• 6ortel $7" # GSM ./00
• Ericsson $7" # GSM ./00
• 7P6 (ase (el)i*m" # GSM +00-./00& / Models& 4 or eac8
•%M6 Port*)al" # GSM+00& . model
•Mascom (ots!ana" # GSM +00
• A=S $SA" # GSM .+00& 2 models
•%CI Iran" # GSM +00& ; models
•ESA% Di)ione Ireland" # $M%S 3 models
•Saaricom 7enya" GSM +00 # 2 models
•<*cent 1iyad8" GSM +00 # . model
• Claro (ra,il" GSM./00 # 3 models
•Globe P8illipines" GSM+00 # 3 models
s
Data #alidation
Aim o! Model CalibrationAim o! Model Calibration
• C8aracterise t8e topolo)y !it8 net!or9 limits # identiication o operatin) ran)e or eac8 model>
• Minimise Standard Deiation Error>
• Proide ,ero mean error
• Determine model parameters in accordance to realistic propa)ation eects existin) !it8in proposed re)ions>
• Ma9e s*re calibrated model corresponds !ell !it8 t8e collected data # data is essential>
• Proide cost eicient 6ominal Plan
'ite 'election'ite 'election
More or .0 sites per model> <ess n*mber o sites can be considered i modelled )eo)rap8ical area is airly small>
=it8in )eo)rap8ic re)ion o model
Spread o site 8ei)8ts representatie o net!or9 sites 8ei)8ts !it8in modelled re)ion
Allo! meas*rements in all cl*tter types
1ootop sites are preerred in a case test transmitter 8as to be mo*nted
Ease o access
6o bloc9in) ob?ects in close icinity
• Distance
• Clutter
• Roads
Mi, o! radial and tangential roads
• Miscellaneous
Do not )lan a ma) along the roads ith ground height abo(e the transmitter antenna3 Oumura5 6ata model can7t model this3
Good balance beteen measurements taen in 8O' and $8O' situations
Do not )lan a route across a big ater sur!ace1 i! site is on the one side o! the lae1 do not dri(e other lae side
Data in regions o! terrain slo)e (ariation
A(oid large blocing ob9ects as high building or long roo!
8ong enough to ensure su!!icient data is ca)tured
Chec ma) data (alidity
In #ehicle1 Recei(e
mounted antenna
1@ Si)nals
• ')ectrum clearance
During C sur(ey allocated test !re+uency shouldn7t be use !or other )ur)oses
/05/:;6< bandidth monitoring
• &+ui)ment con!iguration
Accurate Radiated "oer setting1 &iR" should be greater than 40d>m
Ra?A(eraged data
@se Omni antenna ith minimum (ertical beamidth o! /2 degrees
Directional antenna can be used but in )ost)roccessing e(erything beyond d>m should be dismissed
• Dri(ing
Distance?Time triggering
'am)ling 5 8ee Criteria'am)ling 5 8ee Criteria
• <ee Criteria # In order to eliminate ast adin) rom meas*rements& minim*m 3' samples s8o*ld be ta9en oer 40> A local mean s8o*ld be o*nd or t8e c8osen n*mber o samples>
• Common practice is to ta9e ;0 samples !8ic8 )ies one sample eery 0>/>
'lo !ading (s Bast !ading'lo !ading (s Bast !ading
• @ast adin) is adin) d*e to m*ltipat8 eect>
• @ast adin) is c8aracteri,ed by 1aylei)8 probability distrib*tion t8ereore canBt be modelled by lo) normal distrib*tion>
• @ast adin) is s*perimposed onto si)nal enelope slo! adin)" !8ic8 !e try to model>
• Slo! adin) is adin) d*e to terrain and cl*tter>
• Slo! adin) ollo!s lo) normal distrib*tion>
• 9*m*ra:ata is lo) normal distrib*tion
8 8
Distance triggering (s time triggeringDistance triggering (s time triggering
• Distance tri))erin) allo!s *s to easily apply <ee criterion>
• %ime tri))erin) is ery diic*lt to ollo! <ee criterion d*e to c8an)e in drie e8icle speed>
• Samplin) in time tri))erin) is not a problem since <ee states ?*st minim*m n*mber o samples>
