Sudarshan_ RF Optim

8/8/2019 Sudarshan_ RF Optim

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www.gtllimited.com

GTL Limited

Network EngineeringNetwork Engineering

Training on RF Optimisation

GSMJune 2005June 2005

Presented by : Sudarshan IyengarPresented by : Sudarshan Iyengar


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AgendaAgenda

A. Understanding RF Network Cycle

B. Basics of RF Design

C. Why do we need optimization

D. !ptimization "tages

#. $hysical and %ardware !ptimization

F. Data&ase parameter optimization' "pecial (ools


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…… Spreadsheet Design ...Spreadsheet Design ...

sua!!y done during Initia! "et#or$ %ui!d

• &in$ budget to 'a!'u!ate the number o( sites)

• *a!'u!ations based on

– subs'riber density+

– tra((i' per subs'riber+

– e,pe'ted gro#th in tra((i'+ et')


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…… CW Drive Test/ Model Tuning...CW Drive Test/ Model Tuning...

Purpose

•

Mode! Tuning is used to– -''urate!y a!!o'ate the sites)

– To a'hie.e more a''urate resu!ts (rom the

predi'tion/simu!ation too! dep!oyed)

–

Identi(i'ation o( hotspots/spe'ia! 'o.erage reuirementareas)

– Tuned mode! 'an be used as a ben'hmar$ (or (uture

e,pansions)


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Mode! Tuning Pro'ess

•

Setup 'onsists o( Test transmitter (or the parti'u!ar band1GSM 00/3400 6 usua!!y 207

• -ntenna 6 Omni/Pane!+ 'ab!es+ a''essories)

• One 'andidate 'hosen to represent ea'h type o( '!utter

area in the net#or$)

• The '!utter types 'ou!d be urban+ suburban+ rura!+ et')

• The test transmitter is setup on a suitab!e roo(top)

• Test (reuen'y 'hosen and transmitted

• 8ri.e test is 'arried out using re'ei.er or T9MS euipment

set to s'an mode)



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Mode! Tuning Pro'ess

•

8ata 'o!!e'ted 6 R,!e. samp!es aggregated o.er 0;50 mbins)

• The R,!e. measurements are pro'essed and input to the

predi'tion too!)

• *!utter o((set and other parameters are 'orre'ted)

• *orre'tions are made to a'hie.e 6 !o#est possib!e Standard8e.iation .a!ues)

Thus #e ha.e a


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…… RF PlanningRF Planning

• The inputs re'ei.ed (rom spreadsheet design and mode!

tuning sur.eys+ is used to prepare a


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…… RF PlanningRF Planning

• The output o( the >&8 is


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…… RF Site Survey/Drive TestingRF Site Survey/Drive Testing

• sing the inputs pro.ided by the nomina! 'e!! p!an+ the RF

team per(orms

– Sur.eys (or ea'h sear'h ring in the net#or$ to identi(y the

suitab!e 'andidates #hi'h 'an be used (or bui!ding the sites)

– *andidates identi(ied are ran$ed on basis o( their RF

suitabi!ity and other parameters su'h as stru'tura! stabi!ity+!ine o( sight '!earan'e1(or T,+ a''essibi!ity+ 'osts+ et')

– 8ri.e testing may be 'arried out in some 'ases+ to assess

the RF suitabi!ity

– On'e suitab!e 'andidate1s is identi(ied))a'uisition begins@@@


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…… RF Planning – The REAL Challenge!!!RF Planning – The REAL Challenge!!!

• -'uisition o( idea! 'andidate poses a rea! 'ha!!enge to the

net#or$ design pro'ess)

• More o(ten than not 'andidates #hi'h are !o#er on priority interms o( RF suitabi!ity are the ones #hi'h get a'uired@@

• O(ten due to a'uisition 'onstraints+ sear'h rings need to bemodi(ied and sometimes e.en the nomina! p!an needs to be'hanged)

•

Thus as an end resu!t the net#or$ bui!t is de.iated (rom the one#hi'h #as origina!!y designed in the nomina! p!an)A@A@@@@B@


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Frequency PlanningFrequency Planning

• GSM #or$s on a (reuen'y reuse pattern)

• -s the sites get a'uired and the bui!d pro'ess starts+ the RFp!anners prepare a C(reuen'y p!anD (or the net#or$)

•

8i((erent te'hniues a.ai!ab!e (or (reuen'y p!an 6 a Fi,edP!an+ b >opping P!an 6 (urther di.ided into %aseband >oppingand SynthesiEed Freuen'y >opping

• RF P!anners either manua!!y or by the use o( an -FP1-utomati'

Freuen'y P!anner 'reate a (reuen'y p!an (or the net#or$)


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• -n optima! (reuen'y is 'riti'a! to ensure good RF

per(orman'e o( the net#or$)

• Spe'tra! 'ha!!enges

• &imited band a!!o'ation

• Fast gro#th rate o( subs'ribers/ tra((i' gro#th

• Tighter reuse patterns


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RF Optimization/Parametric OptimizationRF Optimization/Parametric Optimization

• 8uring the net#or$ bui!d initia! RF optimiEation is done+

to ensure that the sites bui!t are reasonab!y meetingtheir ob?e'ti.es)

• 8uring the net#or$ bui!d phase it is a!so ensured that

optima! parameter settings are done (or a!! sites toensure good per(orman'e)

• 8etai!ed e,p!anation o( the abo.e to (o!!o#@@


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Traffic Planning/Expansion PlanningTraffic Planning/Expansion Planning

• T#o stages (or *apa'ity P!anning I Initia! "et#or$ %ui!d II

Future 9,pansion)

3 Initia! *apa'ity P!an

• Spreadsheet design is used)

• The e,pe'ted tra((i' is 'a!'u!ated based on a 'ertain

amount o( tra((i' assigned per subs'riber 6 say 25 m9)• The tota! tra((i' reuirement is tra((i' per subs'riber

tota! no o( subs'ribers)

• "et#or$ 'apa'ity is based on a 'ertain GOS 6 say 2 )

•

9r!ang % tab!e used to 'a!'u!ate the no) o( TR+ hen'e noo( sites)


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Traffic Planning/Expansion PlanningTraffic Planning/Expansion Planning

• T#o stages (or *apa'ity P!anning I Initia! "et#or$ %ui!d

II Future 9,pansion)2 Future 9,pansion

• This 'an a!so be done using spreadsheet design

methodo!ogy+ using a (igure o( e,pe'ted tra((i' gro#th)

• -!ternati.e!y TR additions are done on an ad;ho' basisby studying the tra((i' trend on a #ee$!y/month!y basis)

• In 'ases #here no (urther TR addition is pra'ti'ab!e+

'apa'ity sites are added in the e,isting net#or$)

• Separate p!anning is done (or Tra((i' *hanne!s1T*> and

-''ess *hanne!s 1S8**>)


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Inbuilding SolutionsInbuilding Solutions

• I%S is reuired in p!a'es #here indoor 'o.eragereuirement is 'riti'a! and the possibi!ity o( pro.iding'o.erage (rom outdoor sites is not pra'ti'ab!e)

• sua!!y imp!emented (or p!a'es !i$e 'orporate o((i'es+hote!s+ hospita!s+ shopping 'omp!e,es+ et')+ #here both'o.erage and 'apa'ity is essentia!)

• I%S imp!ementations may 'onsist o(•Repeaters 6 &o# 'ost so!ution (or 'o.ering a sma!! area

#ith !ess tra((i'

•Mi'ro'e!!s/Ma'ro'e!!s 6 Separate %TS sites #hi'h 'an be

a sing!e 'arrier Cmi'ro'e!!D or a mu!ti 'arrier Cma'ro'e!!D+

imp!emented in p!a'es #here !arger area needs to be'o.ered and has higher tra((i' reuirement)


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Inbuilding SolutionsInbuilding Solutions

• I%S imp!ementations usa!!y dep!oy a


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BenchmarkingBenchmarking

• %en'hmar$ing is done (or ha.ing a 'omparison o( o#nnet#or$ #ith 'ompetitorDs net#or$ in terms o(

'o.erage/.oi'e ua!ity)

• %en'hmar$ing is a!so done (or 'omparing o#n net#or$Dsper(orman'e against 'ertain set HPIs or pre.ious!ya'hie.ed per(orman'e targets)

• Spe'ia! too!s !i$e .oi'e euipment is a.ai!ab!e (or .oi'eua!ity ben'hmar$ing)

• For 'o.erage/ua!ity ben'hmar$ing 'ou!d be done usingregu!ar dri.e test and post pro'essing too!s !i$e T9MS and89SH*-T

• "et#or$ Operator/O9M .endor usua!!y sub'ontra'ts thisa'ti.ity to a rd party+ in order to deri.e unbiased resu!ts(rom the e,er'ise


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• Statisti'a! data (rom ben'hmar$ing 'an be used as a.a!uab!e input to the net#or$ optimiEation pro'ess)

• The data is used to identi(y #ea$ areas in the net#or$+#hi'h he!ps in de.e!oping strategies (or impro.ing thenet#or$ per(orman'e)

BenchmarkingBenchmarking


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Mobi!e *ommuni'ations propagation is impa'ted by :

Path &oss

Re(!e'tion

8i((ra'tion

Mobile Communications PropagationMobile Communications Propagation

PathLossPathLoss


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The basi' path !oss is the transmission !oss in (ree spa'e)

Lfsl = 32.4 + 20 log d(in Kms)+ 20logf(in Mhz)

At 900 Mhz, at a distance of !m , Loss = 9." d#

Act$al %&ediction of loss cannot #e done on this, since in a mo#ileen'i&onment the mo#ile ill &ecei'e signals f&om se'e&al &eflections.he a#o'e fo&m$la is onl* 'alid $nde& di&ect Land no &eflection conditions.

d

Path LossPath Loss

ReflectionReflection


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-eflection occ$&s hen a %&o%agating elect&omagnetic a'e im%inges $%on as$&face hich has 'e&* la&ge dimensions as com%a&ed to the a'elength of the

%&o%agating a'e. -eflections occ$& f&om s$&face of ea&th, #$ildings,alls and ate&.

he a'e is %a&tiall* a#soed and %a&tiall* &eflected.

