air interface dimension ing tec

8/6/2019 Air Interface Dimension Ing Tec

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Air Interface Dimensioning Techniques for Coverage

and Capacity-Limited HSPA Based Networks

Anis Masmoudi 1,21High Institute of Electronic and Communication (ISECS),

University of Sfax,

Sfax, Tunisia

[email protected]

Sami Tabbane 22 Mediatron Lab.,

SupCom School, University of Carthage,

Tunis, Tunisia

[email protected]

AbstractDimensioning and planning air interface of the

evolution of UMTS networks based on HSPA technique is

interesting for mobile operators. This paper deals with coverage-

limited and capacity-limited dimensioning of HSPA based

Beyond 3G networks with the necessary analytical support. The

models established for coverage-limited case are exploited to

generate abacuses useful mainly for initial dimensioning. Thecapacity-limited dimensioning case is also studied through

different methods based on scheduling techniques. We suggest

enhancing Fair Throughput scheduling technique to improve

coverage and capacity dimensioning performance. Finally, we

assess performance of the different introduced methods for

HSPA dimensioning by comparing them to basic UMTS (Release

99) dimensioning.

Keywords-HSPA; air interface dimensioning; coverage-limited;

capacity-limited; scheduling techniques; network performance;

planning abacuses

I. INTRODUCTIONThe fast challenge of mobile radio networks pushes

operators to adapt their planning and engineering procedures

to the new technologies and multimedia services. Radio

interface planning and dimensioning is especially crucial since it

remains a bottleneck for the whole process of network

deployment. In this paper we investigate planning and

dimensioning methods for HSPA (High Speed Packet Access)

[1][7] Based Beyond 3G (B3G) mobile networks known as3.5G. This includes both coverage and capacity-limited cases.

Dimensioning air interface consists in determining the number

of radio sites to deploy through the calculation of cell size and

capacity. Dimensioning performance is measured and

evaluated in terms of capacity enhancement and sitesminimization or cell size maximization. Our approach is

original since most of the literature about HSPA deals only

with its performance through different scheduling techniques

[7] but with neither consistent analytical study nor practical

engineering rules for this evolution of 3G networks. In this

paper, first we give a new definition of air interface coverage

in HSPA based networks with the adequate proof and

analytical support. The last established mathematical

formulations are used to plot some figures as abacuses that

help to dimension coverage-limited HSPA networks. To

complete our dimensioning study, we present the capacity-

limited case. In fact Fair Resource and Fair Throughput

scheduling techniques are distinguished as case studies for

capacity-limited dimensioning. We show also an introduced

dimensioning principle based on an Enhanced Fair

Throughput scheduling technique that we suggest in order toimprove capacity and coverage performance so as to obtain a

more efficient dimensioning. Those scheduling based methods

are mathematically modelled. Finally, the described

dimensioning techniques introduced for HSPA evolution are

evaluated by making a comparison between them and other

methods used for the basic downlink Release 99 UMTS (with

activated and deactivated power control, and Fair Power

Partitioning: FPP). This comparison is accomplished through

simulation and the assessment is performed in terms of the

spectral efficiency and the allowed coverage.

II. RADIO COVERAGE REFORMULATION IN HSPABASEDSYSTEMS

The coverage concept in HSPA based UMTS (Universal

Mobile Telecommunication System) networks is defined as the

fact that the signal received by mobile guarantees a minimum

required received SINR (Signal to Interference and Noise

Ratio) or power level threshold at a given coverage

probability as on the expressions (1)(4).

The area coverage probability Fu is written as follows

[8][9]:

+=

b

baerf

b

baaerfFu

.11.

..21exp)(1

2

12

where

2.

0 rPxa

= and2.

log..10 10 enb = ,

: Standard-deviation of the shadowing effect (in dB)x0: Mean threshold of the power sensitivity

Pr: Average level of the power on the cell border

n: Propagation coefficient

e: Exponential constant

The differencex0 Prrefers to the shadowing margin

(1)

(2)

2010 17th International Conference on Telecommunications

978-1-4244-5247-7/09/$26.00 2009 IEEE 196


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erf: error function defined by :

=x

t dtexerf0

2

2)(

The coverage probability Cu on the cell edge (border) is

given by:

[ ])(12

1aerfCu =

The previous definition is equivalent to the fact that a minimal

given bit rate is guaranteed (at the same probability). That has

the same significance as guaranteeing a given quality indicator

parameter (called in HSPA as Channel Quality Indicator or

simply CQI [1][5], [7]).

