Quality of Experience Modeling for Fixed Broadband Internet Using Machine Learning Algorithms
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Date
2024-04
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Addis Ababa University
Abstract
As the demand for dependable fixed broadband internet services continues
to grow, ensuring an excellent Quality of Experience (QoE) for end-users is
essential. This thesis centers on QoE modeling, employing advanced machine
learning techniques, specifically Support Vector Machine (SVM) and
Random Forest algorithms. The study utilizes subjective assessments and
Quality of Service (QoS) metrics, including latency, upload speed, download
speed, uptime, packet loss, and jitter, to comprehensively comprehend and
model the factors influencing user satisfaction.
The research incorporates an exhaustive feature selector to extract pertinent
features from the dataset, enhancing the precision of the models. Hyperparameter
optimization is carried out through a Grid Search approach to
fine-tune the models for optimal performance. To assess the models, a robust
cross-validation methodology is implemented.
The results indicate that SVM surpasses Random Forest in QoE modeling
for Virtual Internet Service Providers (vISPs) like Websprix and ZERGAW
Cloud with average accuracy score of 92% and 70% respectively. Conversely,
Random Forest proves to be the more suitable model for predicting QoE in
the case of the national ISP, ethio telecom with average accuracy value of
88%. This comparative performance analysis offers valuable insights into the
distinct strengths of each model for different service providers.
The research findings also indicate that employing both subjective and QoS
metrics in combination to model the user QoE yields superior model performance
and predictive outcomes compared to relying solely on subjective
assessments and QoS metrics.
These findings contribute to the ongoing discussion on QoE enhancement
in fixed broadband internet services, providing practical recommendations
for service providers based on observed model performances. The application
of machine learning, feature selection, and hyperparameter optimization
techniques underscores the importance of these methodologies in customizing
QoE models to specific service contexts, ultimately enhancing user satisfaction
in diverse fixed broadband Internet environments.
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Keywords
QoE, QoS, Support Vector Machine, Random Forest, vISP, Fixed Broadband Internet, Machine Learning Algorithm