Amo$nt of a#so&%tion ill de%end on the &eflection coefficient of the&eflecting s$&face.

-eflection coefficient is f$nction of the mate&ial %&o%e&ties and de%ends ona'e %ola&ization , angle of incidence and the f&e$enc*.




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Path !oss (or 2 ;ray Mode! 1 o.er (!at 'ondu'ti.e sur(a'e

ht hr

d

L2&a* = 40 log d / ( 20 log ht + 20 log h& )

Anal*tical fo&m$la, onl* 'alid fo& la&ge& distances ( 0 Km)

Loss inc&eases at la&ge& distance at a &ate of 40d# 1dec.-t 00 MhE+ 30+000m distan'e + ht 300m+ hr 3)5m&(s 333)5 db #hereas &2ray 33K)5 db

his indicates that in 2 &a* %ath , additional loss of " d#.




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Re(!e'tion in a'tua! mobi!e en.ironment + #ou!d be (rom mu!tip!e paths) So+ re(!e'tion in mobi!e 'ommuni'ations is Mu!tipath re(!e'tion) RS& #i!! be resu!tant o( !e.e!s 'oming (rom a!! paths)



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8i((ra'tion a!!o#s radio signa!s to propagate around the 'ur.edsur(a'e o( earth and behind obstru'tion)

ht hr Shadow region

RS& drops as the re'ei.er mo.es deep into the shado# region

Huygen's principle on phenomenon of diffraction

All %oints on a a'e/f&ont can #e conside&ed as %oint so$&ces fo& the %&od$ction ofseconda&* a'elets, and that these a'elets com#ine to %&od$ce a ne a'e/f&ont inthe di&ection of %&o%agation.

8i((ra'tion is 'aused by the propagation o( se'ondary #a.e!etsinto the shado#ed region

DiffractionDiffraction


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>i!!s+ Mountains+ %ui!dings #i!! 'ause $ni(e edge di((ra'tion In a Mobi!e en.ironment most o( the di((ra'tion is $ni(e edge)

Diffraction is of two types in general

Smooth Sphere 8i((ra'tion Hni(e 9dge 8i((ra'tion

Smooth Sphere 8i((ra'tion

8i((ra'tion ta$es p!a'e through a!most a (!at sur(a'e)

Hni(e 9dge 8i((ra'tion

DiffractionDiffraction

CalculationofDiffractionLossCalculationofDiffractionLoss


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Fresne! Eone geometry

-rea around the &OS #ithin #hi'h a di((ra'tion 'an resu!t into

antiphase1340 deg 'ondition is the (irst (resne! Eone)

I( an ob?e't is #ithin the (resne! Eone or 'omp!ete!y b!o'$s the Eone+ then a!so energy#i!! arri.e at the re'ei.er but #i!! di((ra'tion !oss)

In Mobi!e en.ironment+ #e are not #orried about '!earan'e+ but on!y #ith the !oss)

ht hr

Calculation of Diffraction LossCalculation of Diffraction Loss



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h

d1 d2

&esnel diff&action %a&amete& (')

Indi'ates the position o( the ob?e't #ith re(eren'e to the (resne!

Eones 1 0 means + ob?e't tip on &OS+ 3 means tip on 3st (resne!Eone on upper side)

2 ( d1 + d2 )

d1.d2hv =

&om ' , e can com%$te the diff&action loss.

( all values in "m" )




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Re!ation o( L.L #ith di((ra'tion !oss 1 graphi'a! )

K n i f e e d g e d i f f r a c t i o n g a i n ( G

a d B )

Fresnel diffraction parameter v

-3 -2 -1 0 1 2 3 !-30

-2!

-20

-1!

-10

-!

0

!



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-adio a'e hen im%inges on a &o$gh s$&face , &eflected ene&g*is s%&ead o$t in all di&ections d$e to scatte&ing.

his is the &eason act$al -L in a mo#ile en'i&onment is oftenst&onge& then hat is %&edicted #* &eflection and diff&action.

#ects s$ch as Lam% %osts and t&ees tend to scatte& ene&g* in

all di&ections, the&e#* %&o'iding additional &adio ene&g* at the&ecei'e&.

ScatteringScattering


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o&m$la5s desc&i#ed ea&lie& a&e #ased on sim%le models of the &adio %ath.

o&m$la5s don5t ta!e ca&e of the t*%e of the te&&ain of the &adio %ath.

-ealistic method of %&ediction o$ld #e to $se em%i&ical data of &adioa'e %&o%agation o'e& 'a&io$s t*%es of te&&ain and land $sage.

6m%i&ical data of this t*%e e&e collected #* !$m$&a f&om hiscom%&ehensi'e &adio a'e %&o%agation meas$&ements

Path Loss PredictionPath Loss Prediction

OkumuraModelOkumuraModel


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!$m$&a de'elo%ed a set of c$&'es gi'ing the atten$ation in e7cess toL in an $an a&ea ith #ase station effecti'e height of 200m and and

mo#ile antenna height of 3m.

hese c$&'es gi'e the loss as a f$nction of f&e$enc* and distance f&om#ase station)

Okumura ModelOkumura Model



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Ok M dlOkumuraModel


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Path !oss (or di((erent heights 'an be 'a!'u!ated by these 'ur.es byusing the (ormu!a)

8ath Loss = Lfsl + A(f,d) / (hte) / (h&e)

G1hte and G1hre are the e((e'ti.e %ase Station and MS antenna heights

"#hte) $ 20 log 1000m % hte % 10mhte2

( )

"#hre) $ 10 log hre & 3mhr e !

( )

hr e

!

( )"#hre) $ 20 log hre & 3m




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:hat is 6ffecti'e Antenna ;eight <

3 m 1! m

hmsl

(erage ground leel #hag)

hte

hte $ (ntenna height a*oe msl#hmsl) - aerage ground leel #hag)

( average ground level is calculated ithin ! # 1$%m )


OkumuraModelOkumura Model


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O$umura 'ur.es are on!y app!i'ab!e (or urban areas)

For other terrains+ O$umura has pro.ided 'orre'tion (a'tors) The 'orre'tion (a'tors are pro.ided (or types o( terrain in the

(orm o( 'ur.es re!ated to (reuen'y)

Open -rea : 'orresponds to a rura! + desert $ind o( terrain

uasi Open -rea : 'orresponds to rura! + 'ountryside $ind o( terrain

Suburban

Okumura Model

OkumuraModelOkumura Model


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8ath Loss(o,,s) = Lfsl + A(f,d) / (hte) / (h&e) / (a&ea)

8ath loss fo& othe& te&&ain5s

Okumura Model



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+onclusion

im%lest, #est and acc$&ate %&ediction model #$t onl* fo& s%ecificte&&ain5s.

lo &es%onse to &a%id changes in te&&ain.

Model is fai&l* good fo& $an and s$#$an a&eas, #$t not as good in&$&al a&eas.

tanda&d de'iations #eteen %&edicted and meas$&ed loss 'al$es 0d to 4 d.

Model is mo&e g&a%hical than mathematical, fo& com%$tation e needfo&m$la5s not g&a%hs.

HataModelHataModel


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;ata model is an em%i&ical fo&m$lation of the g&a%hical %ath loss data%&o'ided #* !$m$&a.

;ata %&esented the $an a&ea %&o%agation loss as a standa&d fo&m$laand s$%%lied co&&ection e$ations fo& a%%lications fo& othe& sit$ations.

o&m$la5s a&e designed fo& com%$te& $sage, #$t the* a&e onl* &o$gh

a%%&o7imations of !$m$&a5s c$&'es.

ince e&&ain t*%es %&ofiles a&e %&acticall* infinite, modeling of thetool $sed fo& %&ediction #eca$se essential #$t ta!ing se'e&almeas$&ements and se'e&al times.

Hata ModelHata Model



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rban terrain

L($an) = >9."" + 2>.> log fc / 3.?2 log hte / a(h&e) + ( 44.9 / >."" log hte ) log d

fc = fre&uenc' in * ( 1$ # 1 *)

hte = B, antenna height ranging !m to 2m

hre = effective receiver antenna height ranging 1m to 1m

d = ransmitter receiver separation distance (1 # 2 %m )

a(hre ) = correction factor for effective mo-ile antenna height hich is a function of si*e of the coverage area in d-

Small to medium city

a(hre) = (1.1 log fc # . ) hre # ( 1.$/ log fc # .0 ) d-

For large city

a(hre) = !.2 ( log 11.$ hre ) # .2



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*orre'tion (or Suburban N Rura! terrains

&oss (or Rura! Open -rea

&oss (or S%R%-"

3(su-) = 3 (ur-an) # 2 4 log (fc520) 6 # $.2

3(ro) = 3 (ur-an) # .0 ( log fc) # 10.!!log fc # .2

&oss (or Rura! uasi;Open -rea

3(r&o) = 3 (ur-an) # .0 ( log fc) # 10.!!log fc # !$.2

COST231 HataModelCOST231-HataModel


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L($) = 4>.3 + 33.9 log fc / 3.?2 log hte / a(h&e) + ( 44.9 / >."" log hte ) log d +@m

fc = f&e$enc* in M;z ( "00 / 2000 M;z) hte = antenna height &anging 30m to 200m

h&e = effecti'e &ecei'e& antenna height &anging m to 0m d = &ansmitte& &ecei'e& se%a&ation distance ( / 20 !m )a(h&e ) = co&&ection facto& fo& effecti'e mo#ile antenna height hich

is a f$nction of size of the co'e&age a&ea in d#@m = @o&&ection facto& fo& cit* size

a(h&e) = (. log fc / 0. ) h&e / ( ."> log fc / 0.? ) d#@m = 0 d# fo& medi$m cit* and s$#$an cente&s ith mode&ate t&ee densit@m = 3 d# fo& met&o%olitan cente&s.

