Bayesian Network Approach for Modelling and Inference of Communication Networks
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Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2019
Författare
Mouliakos, Themistoklis Christos
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A collection of radio base stations defines the radio access network which is responsible
for connecting the user equipment with the core network which in turn connects
to the internet through a gateway. The radio base station provides the uplink and
downlink communication of the user equipment and its cover region is divided in
cells. There can be thousands of parameters affecting the functionality of a radio
base station and a primary concern of this thesis is to estimate probabilistic relations
between those parameters and the key performance indicators.
Bayesian Networks is a powerful mathematical tool which can model complex systems
and present possible co-influences between variables. In the last decades there
was a great advancement on creating Bayesian Networks from data, mainly because
of new algorithms and the increase of computational power. The power of
the Bayesian Networks to represent in a compact and visually easy way the joint
probability of a set of variables, make it ideal candidate for the complex data of a
communication network.
In this thesis work, most of the focus will be put on learning an acceptable structure
by a combination of expert’s knowledge and appropriate learning methods. Two
main approaches were investigated by using a Hybrid Network(mix of continuous
and discrete data) and a full discretization of the variables. Although both networks
managed to capture expected associations between the variables, the hybrid network
poses serious restrictions that are not well supported by the data.
In order to investigate more approaches to structure learning, the Hill Climbing algorithm
was enhanced with random restarts. As a third option the bootstrap method
was tested also as a different way to construct more robust models and estimate the
strength confidence of both the arcs and their direction. Finally a comparison of the
predictive power for the different learning techniques was evaluated through cross
validation.