In order to validate this last new definition of radio

coverage (in HSPA), we calculate, for a given shadowing

standard-deviation and for different values of the distance to

node B (25 m to 2 Km with a step of 25 m), the probability

that CQI is above the CQI threshold value CQI0 referring to a

given service bit rate (assuming the correspondence table of

the standard [10] between CQI and the Transport Block SizeTBS, and that instantaneous bit rate depends on the TBS

through the Transmit Time Interval TTI denoted as TTIdelay

whose value is specified by the standard: Eg. TTI is equal to 2

ms in HSDPA: High Speed Downlink Packet Access). This

calculation is repeated for four shadowing standard-deviation

values (6 dB, 8 dB, 10 dB and 12 dB) and four services at

different required nominal bit rates (64 Kb/s, 128 Kb/s, 384

Kb/s and 2 Mb/s). The calculation of the probability is

accomplished according to the following theoretical discrete

distribution model valid in the DL of HSPA systems:

===

+

2

)(

2

)(

2

1)(Pr

1k

k

k

dLnerf

dLnerfkCQIobp

where

=

1010

)(

10 101010intraratioTXinter IOffsetkCQIPI

kd if

+

Offset

CQI

IPk

ratio

intraTX0 (Iinter


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independent of the shadowing standard-deviation value

(corresponds to the average power received by the node B

which is the same as the one obtained with a deterministic

propagation model).

Figure 1. Coverage probability versus distance to node B for different services

(having different nominal bit rates) and shadowing values

III. ABACUSES FIGURES FORCOVERAGE-LIMITEDDIMENSIONING

In this paragraph, we show examples of abacuses figures

generated on the basis of the mathematical study achieved in

the previous paragraph. So, those abacuses are valid for the

coverage-limited case. The capacity-limited case is carried out

in the next two paragraphs.

Fig. 2 & 3 provide examples of abacuses respectively for

categories 10 (having the highest offered bit rate) and 1 (withthe least provided bit rate) of mobile terminals [5] allowing

to dimension a HSPA network by determining the maximum

cell radius versus minimum required bit rate (guaranteed at a

given cell coverage probability).

For such abacuses, the specification of bit rate and

required coverage probability is enough to find the maximum

allowed cell radius by the help of both figures (Fig. 2 shows

the complete range of bit rates: until 5.7 Mb/s, and Fig. 3

extracts the part of abacuses whose bit rate doesnt exceed

1 Mb/s for a better view).

The discrete aspect of the abacuses translates the effect ofAdaptation in Modulation and Coding affecting with adiscrete manner the bit rate supported by the link (limited

numbers of CQIs thus well determined Transport Block

sizes).

Note that at a given fixed coverage probability, themaximum radius doesnt exceed the maximum value

corresponding to CQI = 1 (peripheral border of the cell

offering the minimum offered bit rate).

The higher the required coverage probability is, thesmaller the maximum cell radius (Dimensioning with

stricter conditions).

Figure 2. Abacus of the cell size versus offered bit rate (High capability

terminals) for different coverage probability values

Figure 3. Abacus of the cell size versus offered bit rate (Low capability

terminals) for different coverage probability values

Fig. 4 is similar to Fig. 2 except by taking the shadowing

standard-deviation as parameter instead of the required area

coverage probability. It shows dimensioning abacuses of

coverage limited bit rate (bit rate guaranteed in 95% of the

cell). Note in particular that the dimensioned radius is lower

for higher shadowing standard-deviation values. This is

effectively logical since the shadowing margin to include in

the link budget increases with the standard-deviation.

Fig. 5 summarizes entirely the abacuses of the threefigures, Fig. 2 to 4, while combining both the following

parameters: coverage probability and shadowing standard-

deviation. The same remarks can be extracted with a global

vision of the impact of both parameters together (shadowing

standard-deviation and area coverage probability). In

particular, the smaller the coverage probability (case of 70%),

the less the impact of shadowing standard-deviation is

important on the dimensioned cell size (due to the impact of

coverage probability on the shadowing margin). The abacuses

of Fig. 6 allow to dimension the cell radius (coverage limited)

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versus required coverage probability while knowing the

required bit rate and the shadowing standard-deviation.