For rura! areas + the ear!ier (ormu!as #i!! app!y

@ /23o&!ing committee de'elo%ed an e7tended 'e&sion of;AA model fo& f&e$encies $% to 2 ;z.

COST 231 - Hata ModelCOST 231 - Hata Model

Path Loss Predictions for GSMPath Loss Predictions for GSM


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election of models fo& %&edicting %ath loss fo& M ill de%end on thecell &anges.

M has 3 cell &anges and diffe&ent %&ediction model fo& each

La&ge @ells

mall @ells

Mic&ocells

Lrg CllLargeCells


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Antenna is installed a#o'e the ma7im$m height of the s$&&o$nding &oof

to%s. 8&o%agation is mainl* #* diff&action and scatte&ing at &oof to%s in the'icinit* of the mo#ile i.e. the main &a*s %&o%agate a#o'e the &oof to%s.

@ell &adi$s is mainl* !m and no&mall* e7ceeds 3 !m. ;ata5s model and the @ 23/;ata model can #e $sed to calc$late

%ath loss in s$ch cells.

Large CellsLarge Cells

Small CellsSmall Cells


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Antenna is sited a#o'e the median #$t #elo the ma7im$m height ofthe s$&&o$nding &oof to%s.

8&o%agation mechanism is same as la&ge cell Ma7im$m &ange is t*%icall* less than / 3 !ms. ;ata model cannot #e $sed since it is a%%lica#le a#o'e !m. @ 23/:alfish/B!egami model is $sed fo& &adi$s less than "!ms in

$an en'i&onment.

COST 231 - Walfish-Ikegami ModelCOST 231 - Walfish-Ikegami Model


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7ithout free 38, -eteen B, and ,

&e$enc* (f) = ?00 / 2000 M;z&ansmitte& height (hte) = 4 / "0mMo#ile height (h&e) = / 3mCistance (d) = 0.02 / " !m;eight of #$ildings ;&oof (m)

:idth of &oad (m)$ilding se%a&ation # (m)-oad o&ientation ith &es%ect to the di&ect &adio %ath 8hi (o)&ee %ace Loss (Lfsl)

8ath Loss = Lfsl + L&st + Lmsd

L&ts D &oof/to%/to/st&eet diff&action and scatte& lossLmsd D m$ltisc&een diff&action loss

COST 231 - Walfish-Ikegami ModelCOST 231 - Walfish-Ikegami Model


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:ith a f&ee L #eteen #s and ms ( t&eet @an*on )

8ath Loss = 42.> + 20 log(d) + 20 log(f) fo& d = 0.020 !m

Indoor LossIndoor Loss


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Additional loss hich occ$&s at 900 M;z hen mo'ing into a ho$se on the#ottom floo& on ."m height f&om the st&eet.

Bndoo& loss nea& indos ( E m ) is t*%icall* 2 d#.

$ilding loss as meas$&ed #* inish 8 'a&ies #eteen 3 d# and /?d#ith an a'e&age of ?d# ta!en o'e& all floo&s and #$ildings.

Bn o$& %&edictions and calc$lations, as %e& M &ecommendations e illconside& "d# as an a'e&age indoo& loss.

MicrocellsMicrocells


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@ell in hich the #ase station antenna is mo$nted gene&all* #elo &oofto% le'el.

8&o%agation is dete&mined #* diff&action and scatte&ing a&o$nd#$ildings ie. the main &a*s %&o%agate in st&eet can*ons.

Mic&ocells ha'e a &adi$s in the &egion of 200 / 300m . Mic&ocells can #e s$%%o&ted #* smalle& and chea%e& 5s.

MicrocellsModelMicrocellsModel


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7ith a free 38, -eteen -s and ms ( ,treet 9an'on )

8ath Loss (M 900 ) = 0. + 20 log(d) fo& d = 0.020 !m8ath Loss (C@ ?00 ) = 0. + 20 log(d) fo& d = 0.020 !m

8&o%agation loss in mic&ocells inc&eases sha&%l* as the &ecei'e& mo'eso$t of L , (e7 D a&o$nd a st&eet co&ne& ).

20d# of loss co$ld #e added %e& st&eet co&ne&, $% to to o& th&ee co&ne&s.

e*ond, this the @23 / :alfish B!egami model sho$ld #e $sed

Microcells ModelMicrocells Model

FadingFading


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A mo#ile &adio signal en'elo%e has contin$os 'a&iations.hese 'a&iations contin$o$sl* fl$ct$ate the signal le'el and is &efe&&ed

to as the fading %henomenon.

ading in mo#ile en'i&onment is of 2 t*%esD

Sma!! S'a!e Fading

&og;norma! Fading


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Small Scale FadingSmall Scale Fading


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,*sering the Short-term fading with reference to aerage leel

Ray!eigh distribution

r o

r o #d*) $ (erage - instantaneous fluctuations

#small-scale fading)

&o &anges in 40 d# ( 0 d# a#o'e and 30 d# #elo the a'e&age )

&o follos a -a*leigh/dist&i#$tion , since gene&all* signals a&&i'e f&om&eflections onl*, hence small/scale fading is often called -a*leigh/2ading.



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Ray!eigh distributionAs %e& -a*leigh dist&i#$tion inc&ease in fade de%th is in'e&sel*

%&o%o&tional to the %&o#a#ilit* (e7D 0 d# fade ma* occ$& fo& 40 Fof the time, he&e the %&o#a#ilit* of 40d# fade o$ld #e 0 F )

avg level

min recv level

of rcvr deepest fades ( t'picall' ! d- )

:rea of poor &ualit'

;r

8&o#a#ilit* that fade de%ths ill ente& a&ea of %oo& $alit* is&e$i&ed to #e less than 0 F.

Fade argin



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Bf %&o#a#ilit* of small/scale fades is mo&e #elo the minim$m &e$i&edsignal le'el, then this co$ld &es$lt in disto&ted s%eech.

o ens$&e this %&o#a#ilit* is less than 0 F , &ansmit 8oe& sho$ld #ead$sted acco&dingl* to achie'e a high fade ma&gin.

%ace Ci'e&sit* is $ite effecti'e fo& this !ind of fades.

-a*leigh dist&i#$tion onl* occ$&s hen the&e a&e all &eflected a'es andno di&ect L signal. Bf the&e is a di&ect L signal %&esent ith&eflections, then it is -icean dist&i#$tion of fading hich is less se'e&e ,since the di&ect com%onent is &elati'el* m$ch st&onge& than &eflected

a'es and ill &est&ict dee% fades.

S S gg

Log-normal FadingLog-normal Fading


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ime

,,

og-normal ading

mall /scale signal 'a&iation hen a'e&aged o$t is called the local mean and ise7%&essed in log scale of %oe& , and is called Log/no&mal fading.

Log/no&mal 'a&iation is d$e to the te&&ain conto$& #eteen the #s and ms. Bf the te&&ain is an o%en a&ea, then the change in signal ill #e ith distance onl*,

#$t no&mall* the&e a&e o#st&$ctions ( #$ildings, t&ees etc. ) hich ca$se a &a%id'a&iation of signal f&om its local mean o'e& an a&ea of " to "0m


60/186


61/186

Reducing Time Dispersion IssuesReducing Time Dispersion Issues


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%timization and @o$nte&meas$&es fo& ime Cis%e&sion is something'e&* inte&esting and can &es$lt into se'e&al iss$es li!e disto&ted 'oice,

echo and e'en d&o%%ed calls GG

@e&tain co$nte&meas$&es hen ado%ted in the %lanning stage can &ed$ceo& eliminate these iss$es.

Bf %&o#lems occ$& late& on, then o%timization needs to #e done. &o$#leshooting m$lti%ath %&o#lems is a #ig iss$e in li'e neto&!s.



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Site &o'ation

Bdentif* %otential &eflecto&s in the %&edicted cell a&ea.

Locate sites fo& nea& &eflecto&s, this ill #&ing the &eflectionsithin the indo.



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8ire'tiona! -ntennas1Se'toriEation

Hsing ecto&ed cells config, ith the di&ectional antenna %ointing aa* f&om the &eflecto&.

Antenna5s f&ont/#ac! &atio is a 'e&* c&itical %a&amete&.



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O.er 7ater %odies ime dis%e&sion o'e& ate& can ma!e the $alit* o&st

@o'e&ing the a&ea f&om the othe& side of the ate& #od* ill a'oidla&ge %ath diffe&ences #eteen &eflected signals.

ide lo#es can still &es$lt into %&o#lems, he&e hando'e&s sho$ldta!e ca&e off, #* %&o%e&l* setting neigh#o&sI %a&amete&s



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Ti!ting -ntennas

ilting Antenna ill &ed$ce ene&g* &adiated toa&ds the &eflecto&. Antenna5s can #e tilted ho&izontall* o& 'e&ticall*.

;o&izontal tilt ill &ed$ce the co'e&age to a la&ge e7tent, hence'e&tical tilt is the most %&efe&&ed one.

Redu'ing Output Po#er

-ed$ction in o$t%$t %oe& ill &ed$ce the ene&g* f&om #oth di&ect as ell &eflected signal. ;ence, 81M ill not change.

Doppler ShiftDoppler Shift


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he shift in f&e$enc* &elati'e to the s%eed of the mo#ile %hone isCo%%le& hift.

fd = v

fd = ,hift in fre&uenc' in *

v = speed of the mo-ile in m5s = avelength in m

Act$al &ecei'ed ca&&ie& f&e$enc* = fc + fd, hen mo#ile is mo'ingtoa&ds the t&ansmitte&.

Act$al &ecei'ed ca&&ie& f&e$enc* = fc / fd, hen mo#ile is mo'ingaa* f&om the t&ansmitte&.

he&e is no shift , hen the 'ehicle is mo'ing %e&%endic$la& to theangle of a&&i'al of the t&ansmitted signal.



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Ob?e'ti.e

%tim$m $ses of -eso$&ces

-ed$ce Bnte&fe&ence



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F=1

F=2

F=!

F=

Co - Channel Re-use factorCo - Channel Re-use factor


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:

:

> = ?

