dl capacity mgmt-wcdma
TRANSCRIPT
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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
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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.
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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
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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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