dl capacity mgmt-wcdma

7/31/2019 DL Capacity Mgmt-WCDMA

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Comparison of Models

for WCDMA Downlink Capacity AssessmentBased on a MORANS Reference Scenario1

Andreas Eisenblatter2∗† , Hans-Florian Geerdes2†, Antonella Munna‡, Roberto Verdone‡

∗atesio GmbH, Berlin, Germany; [email protected]†Zuse Institute Berlin (ZIB), Germany; {eisenblaetter,geerdes}@zib.de

‡IEIIT-BO/CNR, DEIS, University of Bologna, Italy; {amunna,rverdone}@deis.unibo.it

Abstract— Third generation wireless telecommunication net-works based on WCDMA technology are being deployed acrossthe world. Since the downlink is likely to be the limiting direction,it is crucial for network engineers to assess the downlink capacityof WCDMA radio cells. In this paper, we revisit a semi-analyticalcapacity evaluation model involving snapshot simulation. We fur-ther develop an alternative approach for assessing cell capacity,which is a generalization of recent analytical dimension reductiontechniques for cell load computation. The second approach worksunder average load rather than snapshots, which enables aquick approximation of the simulation results. We investigatethe relationship between the two approaches. We demonstratehow the MORANS (MObile Radio Access Reference Scenarios)reference datasets can be used to compare different approacheson a common basis. Based on a MORANS real-world scenario,we compare the capacity of different cells under varying softhandover parameters. The results show how cells’ capacitiesvary under realistic data. As the approximative method is quiteaccurate, we can conclude that no snapshot simulation is neededfor capacity analysis in our setting.

I. INTRODUCTION

Radio networks based on WCDMA technology are currently

being deployed by telecommunication operators across the

world. For dimensioning these radio networks, a capacity

estimation is crucial. In WCDMA, all signals are transmitted

on the same frequency band, so interference is inevitable,

and radio networks are typically interference-limited. Capacity

analysis is more involved than for traditional radio systems

since the amount of interference depends strongly on themobile’s location. WCDMA technology allows for data rates

that are much higher than with traditional radio technology.

These data rates will support services that are especially

demanding in the downlink direction. The downlink capacity

is thus expected to become the bottleneck. We therefore focus

on analyzing the downlink capacity of radio cells.

Soft handover (SHO), the capability of a mobile device to

be connected to several base stations (BSs) at a time, is a

novel feature of WCDMA radio technology. This mechanism

can be applied if a mobile device receives several radio signals

from different antennas at a comparable strength. The set of

1This work is a product of the authors’ participation in C OS T 273 and theMORANS initiative (http://www.cost273.org/morans ).

2Supported by the DFG Research Center M ATHEON ”Mathematics for key

technologies” in Berlin, Germany.

BSs that the mobile is connected to is called its active set

(AS). Several radio resource management parameters play arole here, most noteably the maximum allowed active set size

and the AS window, the range in which the signal strengths

received from the BSs in the active set may vary. If no SHO

is used, the mobile is normally connected to the base station it

receives the strongest signal from, this is called site selection

diversity transmission (SSDT). We compare the capacities for

SSDT and SHO mode and also investigate the effect of the

SHO parameters on a cell’s capacity.

Monte-Carlo simulation using random realizations of static

user distributions (snapshots) is a well-known approach for

this kind of analysis. While it is generally accepted as a fairly

accurate means for capacity prediction, it is computationally

expensive since experiments have to be repeated until theoutcome is stochastically reliable. We will present a method

that uses snapshot simulation along with an analytical one that

does not require simulation and compare capacity results from

both models. The comparison will be done based on a real-

world scenario, which is a result achieved by the M ORANS

initiative within COST273 [1].

In the remainder of this section we introduce our system

model and the soft handover scheme we use, in II our

definition of capacity and the two models for capacity are

presented and compared. We provide computational results for

both models in III and draw conclusions in IV.