+ $ d*

> = ,&rt ( ! @ A )

Adjacent-Channel Re-use CriteriaAdjacent-Channel Re-use Criteria


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Adacent A-@J5s sho$ld not #e $sed in the same cell

Bt ill ha'e no %&o#lems in Conlin!, #$t ill ha'e high &is! of $%lin!inte&fe&ence (d$e to mandato&* $%lin! %oe& cont&ol ).

Bf Conlin! d*namic %oe& cont&ol is not $sed

- 40 d*m # +a $ 20 )

- 0 d*m # +a $ -20 )

! d*m

33 d*m

ince all the A-@J5s in a cell a&ef&ame s*nched, imeslot n$m#e&sill align on all the A-@n5s

jj

Adjacent-Channel Re-use CriteriaAdjacent-Channel Re-use Criteria


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Adacent A-@J5s can #e $sed in adacent cells, #$t as fa& as %ossi#lesho$ld #e a'oided.

As s$ch se%a&ation of 200 Khz is s$fficient, #$t ta!ing intoconside&ation the %&o%agation effects, as facto& of %&otection >00

Khz sho$ld #e $sed.

Bn the o&st, Adacent A-@J5s can also #e $sed in adacent cells #*setting a%%&o%&iate hando'e& %a&amete&s ( disc$ssed late& ino%timization)

8&acticall* not %ossi#le in most of the neto&!s d$e to tight &e$se

Cell ConfigurationCell Configuration


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mnidi&ectional @ell

B,

ecto&ial @ell

B,

&o# gain -ntennas &esser penetration/dire'ti.ity Re'ei.es Int (rom a!! dire'tions &o#er imp!ementation 'ost

>igh gain -ntennas >igher penetration/dire'ti.ity Re'ei.es Int (rom !esser dire'tions >igher imp!ementation 'ost


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Sectored CellsSectored Cells


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:1

:2

:!!

/

B1

B2

B! !

/

9

1

92

9!!

/

Re'ei.esInter(eren'e(rom !esserdire'tions)

Re-use PatternsRe-use Patterns


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-e/$se 8atte&ns ens$&es the o%tim$m se%a&ation #eteen @o/@hannels.

-e/$se %atte&n is a fo&mation of a cl$ste& ith a %atte&n of f&e$enc*dist&i#$tion in each cell of the cl$ste&.

ame cl$ste& %atte&n is then &e/$sed.

Pre(erred Re;use Patterns

mni / @ells D 3 cell, cell, 2 cell, 4 cell, 9 cells etc

ecto& / @ells D 319 , 412, 12

3/9 Re-use Pattern3/9 Re-use Pattern


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(1

(2(3 51

53+1

+2+3

(1

(2(3 51

5253+1

+2+3

(1

(2(3 51

5253+1

+2+3(1

(2(3 51

5253+1

+2+3 (1

51

5253

(1

(2(3

52 +1

+2+3

+2+3+2+3 +2+3

(1

Exercise !!!Exercise !!!


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(1

(2(3 51

53+1

+2+3

(1

(2(3 51

5253+1

+2+3

(1

(2(3 51

5253+1

+2+3(1

(2(3 51

5253+1

+2+3 (1

51

5253

(1

(2(3

52+1

+2+3

+2+3+2+3 +2+3

(1

Hsing A-@J5s to9 , do the channel allocation fo& the #elo cells$sing 319 %atte&n

Frequency Allocation in 3/9 patternsFrequency Allocation in 3/9 patterns


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Adacent @hannel Bnte&fe&ence is 'e&* diffic$lt to a'oid ithinthe cl$ste& itself.

1

3

2

6!

4

7

4/12 Reuse Patterns4/12 Reuse Patterns


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D1

D3

51

53

+1

+2+3 D1

(1

(2(3 51

5253+1

+2+351

5253 (1

(2(3+1

+2+3 +1

D1

D2D3

D2D35253 5253

D2 +1

+3

52

D2D3(1

(2(3 51

5253

+2 D1

D2D3(1

ExerciseExercise


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Hsing A-@J5s > to2 do the channel allocation fo& the #elo cells$sing 412 %atte&n.

D1

D2D3 +1

+351

5253

+1

+2+3 D1

D2D3(1

(2(3

(1

(2(3 51

5253+1

+2+351

5253 (1

(2(3+1

+2+3 +1

D1

D2D3

51

5253

+2D1

D2D3

D2D35253 5253

(1

4/12 Pattern Channel Allocation4/12 Pattern Channel Allocation


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1

3!

2 7

4

1112

10 6

412 %atte&n a'oids adacent channels in adacent cells

Reuse Patterns ConclusionReuse Patterns Conclusion


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&arger reuse patterns gi.e redu'tion in inter(eren'e

Re;use patterns be'omes more e((e'ti.e #ith se'toria! 'e!!'on(igurations)

To imp!ement !arge patterns 1 !i$e /32+ Q/23 + more 'hanne!sare reuired)

So #ith !ess resour'es+ the best #ay to p!an is :

3) se optimum no o( 'hanne!s per 'e!!)2) Thus+ in'rease the pattern siEe)

Critical Factors for good RF NetworkCritical Factors for good RF Network


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• Grid based RF design)

• Maintain standard aEimuths #hi!e se'toriEing 'e!!s 6 This

ma$es (reuen'y p!an easier• *orre't 'hoi'e o( antenna type (or spe'i(i' 'o.erage

reuirements)

• se o( optima! antenna heights 6 Shou!d be su((i'ient to

'ater to the 'o.erage area+ but shou!d not e,'eed thereuirement+ e!se it resu!ts into !arge spi!!o.ers andinter(eren'e+ ma$ing reuse di((i'u!t@@

• se optima! ti!t 6 9!e'tri'a! ti!t as (ar as possib!e) Insome 'ases 'ombination o( e!e'tri'a! and me'hani'a! ti!ts


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Quality of ServiceQuality of Service


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8ffect of 9,S :

Cissatisfied @$stome&s### @$stome&s face desc&i#es *o$& %&ofit

c$&'e/// Cissatisfied c$stome& %&e'ents 0 ne

-e'en$e/// @$stome& itcho'e&s/// Less Je @$stome&s/// @ost of C&o%%ed @alls/// @ost of loc!ed @alls

Importance of RF OptimizationImportance of RF Optimization


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• - %timization is a contin$o$s and ite&ati'e %&ocess.

• Main oal o achie'e %e&fo&mance le'els to a ce&tain set

standa&d.• Jeto&! s$#sc&i#e&s e7%ect i&eline1nea& i&eline $alit*.

• Jeto&! s$#sc&i#e&s also e7%ect 00 F a'aila#ilit* at all gi'entimes.

• - neto&! o%timization is a %&ocess to t&* and meet thee7%ectation of s$#sc&i#e&s in te&ms of co'e&age, o,neto&! a'aila#ilit*.

• - o%timization also aims to ma7imize the $tilit* of thea'aila#le neto&! &eso$&ces.

• 6ach o%e&ato& has a ce&tain set of decided K8Bs (Ke*8e&fo&mance Bndicato&s) #ased on hich the o%e&ato& g$agesthe %e&fo&mance of his neto&!.



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• -1Access Jeto&! K8Bs can #e #&oadl* classified intoth&ee t*%es

a) Access &elated K8B#) &affic1-eso$&ce Hsage &elated K8B

c) ;ando'e& &elated K8B

• 67am%les of access K8B

a)C@@; C&o% &ate #) @all set$% s$ccess &atec)C@@; loc!ing, etc.

• 67am%les of &affic K8B

a)@; C&o% -ate #) @all s$ccess &ate

c)@; loc!ing, etc.• 67am%les of hando'e& %e&fo&mance K8B

a);ando'e& $ccess &ate #) ;ando'e& fail$&e &ate.

c);ando'e& %e& ca$se, %e& neigh#o$&, etc.



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• A%a&t f&om the K8Bs mentioned ea&lie& the o%e&ato& ma*ha'e his on set of c$stom K8Bs hich the o%e&ato& feels is

c&itical to g$age the %e&fo&mance of his neto&!.• - o%timization %&ocess d&i'es the effo&t to achie'e and

maintain the neto&! %e&fo&mance K8B.

• %timization can #e #&oadl* di'ided into 3 catego&ies, asfollos

a) ;a&da&e %timization

#) 8h*sical %timization

c) Cata#ase18a&amete& %timization

• ene&all* the acti'ities mentioned a#o'e a&e done in %a&allel.

Bn some cases one ma* %&ecede the othe&.


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Network Optimization Cycle…Network Optimization Cycle…


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!ptimization "tages!ptimization "tages

RF $lanning

Network Rollo+t

'B+ild $hase

Nominal Cell Design

RF Fine t+ning

Data&ase

parameter optimization

$hysical'

%ardware

!ptimization

Network $re 4

!ptimization

(ra0c !ptimization


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Hardware Optimization -Hardware Optimization -Typical Hardware Problems


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• Bn most cases, ha&da&e fail$&es on a 1@ o& an* %a&tof the access neto&! ala&ms a&e gene&ated at the M@,

hich hel% in identif*ing the fa$lt• Bn some cases, the&e a&e no ala&ms gene&ated

• Ke* statistics f&om M@- co$ld %oint toa&ds ha&da&efail$&es *%ical statistics hich indicate s$ch %&o#lems a&e

a) 8oo& Assignment $ccess1;igh Assignment fail$&e &ate

#) ;igh @;1C - Loss

c) ;igh hando'e& fail$&e &ate

d) Loe& call 'ol$me1t&affic on the cell


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96/186

• 8ath #alance %&o#lems his is also one of the commonca$ses fo& %oo& cell %e&fo&mance.

%ath #alance is %egged as an M@- statistic on a cell #asisene&al fo&m$la is %ath #alance=$%lin! %athloss donlin!%athloss.

