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Machine Learning (ML) provides a theoretical and methodological framework that allows to quantify the relationship between the user's Quality of Experience (OoE) and the network's Quality of Service (QoS). In the literature, several ML-based QoS/QoE correlation models have been proposed. All of those models use inductive supervised learning techniques and most of them are built in an offline batch...
The machine learning provides a theoretical and methodological framework to quantify the relationship between user OoE (Quality of Experience) and network QoS (Quality of Service). This paper presents an overview of QoE-QoS correlation models based on machine learning techniques. According to the learning type, we propose a categorization of correlation models. For each category, we review the main...
The Quality of Experience (QoE) determines the user satisfaction degree of communication services. Quantifying the relationship between User QoE and Network Quality of Service (QoS) enables the services providers to estimate the contribution of network performance in overall user satisfaction. Based on the Multiple Linear Regression (MLR) and the exponential hypothesis, we propose in this paper a...
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