It is evident that the higher the required bit rate, thesmaller the dimensioned cell radius (because the CQI at

the edge of the cell is important).

The impact of the shadowing standard-deviation on thedimensioned cell size diminishes with the increase of the

maximum guaranteed service bit rate. In other words,shadowing effect is more important with a lower bit rate

service. This is due to the fact that the required CIR of a

low bit rate service is below that of a higher bit rate, thus

more sensitive to propagation channel variations due to

shadowing.


terminals) for different shadowing standard-deviation values


terminals) for different coverage probability and shadowing standard-

deviation values

The more the coverage probability increases and

approaches to 100%, the more the dimensioned radius

decreases asymptotically (near the coverage probability of

100%). This is due to the shadowing effect requiring an

infinite margin to reach a coverage probability of 100% (not

reached in practice).

Figure 6. Abacus of the cell size versus the area coverage probability for

different services (different nominal bit rates) and different shadowing

standard-deviation values

IV. SCHEDULING METHODS BASED DIMENSIONING(CAPACITY-LIMITED)

After dealing with the coverage-limited case, we present in

this section the capacity-limited dimensioning. Since the

scheduling is the main bottleneck for this case study, our

dimensioning methods are based on different scheduling

techniques. The last sub-section is a suggestion of a new

introduced scheduling method that can enhance dimensioning

performance of HSPA based UMTS networks.

A. Fair Resource scheduling based dimensioningHSPA capacity is limited either by the number of codes

HS-PSCH (High Speed Physical Shared Channel) [1][5] or

by the total node B power. The cell size in the first case (Rc) is

the greatest radius R verifying the equality in the following

condition of HSPA code limitation:

15)()(

21

21

221 00

0

+ ++

+ kkki

iii rRnrrn

where ri = ri,min and ri+1 = ri+1,min = ri,max, ri and ri+1 denote

respectively the lower and upper limits of the range of the

sub-cell (ring) having a CQI equal to CQIi (same modulation,

coding rate and number of physical shared channels or codes

ni used thus having the same corresponding block size TBSi

according to the adequate table of [10]), and such that

10 +kCQI is the CQI of the ring including the border of the cell

having the size R. The number 15 in the numerator of the

second term of (7) refers to the number of the codes

(physical shared channels) allocated to HSPA (the value of 15

is taken here as an example of the maximum number allocated

for HSDPA until the end of this paper). In the second case

(node B power limited capacity), the sizeRp of the cell is such

that:

(7)

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10102 1010TotTX PP

pR =

where PTot is the total power of the node B. The capacity

limited cell radius is: ),(min pccap RRR = .

By assuming the dimensioning and the bit rate limited only

by coverage and link quality but not by capacity, the cell

radius R will depend on minimum bit rate Rmin of theconcerned service (by taking always the assumption of the

Fair Resource as the used scheduling technique). The bit

rateRmin refers to the Transport Block Size TBS0 as follows:

TBS0 =i

min { TBSi / TBSi tables [10]

and TBSiRminTTIdelay }

In this case, and by accomplishing a dimensioning without

codes multiplexing, the cell radius R will be the distance r0

referring to TBS0 or exactly the minimum radius of the

internal CQI ring (core) corresponding to the maximum CQI

that radio condition and terminal capability allow. In fact,

TBS0 refers to a CQI0 (the maximum allowed value) [10] to

which refers a minimum SINR value (SINRmin) computed as

follows:

)( 0min OffsetCQICQISINR ratio =

From the last value, we can extract the maximum

attenuation from SINR definition expression, and thus the

corresponding radius r0. If we assume the total power

transmitted by the node B constant (not depending on the

number of mobiles served), then the computation of the cell

radius can be made immediately. Yet in reality, the power

transmitted depends on the traffic, and is proportional to the

number of mobiles served in the cell (since individual transmit

power is constant), hence the transmitted intracellular power is

proportional to the number of active mobiles of the cell, so it

depends on the cell radius. Therefore, for more accuracy and

precision, we must apply an iterative algorithm until its

convergence to the cell radius. The last parameter shouldnt

exceed, in any case and whatever the service and its required

bit rate, the value of the radius referring to the minimum

allowed value of CQI (equal to 1).