Bn Moto&ola %ath#alance= %athloss+0.

he&e %athloss = $%lin! %athloss donlin! %athloss.$%lin! %athloss = act$al Ms 7%oe& &7le'S$l

donlin! %athloss = act$al s 7%oe& &7le'Sdl

Bt is desi&a#le to ha'e the %athloss 'al$e as O0P hich&e%&esents a #alanced %ath. ;oe'e& a de'iation of +1/ 0 isacce%ta#le



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• 8ath #alance %&o#lems Bf the %ath#alance is #elo 00 o&a#o'e 20, it indicates that the&e co$ld #e a %&o#lem in

eithe& donlin! o& $%lin!. 8 'al$e a#o'e 20 &e%&esents aea!e& $%lin! and st&onge& donlin!, he&eas 8 'al$e #elo00 o$ld &e%&esent a ea!e& donlin!.

Bf M;A1MA is $sed o& &ecei'e di'e&sit* is a%%lica#le,anadditional 3 d gain is int&od$ced in the $%lin!. Bn s$ch case a

de'iation of 20 is acce%ta#le, i.e, a 8 of 9" o$ld #e no&malin s$ch case.

• 8ath alance Bf the 8 statistic indicates %&o#lem in the

donlin!1$%lin! the - %ath sho$ld #e t&aced fo& %ossi#leha&da&e fa$lts. 8ossi#le things that co$ld go &ong a&e

a) ;igh T:- d$e to fa$lt* feede& ca#le

#) Bm%&o%e& connecto&isation

c) a$lt* com#ine&



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d) a$lt* antenna im%&o%e& im%edance matching #eteen

antenna and feede& ca#le (&a&e case)

• 8&ocesso& %&o#lems

• he %&esent e$i%ment a&chitect$&e is $ite &o#$st and ith

the e'ol$tion of TLB techni$es, the diffe&ent ha&da&e mod$les

ha'e #een com%acted into single $nits.

• he c$&&ent -Ns1-Hs a&e ha'ing in#$ilt %&ocessing a#ilities a%a&t

f&om also containing the - %h*sical channels.

• ;oe'e& in %laces he&e olde& e$i%ment (fo& e.g. Moto&ola

Bn@ell1Mcell) a&e still in $se, %&o#lems ith %&ocesso& (8-@ o&M@H), co$ld #e enco$nte&ed.

• hese %&o#lems a&e easil* identifia#le #* d&i'e test and $s$all* also

sho $% deg&adation on M@- statistics. ;oe'e& in the c$&&ent

scena&io these %&o#lems ha'e &a&e occ$&ences.



99/186

• @1&anscode& 8&o#lems Altho$gh the occ$&&ence is &a&e, the&e

a&e instances he&e some %a&t of &anscode& o& timeslot on the

8@M lin! go fa$lt*. Bn s$ch cases, the timeslot ma%%ing needs to #eidentified and a%%&o%&iate t&o$#leshooting ste%s need to #e ta!en.

hese %&o#lems can seldom #e identified #* d&i'e testing.

• te%s fo& ;a&da&e %timization

a) @hec! f&om M@- statistics fo& indications of ha&da&e fa$lts

#) @hec! e'ent logs f&om M@- to find o$t if an* ala&ms e&e

gene&ated

c) @ond$ct call test on the site1cell in $estion chec! fo&

assignment fail$&es, hando'e& fail$&es, f&om la*e& 3 messages.

Hardware Optimization –Hardware Optimization –Hardware Optimization Steps


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• te%s fo& ;a&da&e %timization

d) Bsolate the %&o#lem to the s%ecific -N. his can #e done #*

Oloc!ingP the s$s%icio$s -N.e) @hec! fo& donlin! &ecei'e le'el on each -N. Bn some cases the

donlin! &ecei'e le'el on a %a&tic$la& -N ma* #e 'e&* lo, d$e to

fa$lt* &adio.

f) -e$est T:- test to #e %e&fo&med if the %&o#lem a%%ea&s to

#e &elated to %oo& %ath #alance.

g) @hec! fo& im%&o%e& connecto&ization, im%&o%e& antenna

installation. ne loose connecto& co$ld s!e the %e&fo&mance of the

enti&e cellGGG

f) Bf the %&o#lem is not isolated to a #ad -N1 othe& ha&da&e f$&the& in'estigations needed to chec! othe& %ossi#le

fa$lt* ha&da&e in the @1N@C-


101/186

Physical RF OptimizationPhysical RF Optimization


102/186

• A ell designed - is !e* to good neto&! %e&fo&mance.

• Mo&e often than not, the act$al neto&! #$ilt is de'iated

f&om the neto&! designed f&om the des!to%. he 'a&iationsa&e

a) Act$al site locations a&e aa* f&om the nominal %lannedlocations.

#) Bt is not %&actica#le to #$ild a g&id/#ased neto&! d$e

to se'e&al const&aints.

c) Antenna heights ma* diffe& f&om the %lanned antennaheights.

• 8h*sical - o%timization ma* #e done at se'e&al stages of

neto&! &ollo$t.



103/186

• 8h*sical - %timization is an essential &e$i&ement d$&ingthe neto&! #$ild1%&e o%timization stages. Bn most cases the

6M 'endo& is &es%onsi#le fo& the neto&! d$&ing this %haseand he ca&&ies o$t the %&ocess to ens$&e that the act$alneto&! is as nea& good as the des!to% designed one.

• he %&ocess com%&ises of cond$cting a d&i'e test fo& theenti&e cl$ste&, hich ma* com%&ise of one o& se'e&al @

a&eas.• he d&i'e test &es$lts a&e %lotted on a B ma% and

deficiencies in co'e&age1inte&fe&ence %&o#lems a&e identified#* %lotting -7le'1-7$al 'al$es.

• Most of the co'e&age deficiencies a&e fi7ed #* ma!ingchanges to antenna heights(&a&e), #o&e and tilts.

• At late& stages %a&amet&ic o%timization is done to #&ing theneto&! %e&fo&mance close to des!to% design.



104/186

• - o%timization is also ca&&ied o$t d$&ing neto&! e7%ansion%hase, i.e hen ne site o& g&o$% of sites a&e added into the

neto&!.• Bn man* neto&!s - o%timization is also done as a &eg$la&

%&ocess to maintain good neto&! %e&fo&mance.

• - o%timization is hel%f$l in &esol'ing s%ecific co'e&age%&o#lems o& inte&fe&ence %&o#lems, cell o'e&&each, no

dominant se&'e& iss$es, etc.• *%ical th$m# &$le to follo hile ca&&*ing o$t %h*sical -

o%timization fo& &esol'ing co'e&age o& inte&fe&ence iss$es /te% D/ &* tilting the antennas.

te% 2D/ &* changing the o&ientation.te% 3D/ Bnc&ease o& &ed$ce the height ifftilt1&eo&ientation does not sol'e the %&o#lem

te% 4D/ @hange the antenna t*%e as a last &eso&t.


105/186

Database/Parameter OptimizationDatabase/Parameter Optimization


106/186

• he %&ocess sta&ts the moment a M neto&! goes on ai&and contin$es on a da*/to/da* #asis, till the neto&! is

o%e&ational.• Hnde& M each 'endo& has h$nd&eds of %a&amete&s hich

can #e %la*ed ith to achie'e diffe&ent %e&fo&mance met&ics$nde& diffe&ent scena&ios.

• Hs$all* most of the %a&amete&s a&e ena#led ith defa$lt

settings and a&e ala*s !e%t $nchanged. ;oe'e& the&e a&esome s%ecific %a&amete&s hich cont&ol the - %e&fo&mancehich can #e changed on a cell o& e'en ca&&ie&/le'el, toachie'e s%ecific im%&o'ements.

f d d #

Database/Parameter OptimizationDatabase/Parameter Optimization


107/186

• M eat$&es efo&e %&oceeding to data#ase %a&amete&s,let $s disc$ss some im%o&tant M feat$&es hich a&e

commonl* #eing $sed in c$&&ent neto&!s.• M neto&!s o&ldide a&e mainl* affected #* the

folloing t*%es of %&o#lemsD/ ) @o'e&age iss$es, 2)Bnte&fe&ence iss$es, 3)@a%acit* iss$es.

• Bnte&fe&ence in M neto&!s can #e &ed$ced significantl*

#* $sing some s%ecial feat$&es, as mentioned • &e$enc* ;o%%ing

• CN and Toice Acti'it* Cetection

• C*namic 8oe& @ont&ol

Database Optimization – Frequency HoppingDatabase Optimization – Frequency Hopping

h i i f h d di d i


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• &e$enc* ho%%ing is one of the standa&dised ca%acit*enhancement feat$&es in M s*stem. Bt offe&s a significant

ca%acit* gain itho$t an* costl* inf&ast&$ct$&e &e$i&ements.• &e$enc* ho%%ing can co/e7ist ith most of the othe&

ca%acit* enhancement feat$&es and in man* cases itsignificantl* #oosts the effect of those feat$&es.

• &e$enc* ho%%ing can #e #&iefl* defined as a se$ential

change of ca&&ie& f&e$enc* on the &adio lin! #eteen themo#ile and the #ase station.

• :hen f&e$enc* ho%%ing is $sed, the ca&&ie& f&e$enc* ischanged #eteen each consec$ti'e CMA f&ame. his meansthat fo& each connection the change of the f&e$enc* ma*ha%%en #eteen e'e&* #$&st.


A fi h f h i d i ili


109/186

• At fi&st, the f&e$enc* ho%%ing as $sed in milita&*a%%lications in o&de& to im%&o'e the sec&ec* and to ma!e the

s*stem mo&e &o#$st against amming.

• Bn cell$la& neto&!, the f&e$enc* ho%%ing also %&o'ides someadditional #enefits s$ch as f&e$enc* di'e&sit* andinte&fe&ence di'e&sit*.



110/186

re/uency

;ime

1

2

3

+all is transmitted through seeralfre/uencies in order toC average the interference (interference diversit')

C minimise the impact of fading (fre&uenc' diversit')


h t th d f f h i i M


111/186

• he&e a&e to methods of f&e$enc* ho%%ing in M,=ase#and 2&e$enc* ;o%%ing ( ;) and ,*nthesised2&e$enc* ;o%%ing

(- ;).• Bn the #ase#and f&e$enc* ho%%ing the -Ns o%e&ate at

fi7ed f&e$encies.

