Computational Intelligence - February 2014 - 44

Application
Notes

Hai-Lin Liu and Fangqing Gu
Guangdong University of Technology,
Guangzhou, China

On Solving WCDMA Network Planning
Using Iterative Power Control Scheme
and Evolutionary Multiobjective Algorithm

I. Introduction

ue to the increasing demand for
mobile radio services, 3G wireless network planning has been
becoming one of the most important
research f ield s. 3G system, e.g.
WCDMA, is based on Code Division
Multiple Access [1], which is quite different from Time Division Multiple
Access (TDMA) as used in 2G system.
In the 3G network planning, not only
are coverage, capacity and quality of the
signal interrelated, but multi-rate and
mixed-business also utilize the common carrier at the same time. As a
result, it makes the 3G network planning become more challenging. In this
paper, we will only concentrate on the
WCDMA network planning. Since the
WCDMA systems have self-interference and the effects of cell-breathing, it
makes the coverage, capacity and interference of the base stations (BSs) restrain
each other [2], [3]. That is, the area
actually covered by a BS depends on the
Quality of Service (QoS) and the traffic
demand distribution. Therefore, the
relationship between coverage, capacity
and interference should be fully taken
into account in the planning process.
In the past few years, a number of
models have been developed for the
WCDMA network planning, which can
be roughly summarized into two categories. In the first category, the models only
consider the location of the BS [4]-[8],
[10], [32]. For example, Amaldi [4]

Digital Object Identifier 10.1109/MCI.2013.2291690
Date of publication: 14 January 2014

planned the location of the BSs by considering the quality constraints for downlink (i.e. BS to user). In [5], the model is
extended to the uplink (i.e. user to BS),
which is more stringent
than the downlink for
symmetric traffic. Also, the
models considering both
downlink and uplink were
presented in [6], [7]. Furthermore, Yang et al. [32]
presented a programming
model which takes into
account soft handover
[10] and the fast transmission power control. By
contrast, the models in the
other category consider
not only the locations of © eyewire
the BSs, but also the BSs' configuration
parameters [11]-[15]. For example, Gu et
al. [11] proposed a multiobjective optimization model for WCDMA network
planning, in which the antenna height
and sector configuration are both considered. Furthermore, one of the most comprehensive models presented in [12] discussed a mathematical programming
model to support the decisions, e.g.
where to install new BSs, and how to
select their configuration (i.e. antenna
height and tilt, sector orientations, etc.),
so as to find a trade-off between maximizing coverage and minimizing cost.
This model has considered not only the
configuration parameters of the BSs, but
also the signal quality constraints in both
uplink and downlink directions.
In the WCDMA network planning,
the most time-consuming procedure is to

IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2014

Yiu-Ming Cheung
Department of Computer Science,
hong Kong Baptist University,
hong Kong, China
Shengli Xie
Guangdong University of Technology,
Guangzhou, China
Jun Zhang
Department of Computer
Science, Sun Yat-Sen University,
Guangzhou, China

guarantee the QoS of the connection.
There are many measures presented for
this purpose. A simple and commonly
adopted model proposed in [16], [17]
assumes that the interference due to the neighboring cells (i.e. intercell
interference) can be
expressed as a fraction of
the interference due to
the other connections in
the same cell (i.e. intracell
interference). This model
is suitable for homogeneous distribution of traffic demand, but not for
the inhomogeneous one.
In the literature, two
power control mechanisms have been presented to minimize
the interference and guarantee the quality.
In the first one [18], [19], the transmitted
power is adjusted so that the power
received on each connection is equal to a
given value. Therefore, whether a connection is active or not just depends on
the mobile station positions. However,
this simple mechanism may allocate more
radio resources than necessary. In the second one [12], the transmitted power is
adjusted so that the signal-to-interference
ratio (SIR) is equal to the target value.
This mechanism is more efficient than
the first one because it allows for the use
of lower power. Nevertheless, it is more
complex because the power emitted by
each station depends on that emitted by
all the others. Moreover, some dimension
reduction strategies have been addressed
in [20]-[22] for systems equipped with

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