In contrast, by assuming the dimensioning is capacity-

limited (either by number of codes allocated to HSPA if

15)(2

0 0>

R

drrrnd where n(r) is the number of codes

referring to the CQI of a virtual mobile at a distance rto thenode B or by the total available power of the node B if

10102 1010TotTX PP

pR > ), or in other words assuming traffic

density above some value, then the bit rateRu guaranteed per

user (assuming always a uniform traffic and the use of the

Fair Resource scheduling technique) can be written as

follows:

2

2

idelay

capiu

rTTI

RTBSR

=

where TBSi is the Transport Block Size referring to the ring i

of the CQI = i (at the border of the cell) having an outer radius

ri+1 >Rcap (capacity limited cell size) in case of one service. In

this last case, in order to guarantee a minimum bit rate Rmin at

the border of the cell, the Transport Block Size TBS0 at the

cell border should be given by:

TBS0 =i

min { TBSi / TBSi tables [10]

and TBSiRmin 2cap

delay

R

TTIri+1 }

In this case, the cell radius can be concluded from TBS0 as

for the previous case of coverage limited dimensioning; yet

the expression (12) above ofTBS0 depends on the cell radius

(through ri+1), then the planner should apply an iterative

process or by dichotomy to converge to the exact cell radius

or extract it from some mathematical formula referring exactly

to the required bit rate Rmin. Thus, if2

12min1 ++< i

cap

i rR

TTIRTBS

(where i is such that TBSi = TBS0), then the dimensioned cellsize Rdim is equal to the limit size of the CQI ring with TBS0

(i.e. ri+1), elsemin

1dim

RTTI

TBSRR icap

= + (ri+1Rdim < ri+2).

B. Fair Throughput scheduling based dimensioning

This paragraph provides expressions of maximum bit rate

per user ensured by Fair Throughput scheduling technique

with and without consideration of codes multiplexing. Thus

we can determine Fair Throughput dimensioning procedure

with its analytical support.

Assuming TTIdelay the Transmit Time Interval duration,and TBS1, TBS2, TBS3,, TBSi, are the respective

Transport Block Sizes of each of the users in the cell

according to their CQIs. Thus the maximum ensured bit rate

by each of the users (without codes multiplexing) can be

written as follows:

=

j jdelay

ens

TBSTTI

R1

1

We can easily check that iTTI

TBSR

delay

iens ; . In

particular, Rens is always below or equal to the most limitingcoverage bit rate at the cell border.

The bit rate in expression (13) is thus the minimum

guaranteed bit rate independently of the number of codes and

multi-codes available in HSPA (It refers to the minimum

required number of codes always below the number of

available HSPA shared codes assumed to be equal to 15).

Otherwise, we can ensure a user bit rate (in Fair

Throughput) above that given by (13) by using more OVSF

(8)

(9)

(10)

(11)

(12)

(13)

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(Orthogonal Variable Spreading Factor) HSPA codes (with

codes multiplexing).

By considering codes multiplexing, we establish that the

maximum ensured bit rate per user can be written as follows:

( )

=

ii

idelay

FTens

TBS

nTTI

R15

where ni is the corresponding number of codes referring to the

useri position within the cell (given by 3GPP standard: Third

Generation Partnership Project[8]).

In the uniform traffic case study, (14) becomes by

reasoning on the different CQI rings of one cell with a

uniform area density as follows:

( )

=

=

+i

iii

idelay

R

delay

FTens

rrTBS

nTTI

drrrTBS

rnTTI

R

)(

15

)(

)(2

15

221

0

assuming that ri and ri+1 are the radii of the CQI ring borders

(ri+1 = ri+1,min = ri,max). Hence, if minimum bit rate value Rmin to

guarantee for the service is known, the maximum allowed area

density of users max can be determined as follows:

=

=

+

i

ii

i

idelay

R

delay

rrTBS

nRTTI

drrrTBS

rnRTTI

)(

15

)(

)(2

15

221min

0min

max

The planner should therefore determine the CQI ring m

referring to bit rate Rmin. So, the dimensioned cell size Rdim

guaranteeing a minimum bit rate Rmin can be extracted from

(15) as follows:

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