• &e$enc* ho%%ing is gene&ated #* sitching consec$ti'e#$&sts in each time slot th&o$gh diffe&ent -Ns acco&ding to

the assigned ho%%ing se$ence.• he n$m#e& of f&e$encies to ho% o'e& is dete&mined #* the

n$m#e& of -Ns


h fi t ti l t f th @@; -N i t ll d t h it


112/186

• he fi&st time slot of the @@; -N is not alloed to ho%, itm$st #e e7cl$ded f&om the ho%%ing se$ence.

•

his leads to th&ee diffe&ent ho%%ing g&o$%s.• he fi&st g&o$% doesnPt ho% and it incl$des onl* the @@;

time slot.

• he second g&o$% consists of the fi&st time slots of the non/@@; -Ns.

• he thi&d g&o$% incl$des time slots one th&o$gh se'en f&ome'e&* -N.

Database Optimization – Baseband HoppingDatabase Optimization – Baseband Hopping


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B

RTSL 0 1 2 3 4 5 6 7

TRX-1

TRX-2

TRX-3

TRX-4

f1 B = BCCH timeslot. It does not hop.

f2

f3

f4

Time slot 0 of TRX-2,-3,-4 hop over f2,f3,f4.

Time slots 1... of !ll TRXs

hop over "f1,f2,f3,f4#.

%aseband hopping 1%% F>)

Database Optimization – RF HoppingDatabase Optimization – RF Hopping


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• Bn the s*nthesised f&e$enc* ho%%ing all the -Ns e7ce%t

the @@; -N change thei& f&e$enc* fo& e'e&* CMAf&ame acco&ding to the ho%%ing se$ence.

• h$s the @@; -N doesnPt ho%.

• he n$m#e& of f&e$encies to ho% o'e& is limited to >3, hichis the ma7im$m n$m#e& of f&e$encies in the Mo#ile

Allocation (MA) list.



115/186

B(R5/6

Non-BCCH TRXs are hopping over

the MA-list (f1,f2,f3,...,fn atta!he" to the !ell.

(R5/7

B # BCCH ti$eslot. TRX "oes not hop.

f1,

f2,

f3,

fn

f1,

f2,

f3,

fn

. . . .

Synthesised hopping 1RF F>)



116/186

• he #iggest limitation in #ase#and ho%%ing is that the n$m#e&

of the ho%%ing f&e$encies is the same as the n$m#e& of-Ns.

• Bn s*nthesised ho%%ing the n$m#e& of the ho%%ingf&e$encies can #e an*thing #eteen the n$m#e& of ho%%ing-Ns and >3.



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-3

;>-1

-H

1? 2? 3

Dig. 0

;>-1

;>-2

5S+;+S<

5++H

re/uency

;ime

123


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• he @ell Allocation (@A) is a list of all the f&e$enciesallocated to a cell. he @A is t&ansmitted &eg$la&l* on the@@;.

• Hs$all* it is also incl$ded in the signaling messages thatcommand the mo#ile to sta&t $sing a f&e$enc* ho%%ing logicalchannel. he cell allocation ma* #e diffe&ent fo& each cell.

• Bn 8M 900 the @A list ma* incl$de all the 24 a'aila#le

f&e$encies UM 04.0?V.• ;oe'e&, the %&actical limit is >4, since the MA/list can onl*

%oint to >4 f&e$encies that a&e incl$ded in the @A list .

Database Optimization – RF Hopping – MobileDatabase Optimization – RF Hopping – MobileAllocationAllocation

• he MA is a list of ho%%ing f&e$encies t&ansmitted to a


119/186

• he MA is a list of ho%%ing f&e$encies t&ansmitted to amo#ile e'e&* time it is assigned to a ho%%ing %h*sical channel.

•

he MA/list is a$tomaticall* gene&ated if the #ase#andho%%ing is $sed.

• Bf the neto&! $tilises the - ho%%ing, the MA/lists ha'e to#e gene&ated fo& each cell #* the neto&! %lanne&.

• he MA/list is a#le to %oint to >4 of the f&e$encies defined

in the @A list• ;oe'e&, the @@; f&e$enc* is also incl$ded in the @A list,

so the %&actical ma7im$m n$m#e& of f&e$encies in the MA/list is >3.

•

he f&e$encies in the MA/list a&e &e$i&ed to #e ininc&easing o&de& #eca$se of the t*%e of signaling $sed tot&ansfe& the MA/list.

Database Optimization – RF Hopping – HSNDatabase Optimization – RF Hopping – HSN

• he ;o%%ing ,e$ence J$m#e& (;J) indicates hich


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• he ;o%%ing ,e$ence J$m#e& (;J) indicates hichho%%ing se$ence of the >4 a'aila#le is selected.

•

he ho%%ing se$ence dete&mines the o&de& in hich thef&e$encies in the MA/list a&e to #e $sed.

• he ;Js / >3 a&e %se$do &andom se$ences $sed in the&andom ho%%ing hile the ;J 0 is &ese&'ed fo& a se$entialse$ence $sed in the c*clic ho%%ing.

• he ho%%ing se$ence algo&ithm ta!es ;J and J as anin%$t and the o$t%$t of the ho%%ing se$ence gene&ation is aMo#ile Allocation Bnde7 (MAB) hich is a n$m#e& &angingf&om 0 to the n$m#e& of f&e$encies in the MA/lists$#t&acted #* one.

• he ;J is a cell s%ecific %a&amete&.

Database Optimization – RF Hopping – MAIODatabase Optimization – RF Hopping – MAIO

• :hen the&e is mo&e than one -N in the $sing the same


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• :hen the&e is mo&e than one -N in the $sing the sameMA/list the Mo#ile Allocation Bnde7 +ffset (MAB) is $sedto ens$&e that each -N $ses ala*s an $ni$e f&e$enc*.

• 6ach ho%%ing -N is allocated a diffe&ent MAB. MAB isadded to MAB hen the f&e$enc* to #e $sed is dete&minedf&om the MA/list.

• MAB and ;J a&e t&ansmitted to a mo#ile togethe& ith

the MA/list.• he MABoffset (Jo!ia) is a cell s%ecific %a&amete& defining

the MAB-N fo& the fi&st ho%%ing -N in a cell. he MABs

fo& the othe& ho%%ing -Ns a&e a$tomaticall* allocatedacco&ding to the MABste%/%a&amete&

Database Optimization – RF Hopping – MAIODatabase Optimization – RF Hopping – MAIO


122/186

"S< Hopping algorithm

-3

@ A HS@

;>-1 0

;>-2 1

;>-3 2

or this ;D


123/186

he MABste% is a Jo!ia s%ecific %a&amete& $sed in the MAB

allocation to the -Ns.

•

he MAB fo& the fi&st ho%%ing -Ns in each cell is defined #* thecell s%ecific MABoffset %a&amete&

• MABs fo& the othe& ho%%ing -Ns a&e assigned #* adding theMABste% to the MAB of the %&e'io$s ho%%ing -N

• MAB-N(J) = MABoffset + MABste%(n/)

Sector ;> B HS@


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Sector ;> B HS@


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:hen - ;o%%ing is de%lo*ed the @@; la*e& is %lanned $singthe standa&d 4N3 o& N3 o& an inte&mediate s$ita#le %atte&n.

•

Ma7im$m %&otection is assigned hile %lanning to the @@;la*e& as it is c&itical to call set$% %&oced$&e.

• o& the @; la*e& the&e a&e mainl* th&ee t*%es of idel*$sed &e$se %atte&ns

• N All secto&s in the neto&! $se a single MA list.

• N3 3 MA lists a&e c&eated. ec A of each cell $ses MAL,

ec $ses MAL2 and ec 3 $ses MAL3

• Ad/hoc1Mi7ed ; M$lti%le MA lists a&e $sed. @an ha'e as

man* MA lists as the n$m#e& of secto&s in the neto&!. he

&e$se is #ased on f&actional loading ith a ma7im$m loadingfacto& of 00 F.

Database Optimization – RF Hopping – LoadingDatabase Optimization – RF Hopping – LoadingFactorFactor

• Loading acto& his is the &atio of no of -N to the no of


126/186

Loading acto& his is the &atio of no of -N to the no ofho%%ing f&e$encies in the MA list

•

Loading acto& = Jo of ;o%%ing -N1Jo of &e$encies.• o& eg. Loading facto& = "0 F if the&e a&e 2 -N and 4

ho%%ing f&e$encies.

• Loest %&acticall* achie'a#le loading facto& is 33 Ffo&N3, F fo& N and highest is 00 F .

• Hs$all* 00F loading facto& is $sed in case of ad/hoc -ho%%ing, fo& cells ith highe& config$&ation (>/>/>),hoe'e& fo& loe& config$&ation li!e (2/2/2) "0 Floading facto& co$ld #e $sed.

•

Bn case of ad/hoc ho%%ing the loading facto& can #e%lanned to #e s%ecific to the cell config$&ation.

Database Optimization – DTX & Power ControlDatabase Optimization – DTX & Power Control

• 8oe& cont&ol and the CN a&e standa&d M feat$&es,


127/186

8oe& cont&ol and the CN a&e standa&d M feat$&es,hich a&e designed to minimise the inte&fe&ence.

•

hese a&e mandato&* feat$&es in the HL, #$t it is $% to theneto&! o%e&ato& to decide hethe& to $se them o& not.

• CN %&e'ents $nnecessa&* t&ansmissions hen the&e is noneed to t&ansfe& info&mation

• 8oe& cont&ol is $sed to o%timise the t&ansmitted signal

st&ength so that the signal st&ength at the &ecei'e& is stillade$ate.

• hese feat$&es can #e indi'id$all* acti'ated fo& $%lin! anddonlin!.

•

%e&ato&s ha'e #een idel* $sing #oth feat$&es in HLdi&ection mainl* in o&de& to ma7imise the #atte&* life inmo#iles.