A. System Model

The scenario contains I cells. The capacity as defined below

is evaluated for several cells. We pick only cells in the center

of the scenario to avoid border effects. For ease of notation, the

cell in question is always denoted with the index 1. Each cell

transmits at maximum power pmax; a portion ρ < 1 of this

power is allocated to the traffic on dedicated channels. The

rest is for broadcast and shared channels. Shadow fading is

neglected, fast fading is assumed to be averaged out by perfect

fast power control due to its short correlation length. This

implies that the Carrier-to-Interference-plus-Noise-Ratio (CIR)

perceived at mobile m (MSm) for the signal on the link to cell1

is assumed to exactly meet a specific threshold value denoted

by µm. The value µm depends on the service and on the soft

handover state (see below). The attenuation between celli and



MSm is denoted by γ im. Furthermore, for each mobile MSm,

an orthogonality factor ωm, a noise power ν m at its receiver,

and a (service-specific) transmission activity factor αm is to

be considered. We write m ∈ celli if mobile m is connectedto celli.

B. Soft Handover

We sketch the SHO scheme used in this paper. For more

details the reader is referred to [2]. We denote by nmax the

maximum number of cells that can be in the AS and by nthe number of currently active cells. SSDT corresponds to

nmax = 1. Users connected to cell1 are divided into classes

[3] based on the received power and the AS window ∆:

Class A. Users only connected to cell1 (n = 1); denoted

M A. (In SSDT there are only Class A users.)

Class B. Users with cell1 as best server and active set size

n > 1; denoted M B.

Class C. Users in soft handover with cell1 in their active

set, but not as best server; denoted M C.

Part of the input data for each mobile is a CIR target value.

This value relates to the mobile being connected to only one

cell without SHO, we denote it by µ(NHO)m for now. If mobile

MSm is in soft handover (n > 1), its CIR target reduces

due to micro-diversity effects provided by Maximum Ratio

Combining to a value µ(SHO)m < µ

(NHO)m . We account for this

by using a diversity factor θ < 1:

µ(SHO)m = θ · µ(NHO)

m

We further assume (cf. [4], [5]) that each link to a cell in the

AS provides the same contribution µ(LNK)m to µ

(SHO)m , that is,

µ(SHO)m = n · µ

(LNK)m . The CIR target for (perfect) fast power

control of cell1 for Class B and C users is thus

µm = µ(LNK)m = µ(NHO)

m · θ/n. (1)

For Class A users, we simply have µm = µ(NHO)m .

II . ASSESSING DOWNLINK CAPACITY

A. Capacity Definition

According to the classification of users introduced in I-B,

we define the capacity of cell1 for a snapshot s as:

M ∗s = M A + M B

By this definition, each served user is counted exactly once.

The average capacity is a mean over S uncorrelated snapshots:

M ∗ = (1/S ) ·S

s=1 M ∗s (2)

B. Model A

We briefly recall the semi-analytical model from [3]. Let

Φ1m denote the portion of total transmission power at cell1 de-

voted to MSm. The following system constraint must hold [4]:

1 ≥

m∈cell1αmΦ1m (3)

The CIR received by MSm is

µm =ρΦ1mγ 1m pmax

(1 − ρΦ1m) ωmγ 1m pmax +I

i=2 γ im pmax + ν . (4)

By transformation, we obtain a closed expression for Φ1m:

Φ1m =

ωm +I

i=2

γ im

γ 1m+

ν

γ 1m pmax

ρ (ωm + 1/µm)(5)

When simulating a snapshot, we evaluate the cell’s capacity

defined in (II-A) by adding users to cell1 as long as the

fundamental inequality (3) holds.

C. Model B

Under the assumptions for Model A, the vector ¯ p of average

transmit powers satisfies a linear equation system involving an

I × I -dimensional coupling matrix C (cf. [6], [7], [8]):

¯ p = C p + ¯ p(η) + ¯ p(fix) , (6)

whereC ii :=

m∈celli

ωm lm , C ij :=

m∈celli

γjmγim

lm ,

¯ p(η)i :=

m∈celli

νmγim

lm , lm := αmµm1+ωmαmµm

,(7)

and ¯ p(fix)i is the power emitted on broadcast channels by cell i.

