QoE Model for Addis Ababa LTE Web Browsing Service Using Neural Network Approach
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Date
2019-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
In order to address the customer’s satisfaction, mobile operators try to find out what the
customer needs and what quality makes the customer satisfied. The customer satisfaction
can be measured or estimated by Quality of Experience (QoE) measurement. Its estimation
and measurement is important to identify the network problems, to understand
causes and contributing factors.
Web browsing is one of the widely used application on Long Term Evolution (LTE) networks.
Therefore, it is essential for service providers to ensure a better QoE on web
browsing service. Web QoE can measure the user satisfaction by subjective or objective
measurement. Subjective test suffers from some drawbacks, such as it has high cost in
terms of time, money, and manual effort and also cannot be used for real-time QoE evaluation.
In
Ethiopia
only
subjective
measurement
is
used,
to
know
the
level
of
customer
satisfaction.
Due
to
that,
the
company
is
exposed
for
high
expenses
and
also
can
not
perform
the
real
time
measurement
of
QoE.
To overcome the problem on subjective test, this thesis developed a web browsing QoE
model, using Neural Network algorithm that is implemented in matlab software. The
model takes the following QoS metrics as input parameters: page response delay, page
content browsing delay and page download throughput. The model map these metrics
to QoE interms of Mean Opinion Score (MOS).
The model performed an estimation of QoE with a Mean Square Error (MSE) of 0.002 and
correlation of 97.2%, relatively to the target QoE. As the result indicates, the estimated
and measured QoE values are highly correlated. And the error between them is very low.
So, this model can be used for estimating the web browsing QoE for the mobile operators,
to get objective measurement advantages. Also, it can be used for operators to identify
the network factors that most influence the web browsing QoE.
Description
Keywords
QoE, QoS, LTE, MOS, Estimation, Web Browsing, Model, Neural Network, Subjective Measurment, Objective Measurment