• Bn a non/ho%%ing neto&! these feat$&es %&o'ide some $alit*


128/186

Bn a non ho%%ing neto&! these feat$&es %&o'ide some $alit*gain fo& some $se&s, #$t this gain cannot #e t&ansfe&&edeffecti'el* to inc&eased ca%acit*, since the ma7im$minte&fe&ence e7%e&ienced #* each $se& is li!el* to &emain thesame.

• he %oe& cont&ol mechanism doesnPt f$nction o%timall*#eca$se the inte&fe&ence so$&ces a&e sta#le ca$sing chain

effects he&e the inc&ease of t&ansmission %oe& of onet&ansmitte& ca$ses o&se $alit* in the inte&fe&ed &ecei'e&,hich in t$&n ca$ses the %oe& inc&ease in anothe&t&ansmitte& and so on.

• his means that, fo& e7am%le, one mo#ile located in a

co'e&age limited a&ea ma* se'e&el* limit the %ossi#ilit* ofse'e&al othe& t&ansmitte&s to &ed$ce thei& %oe&.


• Bn a non/ho%%ing neto&! these feat$&es %&o'ide some $alit*


129/186

n a n n %% ng n t & t f at$& %& m $a t*gain fo& some $se&s, #$t this gain cannot #e t&ansfe&&edeffecti'el* to inc&eased ca%acit*, since the ma7im$minte&fe&ence e7%e&ienced #* each $se& is li!el* to &emain thesame.

• he %oe& cont&ol mechanism doesnPt f$nction o%timall*#eca$se the inte&fe&ence so$&ces a&e sta#le ca$sing chain

effects he&e the inc&ease of t&ansmission %oe& of onet&ansmitte& ca$ses o&se $alit* in the inte&fe&ed &ecei'e&,hich in t$&n ca$ses the %oe& inc&ease in anothe&t&ansmitte& and so on.

• his means that, fo& e7am%le, one mo#ile located in a

co'e&age limited a&ea ma* se'e&el* limit the %ossi#ilit* ofse'e&al othe& t&ansmitte&s to &ed$ce thei& %oe&.

• Bn a &andom ho%%ing neto&! the $alit* gain %&o'ided #*



130/186

%% g * g % *#oth feat$&es can #e efficientl* e7%loited to ca%acit* gain#eca$se the gain is mo&e e$all* dist&i#$ted among the $se&s.

• ince the t*%ical 'oice acti'it* facto& (also called CNfacto&) is less than 0.", CN effecti'el* c$ts the neto&!load in half hen it is $sed.

• he %oe& cont&ol o&!s mo&e efficientl* #eca$se each $se&

has man* inte&fe&ence so$&ces. Bf, one inte&fe&e& inc&easesits %oe&, the effect on the $alit* of the connection is notse&io$sl* affected. Bn fact, it is %&o#a#le that some othe&inte&fe&e&s a&e dec&easing thei& %oe&s at the same time.h$s, the s*stem is mo&e sta#le and chaining effects

mentioned ea&lie& do not occ$& f&e$entl*.



131/186

"(2@C+ on 1. dBD;> on 2.! dB+ on? D;> on !. dB

"(2@C+ on 1. dBD;> on 2.! dB+ on? D;> on !.$ dB

euse 31? ;E 3'm1h euse 31? ;E !0'm1h

+12 impro)ement

he sim$lated gain of 8@ and CN ith ;.


• CN has some effect on the -N$al dist&i#$tion.


132/186

• Jo&mall* the 6- is a'e&aged o'e& the d$&ation of one

A@@; f&ame lasting 0.4? seconds and consisting of 04CMA f&ames.

• ;oe'e&, fo$& of these CMA f&ames a&e $sed fo&meas$&ements, so that onl* 00 #$&sts a&e act$all*t&ansmitted and &ecei'ed.

• :hen CN is in $se and the&e is no s%eech acti'it*, onl* the#$&sts t&ansmitting the silence desc&i%to& f&ame (BC/f&ame) and the A@@; a&e t&ansmitted.

• :hen the&e a&e %e&iods of no s%eech acti'it*, the 6- isestimated o'e& $st the #$&sts ca&&*ing the silencedesc&i%to& f&ame and the A@@;. his incl$des onl* 2#$&sts o'e& hich the 6- is a'e&aged (s$# $alit*).


• 6- gets a'e&aged m$ch mo&e effecti'el* hen CN is not


133/186

g g *$sed *ielding to a $alit* dist&i#$tion he&e the %&o%o&tion ofmode&ate $alit* 'al$es is enhanced.

• he s$# $alit* dist&i#$tion is ide& than the f$ll $alit*dist&i#$tion, meaning that mo&e good and #ad $alit* sam%lesa&e e7%e&ienced.

• he diffe&ences #eteen f$ll and s$# $alit* dist&i#$tions

a&e la&gest in f&e$enc* ho%%ing neto&!s $tilising lof&e$enc* allocation &e$se, since in that !ind of neto&!s theinte&fe&ence sit$ation ma* #e 'e&* diffe&ent f&om #$&st to#$&st.

• A co$%le of se'e&el* inte&fe&ed #$&sts ma* ca$se 'e&* #ad

$alit* fo& the s$# $alit* sam%le hen the* ha%%en to occ$&in the set of 2 #$&sts o'e& hich the s$# $alit* isdete&mined.


• he f$ll $alit* sam%le of the same time %e&iod has %&o#a#l*


134/186

* % % % *onl* mode&ate $alit* dete&io&ation #eca$se of the #ette&a'e&aging of 6- o'e& 00 #$&sts.

• Bn a &eal neto&! $tilising CN the $alit* dist&i#$tion is ami7t$&e of f$ll and s$# $alit* sam%les.

• he %&o%o&tions of f$ll and s$# sam%les de%end on the s%eechacti'it* facto& also !non as the CN facto&.

• he diffe&ences in the 6- a'e&aging %&ocesses ca$sesignificant diffe&ences in the -NHAL dist&i#$tions. hesediffe&ences sho$ld #e ta!en into acco$nt hen the -NHALdist&i#$tions of neto&!s $tilising and not $tilising CN a&ecom%a&ed.


135/186


• 8oe& @ont&ol hat to o%timize


136/186

%

• he %a&amete&s to o%timize in case of %oe& cont&ol a&e the

indo settings.



137/186

Conlin! 8oe& @ont&ol *%ical -7le' :indo settings

Downlink Rxlev (dBm)

B S T x P o w e

- / -/

42



138/186

Downlink Rx!"#l

B S T x P o w e

0 4

42

Conlin! 8oe& @ont&ol *%ical -7$al :indo settings



139/186

$%link Rxlev (dBm)

& S T x P o w e

- 0 -0

33

H%lin! 8oe& @ont&ol *%ical -7le' :indo settings

/


• 8oe& @ont&ol %a&amete&s hich can #e set


140/186

• Conlin!1H%lin! -7le' th&eshold (lS&7le'SdlS% 1lS&7le'S$lS%)

• -7$al th&eshold(lS&7$alSdlS% 1 lS&7$alS$lS%)

• 8oe& inc&ement1&ed$ction ste% size(%oSincSste%SsizeSdl1%oS&edSste%SsizeSd)

• C*namic ste% ad$st algo&ithm(d*nSste%Sad)

• 8oe& @ont&ol eat$&es



141/186

• #ecti'e is to &ed$ce a'e&age inte&fe&ence

•

Bn case of $%lin! also hel%s in sa'ing #atte&* %oe&• Algo&ithm o&!s on meas$&ement &e%o&ts sent #* the M

e'e&* 4?0 ms (A@@; f&ame)

• Conlin! %oe& cont&ol cannot #e a%%lied to @@; ca&&ie&

• H%lin! %oe& cont&ol is mandato&* #$t donlin! %oe& cont&olis not mandato&*. eat$&e selecta#le #* the o%e&ato&.

• o& cont&olling inte&fe&ence in the neto&! the o%e&ato& $sesCN, 8oe& @ont&ol and &e$enc* ;o%%ing. hese feat$&eseffecti'el* act as com#ined fo&ces in inte&fe&ence &ed$ction

and im%&o'ed call $alit*.

Database OptimizationDatabase Optimization

*%ical %&o#lems hich M s$#sc&i#e&s e7%e&ience a&e


142/186

• @o'e&age iss$es

•

Toice $alit* iss$es• Access iss$es1congestion

• ;ando'e& &elated iss$es

• C&o%%ed calls

8a&amete&s a&e #&oadl* classified into the folloing g&o$%s



143/186

• Access &elated %a&amete&s

•

@all handling1;ando'e& &elated %a&amete&s• @ongestion &elated %a&amete&s



144/186

Database Optimization – IDLE Mode CellDatabase Optimization – IDLE Mode CellSelectionSelection

W he M $ses a %ath loss c&ite&ion %a&amete& @ tod t min h th ll is s it #l t m% n UM 03 22V


145/186

dete&mine hethe& a cell is s$ita#le to cam% on UM 03.22V

W @ de%ends on 4 %a&amete&sDW . -ecei'ed signal le'el (s$ita#l* a'e&aged)

W 2. he %a&amete& &7Le'AccessMin, hich is #&oadcast on the@@;, and is &elated to the minim$m signal that the o%e&ato&ants the neto&! to &ecei'e hen #eing initiall* accessed #* an

MW 3. he %a&amete& ms78&Ma7@@;, hich is also #&oadcast on

the @@;, and is the ma7im$m %oe& that an M ma* $se heninitiall* accessing the neto&!

W 4. he ma7im$m %oe& of the M.


Cell "electionin.D,# *ode8&asedonC6


146/186

Cell "election in .D,# *ode8 &ased on C6

9 Radio Criteria

A : Recei)ed ,e)el A)erage / p6

C6 : ;A / *a1;B8


147/186

W Bn case of &eselection f&om one cell to anothe& in the same locationa&ea the @ 'al$e of ta&get cell m$st #e highe& than so$&ce cell

W Bn case of &eselection to a ta&get cell in a diffe&ent location a&ea the @ 'al$e m$st #e g&eate& than that of the so$&ce cell #* adata#ase %a&amete& QcellS&eselectSh*ste&esisR

@ell -eselection @2

W @2 is an o%tion M feat$&e hich can onl* #e $sed fo& cell&eselection, it can #e ena#led o& disa#led on a cell #asis.