Assuming ¯ pi = pmax ∀ i, we derive an alternative version of

system constraint (3) by considering only the first cell in (6):

pmax ≥ C 11 pmax +

j>1 C 1j pmax + ¯ p(η)1 + ¯ p

(fix)1 (8)

In a Monte-Carlo scheme similar to Section II-B, we can

evaluate the capacity of the system by adding mobiles to

cell cell1, thereby increasing the values C 11, C 1j , and ¯ p(η)1 ,

until the right-hand side of (8) exceeds pmax for the first time.The linear equation system (6) can be formed for average

user load instead of users in a snapshot (cf. [8]). Let S denote

the set of services and T s the spatial average user density

function for each s ∈ S . A CIR target µs and user activity αs

are defined per service. The “average” coupling matrix C is

obtained by integrating1 over all points p in the cell’s area

and weighting the elements with user density and service load

functions:

C 11 :=

p∈cell1

ω p ls( p)dp , C 1j :=

p∈cell1

γ jpγ 1 p

ls( p) ,

¯ p(η)1 := p∈cell1

ν p

γ 1 pls( p) , ls( p) :=

s∈S

T s( p)αsµs

1 + ω pαsµs

,

The continuous equivalent of adding users to the cell is to

simultaneously increase the average user load in the cell until

the equivalent of (8) is met with equality. For this purpose, we

use a traffic scaling factor λ. The average maximum amount

of traffic admissible for cell1 is calculated from

pmax = λ

pmax

j C 1j + ¯ p

(η)1

+ ¯ p

(fix)1 . (9)

The value of the resulting scaling factor λ corresponds to the

fraction of “average users” in classes A and B that can be

served by cell1. The total number of average users in cell1 is

¯N := p∈cell1 s∈S T s( p) .

1Usually, data is provided in pixel format. The integrals in the notation

actually become sums over pixels.



Accordingly, the average number of users served is M ∗ :=λ·N . Note that it is particularly easy to calculate as no Monte-

Carlo simulation is involved.

D. Comparison of the Models

The fundamental difference between Model A and the

snapshot version of Model B (8) is the notion of intra-cell in-

terference from power-controlled channels for CIR calculation.

A sketch of the situation can be found in Fig.1. In Model A,

the amount of intra-cell interference is always calculated on

the basis of the fixed output power pmax. The power spent

by the base station on the link for which the CIR is to be

calculated is subtracted from pmax. The result is the intra-cell

interference. The overall signal strengths considered for CIR

calculation thus sum up exactly to pmax. In Model B, on the

other hand, intra-cell interference is calculated as the sum of the powers for all other active radio links, weighed with the

respective activity factor αm. As can be seen in the figure,

this leads to a higher intra-cell interference. The difference is,

however, less pronounced in practically relevant settings since

the “granularity” of users is much higher.

1/3

2/3

1

CIR calculationAverage output power

Φ1m

signal

interference

Φ11

Pm αmΦ1m

= (1 − α1Φ1)

Model B

Pm=1

αmΦ1m

Model A

(1 − Φ1)

intra-cell

Fig. 1. Schematic comparison of evaluation models. Three users with a

relative power consumption of 2/3 and an activity factor of 0.5 are assumed

More interference is assumed in Model B, so the capacity

evaluation is more pessimistic. On the other hand, by using the

scaling factor λ in the average-based version (9) of Model B,

we assume that the fundamental system constraint (3) isalways met with equality (which is not the case in Monte-

Carlo simulations as there is a certain granularity of users).

This pushes Model B’s results into a more optimistic direction

again. It is, however, not completely clear how the averaging

over the cell’s area influences the results of Model B compared

to Monte-Carlo simulation. Our computational experiments

below indicate that the approximation is quite accurate.