• Aera)in) oer 40 is problem to implement in time tri))erin) since t8ere is not constant n*mber o samples oer 40 ca*sed by speed ariation>
• =8eneer possible c8oose distance tri))erin)>
Total dri(ing route )er modelTotal dri(ing route )er model
• In order or model to be realistic& statistically s*icient n*mber o data need to be collected>
• Aircom practise is to 8ae at least 30000 data>
• 30000 data )ies total drien distance o
0000,40/EFm or
20m )er site !or /F00M6< range3
• I t8is distance is not ac8ieable d*e to limitation in driable roads it is recommended to 8ae more t8an .0 sites per model>
• As stated beore& in a case o modellin) small )eo)rap8ical area !it8 3 sites& t*nin) can be perormed !it8 .0000 data or 229m per site>
• %8e more data t8e model is more realistic
Data "ost )rocessingData "ost )rocessing • De)ends on customer re+uirements
A(eraged Measurements – )ost )rocessing in(ol(es sim)le con(ersion into 'ignia !ormat su))orted by &nter)rise
'ignia data !ile - 3dat contains longitude1 latitude -decimal degrees and recei(ed le(el -d>m
&(ery data !ile must ha(e header !ile ith identical name but ith e,tension 3hd3
6eader !ile must ha(e antenna ty)e -identical name to one in Assetg1 T, )oer1 T, antenna height1 coordinates3
It is common )ractice to include all gains and losses under T, )oer (alue and lea(e other !ields rele(ant to gain?losses in the header blan3 There!ore in a T, !ield usually is )ut
T, – Ct HAtg –ArgHCrl here
T,5T, )oer-d>m1
Ct5cable loss beteen transmitter and antenna -d>1
Atg5transmitting antenna gain -d>i
Arg5recei(ing antenna gain -d>i
Crl5cable loss beteen recei(er and recei(ing antenna -d>
December 2004
C Data #alidationC Data #alidation • Com)are the site data -)hotogra)hs1 surrounding
clutter and terrain )ro!ile to the Clutter and DTM layer o! the ma) data )ro(ided3
• Chec the dri(en routes against (ectors ithin the ma) data3
• Bilter out any in(alid data that may cause anomalies in the calibration )rocess
• Mae sure that details relating to a site -&IR"1 8ocation1 6eight1 Antenna !ile corres)ond to re)orts !rom C 'ur(ey3
Data !ilteringData !iltering
• @ilter cl*tter types t8at 8ae less t8an ;00 bins> Cl*tter osets or t8em !ill be estimated later in t8e model t*nin) process>
• @ilter o*t any ile !8ic8 s8o!s extreme in si)nal leel>
• $n*s*ally 8i)8 si)nal leel at ar distance can be ca*sed by relection oer bi) !ater s*race& or driin) alon) ro*te !8ic8 is 8i)8er t8an antenna>
• $n*s*ally !ea9 si)nal leel can be ca*sed by driin) be8ind bloc9in) ob?ect>
• 9*m*ra #:ata canBt model aboe sit*ations& t8ereore t8ese data m*st be iltered o*t>
• =it8 care*l ro*te plannin) ilterin) can be aoided>
Data Ty)es5C Measurements5 C 'ignal
To set u) thresholds double clic on C 'ignal and s)eci!y thresholds under Categories tab
The same goes !or other o)tions inside C Measurements
n i n )
Oumura56ataOumura56ata
• 9*m*ra:ata is a !orld!ide t8e most pop*lar model in mobile telecomm*nication
• It is semiempirical model>
• (ased on 9*m*ra meas*rements in %o9yo in .+'/ mat8ematical model !as p*blis8ed in .+/0 by :ata>
• <imitations
Oumura56ata in AssetOumura56ata in Asset
• Asset *ses sli)8tly modiied 9*m*ra:ata "loss ;/ H ;2log-d H ;6ms H ;4log-6ms H ;:log-6e!! H
;Jlog-6e!!log-d H ;K8di!! H 8clutter
d is distance in m beteen T, antenna and mobile station
6ms is mobile station height
6e!! is e!!ecti(e antenna height in metres