W Bf @2 %a&amete&s a&e not #eing #&oadcast the @ %&ocess is $sed fo&&eselection.

@ell -eselection @2



148/186

W @2= @ + cellS&eselectSoffset tem%o&a&* offset ;

(%enalt*Stime ) (fo& %enalt*Stime E3)W ;= 0 if %enalt*Stime

W ;= if E %enalt*Stime

W @2= @ cellS&eselectSoffset (fo& %enalt*Stime= 3)

:h* @2


149/186

W Bn d$al#and neto&!// to gi'e diffe&ent %&io&ities fo&

diffe&ent #andW Bn m$ltila*e&// to gi'e %&io&it* to mic&ocell fo& slo mo'ing

t&affic

W An* othe& s%ecial case he&e s%ecific cell &e$i&ed highe&%&io&it* than the &est

@ell -eselection t&ateg*

W 8ositi'e offset// enco$&age Ms to select that cell

W Jegati'e offset// disco$&age Ms to select that cell fo& the

d$&ation %enalt* ime %e&iod



150/186

Database Optimization – HandoversDatabase Optimization – Handovers

;ando'e&


151/186

W he hando'e& (;) %&ocess is one of the f$ndamental%&inci%les in cell$la& mo#ile &adio, maintaining the call in%&og&ess hilst the mo#ile s$#sc&i#e& is mo'ing th&o$gh theneto&!.

W Bn idle mode the M does a cell &eselection, he&eas indedicated mode the M %e&fo&ms a hando'e&.

W ;ando'e&s a&e mainl* classified into to t*%es

W A) Bnte& cell hando'e&s

W ) Bnt&a cell hando'e&s

W Bnte& cell hando'e&s f$&the& classified asW Bnte& ie #eteen to cells #elonging to diffe&ent

@s

W Bnt&a ie #eteen to cells #elonging to same @

;ando'e&



152/186

W Bnt&a cell hando'e&s is the sitching of call f&om onechannel1-N to anothe& -N ithin the same cell1. hisis an o%tional feat$&e hich can #e ena#led on a cell #asis.Bnt&a cell hando'e&s $s$all* ta!e %lace hen the -7$al onthe so$&ce channel dete&io&ates.

;ando'e& %&ocess ma* #e initiated d$e to the folloing main

&easonsW -adio @&ite&ia

W o maintain &ecei'e le'el1&ecei'e $alit*

W A#sol$te M/ distance

W 8oe& $dget

W Jeto&! @&ite&ia

W &affic load (to manage t&affic dist&i#$tion)

W ;ando'e&s also classified as im%e&ati'e1non/im%e&ati'e #asedon the &eason fo& hich the %&ocess is t&igge&ed



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on the &eason fo& hich the %&ocess is t&igge&ed.

W he ca$se 'al$e contained in the hando'e& &ecognisedmessage ill affect the e'al$ation %&ocess in the @.

;ando'e& ca$ses ma* #e %&io&itized as follos

W . H%lin! $alit*

W 2. H%lin! Bnte&fe&enceW 3. Conlin! $alit*

W 4. Conlin! Bnte&fe&ence

W ". H%lin! Le'el

W >. Conlin! Le'el

W . Cistance

W ?. 8oe& $dget


8oe& #$dget hando'e&


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W Bf an M on a allocated &eso$&ce d$&ing its meas$&ement&e%o&ting %&ocess sees anothe& channel that o$ld %&o'ide ane$al o& #ette& $alit* &adio lin! &e$i&ing a loe& o$t%$t%oe& then a hando'e& ma* #e initiated.

W ;ando'e&s d$e to %oe& #$dget ens$&e that the M is ala*slin!ed to the cell ith minim$m %athloss tho$gh the $alit*

and le'el th&esholds ma* not #e e7ceeded.W ;ando'e& to the ta&get cell ta!es %lace hen 8

hoMa&gin8

W 8 = (ms78&Ma7 A'S-7le'SCLS; (#ts78&Ma7

SN8:-)) (ms78&Ma7(n) A'S-7le'SJ@6LL(n))he&e n RnthR adacent cell hich is a hando'e& candidate


8oe& #$dget hando'e&


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W hoMa&gin8 is a %a&amete& hich can #e set on a cell tocell #asis. 6ach cell ma* ha'e a diffe&ent 'al$e fo& eachneigh#o$& cell hich is a candidate fo& %oe& #$dgethando'e&.

W hoMa&gin is e7%&essed in d and is $s$all* set to 4. ;oe'e&this ma* #e &ed$ced if the hando'e& needs to #e s%eeded o&

inc&eased to > o& highe& to %&e'ent %ing/%ong o& to dela*hando'e&s

W Bn some cases negati'e homa&gin ma* also #e $sed.


;ando'e& Algo&ithms

d l h d dd d f l


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W ;ando'e& algo&ithms a&e $sed in addition to defa$lt%a&amete&s to cont&ol the hando'e& %&ocess

W hese algo&ithms assist in mo#ilit* management and a&eeffecti'e in t&affic dist&i#$tion.

W he algo&ithms ha'e an im%o&tant &ole to %la* in Mneto&!s hich $se m$lti/#and o& m$lti/la*e& a&chitect$&es.

;ando'e& Algo&ithms

M l h d h #



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W Bn Moto&ola s*stem the&e a&e %&oced$&es. hese a&e set #*the %a&amete& %#gtSalgSt*%e. he algo&ithms a&e #&iefl*defined as follosD/

W *%e @on'entional M 8

W *%e 2 -est&icted 8 fo& mac&o cells

W *%e 3 8 ith -7le' as $alifie&W *%e 4 8 ith time in cell as $alifie&

W *%e " 8 ith dela* since neigh#o$& le'el e7ceedsth&eshold as $alifie&

W *%e > Cela*ed %oe& #$dget $sing d*namic hando'e&ma&gin

W *%e 8 algo&ithm to a'oid adacent channelinte&fe&ence

;ando'e& Algo&ithms

f h h l d 2 3 d



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W f the se'en, the most commonl* $sed a&e *%e, 2, 3 and .

W 6ach hando'e& candidate cell can #e defined as one of these'en t*%es of neigh#o$& to the so$&ce cell.

;ando'e& %e& ca$se

W he hando'e& %e& ca$se statistic is a co$nte&/a&&a* statistic

hich co$nts the &eason fo& each hando'e& e'ent on all cellsfo& hich it is ena#led.

W his statistic gi'es im%o&tant info&mation a#o$t the hando'e&%e&fo&mance of the cells and can #e $sed fo& t&o$#leshootingcells hich ha'e high Qhando'e& fail$&e &ateR.

;ando'e& %e& neigh#o$&

hi t ti ti i th l f f h d tt t



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W his statistic gi'es the 'al$e of no of hando'e& attem%ts asell as s$ccesses fo& each neigh#o$& cell. his statistic isalso hel%f$l in t&o$#leshooting hando'e& %e&fo&mance, it can#e $sed to identif* neigh#o$& &elations hich ha'e a highQhando'e& fail$&e &ateR

W he hando'e& %e& neigh#o$& statistic can also #e $sed fo&

neigh#o$&list %&$ning.



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Database Optimization – TRHO/CongestionDatabase Optimization – TRHO/CongestionRelated ParametersRelated Parameters

-; :hat does it do


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W -; effecti'el* &ed$ces the se&'ice a&ea of the congestedcells

W Bnc&eases se&'ice a&ea of $nde&/$tilised ta&get cells

W ; is t&igge&ed $sing a s%ecial %a&amete&Qamh&ho8#gtMa&ginR instead of hoMa&gin8#gt

W ene&al g$idelineDW a&get cell Q-7le'accessminR sho$ld #e set highe& to

a'oid #ad donlin! -7$al afte& ;

W amh&ho8#gtMa&gin m$st #e loe& than hoMa&gin8#gt


-;1@ 8a&amete&s


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W amhH%%e&loadth&eshold his %a&amete& dete&mines

minim$m t&affic load th&eshold at hich cell sta&ts to intiate-; defa$lt 'al$e ?0 F

W amhMa7Loadfa&get@ell his %a&amete& dete&minesma7im$m t&affic load th&eshold #e*ond hich ta&get cell illnot acce%t -; hand/ins defa$lt 'al$e >0 F

-;1 8a&amete&s

W amh&ho8#gtMa&gin his %a&amete& is ne 8#gt ma&ginhen cell e7ceeds amhH%%e&loadth&esh. BtPs the &e'ised%oe& #$dget ma&gin hich &e%laces the no&mal 8#gt

definition hen the &ho c&ite&ia a&e met defa$lt 'al$e is "d.


-;1Adacenc* 8a&amete&s


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W t&hoa&getLe'el his %a&amete& dete&mines the minim$m

-B of the 'alid ta&get cell candidate &e%o&ted #* themo#ile defa$lt is ?" dm

Ci&ected -et&*



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W A t&ansition (hando'e&) f&om C@@; in one cell to a @; in

anothe& cell d$&ing call set$% d$e to $na'aila#ilit* of an em%t*@; ithin the fi&st cell.

W o cont&ol t&affic dist&i#$tion #eteen cells to a'oid a call&eection.

W @an #e $sed fo& #oth M@ and M@W etting g$idelinesD

W d&h&eshold sho$ld #e highe& than -7le'mincell(-7le'accessmin)X else the im%&o'ed ta&get cell selectionc&ite&ia ill #e igno&ed.

@ongestion -elief



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W his %&oced$&e is initiated hen an M is assigned to an

C@@;, &e$i&es a @; and none a&e a'aila#le.W o o%tions a&e offe&ed fo& deciding ho man* hando'e&

%&oced$&es a&e act

Sudarshan_ RF Optim

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