In the remainder of this section we will give the analytical

analysis corresponding to the preceding discussion. When

calculating the CIR for a link to mobile m in Model A, the

power ρ(1−Φ1m) pmax not spent on the link is accounted as

intra-cell interference and appears—with due consideration of

attenuation and orthogonality—in the denominator of (4).

In Model B, on the other hand, intra-cell interference is

calculated as the sum of all third-party link powers weighed

with the respective activity factor. The average output power

is fixed at pmax. In the deduction of the linear equation

system (6) (cf. [9]) the intra-cell interference for a single

mobile m is calculated as

¯ p(fix)i +

n∈celli, n=m αn pin , (10)

where pin denotes the power spent by base station i on the

link to mobile n. So for calculating the CIR for a transmission

towards mobile m, all remaining dedicated links are assumed

to interfere with their average transmission power. Using the

notation of Model A, that is, ρ := 1− ¯ p(fix)/pmax and Φ1n := pin/(ρ · pmax), the intra-cell interference term (10) becomes

ρ

n∈cell1n=m

αnΦ1n pmax = ρ(1 − αmΦ1m) pmax .

Hence, for analysis of the transmission to mobile m the cell’s

total output power (link power plus intra-cell interference) is

assumed to be higher than the average power, namely

ρ

>1 (

n∈cell1n=m

αnΦ1n + Φ1m) pmax .

This concept is inspired by the fact that for practical network

planning, the average transmission power is usually limited

to a certain fraction (for example 70 %) of the equipment’s

technical maximum transmit power. The headroom is used for

equalizing fading in fast power control and to allow for agraceful degradation of service in a congestion situation.

The consequences of this different concept can be best

observed when transforming (8) into a version similar to (3):

1 −¯ p

(fix)1

pmax≥

m∈cell1

αm

ωm +

j>1

γjmγ1m

+ νm pmaxγ1m

αmωm + 1/µm

Since 1−¯ p(fix)1 /pmax = ρ, the transformed version of (8) reads

1 ≥

m∈cell1

αm

ωm +

j>1

γjmγ1m

+ νm pmaxγ1m

ρ(αmωm + 1/µm)

. (11)

This is almost the same as (3), except for the activity factor

αm that appears in the denominator. However, since µ is

usually in the range of -15 to -10 dB, the term 1/µm clearly

dominates the denominator of (11) and (3), the difference is

thus negligible for practical purposes.

III . COMPUTATIONAL RESULTS

A. The MORANS Turin Scenario

Our computational results are based on the realistic Turin

scenario developed within the COS T 273 MORANS activity [1].

The MORANS (MObile Radio Access Network reference

Scenarios) initiative is undertaken within the Radio Network

Aspects Working Group (WG3) of the COS T 273 Action. Its

goal is to increase comparability among results of different ap-

proaches to evaluate radio network planning and radio resource



TABLE I

SERVICE INFORMATION

Service DL bit rate DL Activity µm User Mix

[Kbps] Factor [dB]Voice 12.2 0.500 -17.48 38.1 %Video Call 64.0 1.000 -14.66 8.1 %Data 32.0 1.000 -11.43 2.6 %WWWa 64.0 0.774 -15.08 50.1 %

aA packet call data source model is assumed for this service. The activityfactor reflects pauses between single packets within a packet call. In addition,

users have a 10 % probability of being in a packet call at any given time. Theremaining 90 % of the users in a snapshot are assumed to be in reading time.Their connection is idle. They consume no radio resources.

management strategies. To this end, reference scenarios for

radio network evaluation and planning are provided. Besides

simple, synthetic scenarios, two real-world-based scenarios(Turin and Vienna) are available. Their definition and use is

more involved than in the synthetic case, but they enable tests

of radio network algorithms under more realistic conditions.