8di!! is a loss due to di!!raction
8clutter is a clutter loss
• Asset 8as 4 al)orit8ms or calc*latin) eectie antenna 8ei)8t
• Asset 8as 4 al)orit8ms or calc*latin) diraction
Asset im)ro(ementsAsset im)ro(ements
• 7. near and 92 near are desi)ned to oercome 9*m*ra:ata limitation or close distances>
• %8ro*)8 Cl*tter <oss # ta9es into t8e acco*nt cl*tter proile alon) distance d rom mobile station to base station>
Through Clutter Model De!initionThrough Clutter Model De!inition
• Eac8 cl*tter cate)ory is )ien %8ro*)8 Cl*tter <oss d(-9m" on t8e pat8 bet!een transmitter and receier>
O(er(ie o! Model CalibrationO(er(ie o! Model Calibration
• %8ere m*st be pro?ect set *p map data& antennas& sites& propa)ation model" in order to start t*nin)
• <oad C= data
• Ma9e appropriate ilterin)& *s*ally 5//0d>m to 540d>m
/2:m to /0000
• Start !it8 t8e dea*lt al*es or 9 parameters
• Do A*to %*ne
• %ry all combination o eectie antenna 8ei)8t and diraction al)orit8ms and determine !8ic8 one )ies t8e lo!est standard deiation
• %a9e note o second and t8ird best
>
C indoC indo
• 3)-Asset%oolsModel %*nin)
• Select t8e resol*tion o mappin) data
• Set *p distance ilterin)
• Set *p si)nal leel ilterin)
• @ilter o*t cl*tter types !it8 ins*icient data by 8i)8li)8tin) t8em
• I yo* t*ne 9 clic9 ?*st 6<S
• Clic9 antenna b*tton i directional antennas !ere *sed
• Set *p deltas
• Clic9 ix box next to t8e 9 actor yo* donBt !ant to t*ne
• Clic9 A*to %*ne *nder %ools tab
• =ait or res*lts
• Fo* can apply ne! parameters by clic9in) apply ne! parameters
December 2004
; )arameters; )arameters
• 73 and 74 are not altered> %8is is beca*se t8ey relate to mobile 8ei)8t !8ic8 in a typical cell*lar system is constant ma9in) t8ese coeicients red*ndant>
• 7 is t8e diraction parameter> It can be determined by t*nin) ?*st 6<S data>
• All 7 parameters m*st 9eep t8e same polarity as in t8e ori)inal 9*m*ra :ata model
;/1 ;21 ;K .0
;1 ;:1 ;J L0
/12 near calibration/12 near calibration
• I model is not )ood close to t8e site& or example *p to 00m& a*to t*ne t8e model rom 00m to .09> Apply o*nd 9 parameters>
• %*ne model a)ain !it8 9;&9' and 9 loc9ed and ilter o*t distances aboe 00m>
• 1es*lt !ill be 9.near and 92 near>
Clutter o!!setClutter o!!set
• Some t8ro*)8 cl*tter osets and cl*tter osets need to be estimated d*e to ins*icient data>
• Estimation is done relatie to t8e cl*tter osets !it8 s*icient data>
• Cl*tter osets m*st be realistic relatie to eac8 ot8er>
Ad9usting M&Ad9usting M&
• Mean error is *s*ally altered ater estimation o cl*tter osets>
• ME can be easily brin) bac9 to 0 by c8an)in) 9.
• I mean error is c8an)e 9. to 9.H
Model analysesModel analyses
• Ma9e statistical analyses or ME and SD or dierent distance ran)es>
• In t8e ran)e o interest& typically .9m to 49m& ollo!in) re5*irements s8o*ld be *lilled
. ME .
SD /
• Area considered densely pop*lated coastal cities>
• $sed re5*ency +3;>2M:,
• %otal o .0 sites !ere incl*ded in t*nin) process !it8 /02'0 points>
• Si)nal stren)8t t8res8old set to #40 to ..0 d(m>
• Distance *sed or t*nin) rom .2;m to .09m>
December 2004
December 2004
'tatistical >readon !or Coastal @rban /:m'tatistical >readon !or Coastal @rban /:m
No. of Bins
2"#$"# 2+;+/ 0>+ '>'
$"#%"# .+. 0>4 ;>4
December 2004
Apoview site
1"#2"# 2324 .>; '>3
2"#$"# 43/3 0>4 ;>+
#alidation o! Tuned Model5'ite 2#alidation o! Tuned Model5'ite 2
Banawa site
2"#$"# 322/ . '>4
