Comparison of Models for WCDMA Downlink Capacity Assessment Based on a M ORANS Reference Scenario 1 Andreas Eisenbl¨ atter 2∗† , Hans-Florian Geerdes 2† , 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— Thir d gene ratio n wir eless telecommunica tion net- works based on WCDMA technology are being deployed across the world. Since the downlink is likely to be the limiting direction, it is crucial for network engineers to assess the downlink capacity of WCDMA radio cells. In this paper, we revisit a semi-analytical capacity evaluation model involving snapshot simulation. We fur- ther develop an alternative approach for assessing cell capacity, which is a generalization of recent analytical dimension reduction techniques for cell load computation. The second approach works under av er age load rather tha n snapsh ots, whi ch ena ble s a quick approximation of the simul ation results . We inv esti gate the rel ations hip between the two appr oache s. We demon strate how the MORANS (MObile Radio Access Refere nce Scenarios) reference datasets can be used to compare different approaches on a common basis. Based on a MORANS real-world scenario, we compar e the capacit y of diffe ren t cell s under vary ing soft hando ver paramete rs. The res ults show how cells ’ capa citie s vary under realistic data. As the approximative method is quite accurate, we can conclude that no snapshot simulation is needed for capacity analysis in our setting. I. I NTRODUCTION Radio networks ba sed on WCDMA technology are currently being deployed by telecommunication operat ors across the world. For dimens ioning the se radio networ ks, a cap aci ty estimation is crucial. In WCDMA, all signals are transmitted on the same freq uency band, so int erfe rence is inevitable, and radio networks are typically interference-limited. Capacity analysis is more involved than for traditional radio systems sin ce the amount of int erfe rence depends str ongl y on the mobile’s location. WCDMA technology allows for data rates that are much higher than with traditional radio technology. The se data rat es wil l sup port se rvic es that are esp eci all y demanding in the downlinkdirection. The downlink capacity is thus expected to become the bottleneck. We therefore focus on analyzing the downlink capacity of radio cells. Soft handov er(SHO), the capability of a mobile device to be connec ted to se ver al base sta tions (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 of1 This work is a product of the authors’ participation in COST 273 and the MORANS initiati ve (http://www.cost273.org/morans ). 2 Supported by the DFG Research Center MATHEON ”Mathematics for key technologies” in Berlin, Germany. BSs that the mobile is connec ted to is calle d its activ e set(AS). Several radio resource management parameters play a role 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 distribu tions (snapsh ots) is a well-k nown approac h for this kind of analysis. While it is generally accepted as a fairly accurate means for capacity prediction, it is computationally exp ens iv e sin ce experi ments ha ve to be repeat ed unti l the outcome 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 initia tiv e within COST273 [1]. In the remainder of this section we introduce our system mode l an d the so ft ha ndov er sc he me we us e, in II our definit ion of cap aci ty and the two models for cap aci ty are presented and compared. We provide computational results for both models in III and draw conclusions in IV. A. System Model The scenario contains Icells. 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 p max ; 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 perfectfast power control due to its sho rt cor rel ati on length. Thi s implies that the Carrier-to-Interference-plus-Noise-Ratio (CIR) perceived at mobile m (MS m ) for the signal on the link to cell 1 is assumed to exactly meet a specific threshold value denoted by µ m . The value µ m depends on the service and on the soft handover state (see below). The attenuation between cell i and
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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
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