This is the first publication that realizes the MORANS

initiative’s goal of comparing results obtained with different

approaches. We have used the Turin scenario. The scenario

includes an area of 17.85 × 15.35 km2. Geographic data

includes a digital elevation model and vector files describing

railways and motorways. Path loss predictions based on the

COS T 231-Hata model are used in the current version which

do not use this information, so we consider a flat scenario.

Traffic characterization, in terms of service information (see

Table I) and usage on 4 different services in both uplink and

downlink are given. Link level simulation tables and target

block error rates have been used to calculate CIR targets. The

user distribution is not homogeneous, it is sketched for the

service voice in Fig.2(a); the distributions of users of other

services are equivalent but scaled according to the service

mix. For the results of our Monte-Carlo simulation (Model A),

800 independent user snapshots have been used, an example

snapshot is shown in Fig.2(a).

We evaluate a reference radio access network included in

the MORANS Turin scenario. A total of 34 sites are deployed,

32 of which are composed of 3 cells and 2 of 4 cells, according

to Fig. 2. In addition, base station configuration parameters, asthe antenna type, mechanical and electrical tilt, azimuth, height

are given, together with the horizontal and vertical radiation

pattern. The transmit powers of BSs are pmax = 10 W; a

fraction of ρ = 0.8 is allocated to traffic channels. The SHO

diversity factor (cf. I-B) is θ = 0.71.

B. Capacity Analysis for Selected Cells

After evaluating all cells in the scenario, we have picked

four cells with results of different characteristics for discus-

sion. Their locations are indicated in Fig. 2(b). We have

analyzed the average capacity (Model A) and its approxima-

tion (Model B) as defined above for different values of the

maximum active set size nmax and the SHO window ∆.

1) Comparison of Models: As can be seen from Figs. 3–

6, the analytical approximation of Model B comes very close

(a) User density for service“voice” and example snapshot

(b) Radio network with cell ar-eas and evaluated cells

Fig. 2. MORANS Turin scenario

to the results of Monte-Carlo-Simulation of Model A. On aqualitative level, the charts show the same relations between

the different parameter sets (relative position of different

graphs). Quantitatively, the results are very similar as well,

with a maximum relative approximation error of about 1 %.

This essentially means that in our evaluation model there is

no need for costly snapshot simulations.

2) SSDT capacity Results: When analyzing the SSDT re-

sults, it is obvious that the results differ noticeably between

cells. In our examples, values range from about 47 users (BS

2 2) to 59 users (BS 25 1). This was to be expected in a

setting with non-homogeneous traffic and irregular cell layout.

The deviations of cell capacities from the mean can in allcases be explained by analyzing the specific local situation.

The two main levers on cell capacity are a) the interference

situation—relative strength of the serving signal over the

interfering signals, reflected in the sumI

i=2γimγ1m

in (5)—and

b) the traffic distribution in the cell relative to the interference

distribution. The more traffic in a cell is placed in areas with

favorable interference situation (areas where the mentioned

sum is small), the higher the capacity. However, some of the

differences could to a certain degree be leveled by considering

shadow fading (cf. the remarks on the effect of SHO).

3) Influence of Soft Handover: It is striking how the

capacity behavior of the selected cells differs when taking intoaccount SHO and varying parameters. This diversity applies

for all cells in the scenario. In general, it can be observed that

the capacity of all cells decreases under SHO if the parameter

∆ is increased too much, all example charts presented here

show a decrease from ∆ = 3 to ∆ = 4. This trend was also

observed for the other cells with very few exceptions. It can

be explained by arguing that with increasing ∆, cells become

members of the active set of users far away from them and

the diversity gain is outweighed by increasing interference.

Beyond this general trend—and besides a general benefit

from diversity as specified in (1)—, cells can be roughly

divided into ones that clearly benefit from SHO (SHO graphs

lie above the SSDT graph) and ones that sacrifice capacity.

An example for the first case is BS 2 2, for the second

BS 25 1. These two types of cells can often be observed to

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