Wireless Network Design: Optimization Models and Solution ...

272 Marina Aguado, Jasone Astorga, Nerea Toledo and Jon Matias
12.1 Introduction
In the last few years, the evaluation of large scale applications across mobile wireless
networks has raised a crucial challenge to the research community. Actually,
when facing the necessity to design or evaluate a specific network implementation
different options appear, namely measurement, emulation, simulation, or analytical
modeling. Therefore, one of the most important steps in the start-up evaluation
process is to identify the most suitable technique for each scenario. In order to do
so, and before setting up a test bed or measurement experiment, writing complex
equations for an analytical model, or building up elaborate simulation scenarios, the
problem to be solved must be clearly stated. As the old saying goes “a problem well
stated is half solved”.
For this purpose, the first issue to be considered is the global aim being sought,
which is, in fact, the analysis of the system’s performance rather than a full set of
numerical statements describing its operation. Bearing this point in mind, it must be
noted that the specific tool or methodology used to reach that aim depends on the
nature of the problem under study and the time scale of interest. In this sense, test
beds provide realism but are coupled to the physical circumstances where the experiment
is carried out and frequently they constitute a complex, expensive, and timeconsuming
approach. Additionally, when a new concept is proposed (e.g., a new
protocol, technology or algorithm) in the research field, the possible approaches are
restricted to analytical models, simulation, and emulation. More specifically, when
a mobile wireless network is involved in a complex multi-factor and multimode
scenario, then an analytical model may not be feasible due to the high level of complexity.
In fact, analytical modeling requires in general many simplifications and
assumptions compared to a simulation process, which can incorporate more details
and require fewer assumptions.
Simulation modeling methods can be quite powerful in gaining insight into the
behaviour of complex systems. The main advantages of simulation modeling include
the ability to evaluate scenarios not easily achievable through empirical methods
(i.e. scalability testing of networks) and the ability to modify models to test
system sensitivity and tune performance [14]. When using simulation modeling,
many beginner analysts tend to model the behaviour of the system under evaluation
as close as possible to the real world without paying attention to the problem they
are expected to solve. Assuming they are even able to complete such a complex
simulation, this approach may result in a costly, time consuming, and fruitless simulation
product. Therefore, when using a simulation tool for evaluation or design
purposes, the idea is not to model the full detailed “scenario” but to keep clearly in
mind the specific question we seek to answer. On the other hand, we cannot forget
that when implementing a system level simulator — a simulator that considers many
users, many possible cells, and different layers of the protocol stack — the signal
propagation must be characterized correctly and accurately enough for the purposes
of the study.
Another important application of simulation modeling techniques is to perform
meaningful comparative studies of different technologies, protocols, or new imple-