Framework for Modelling Mobile Network Quality of Experience Through Big Data Analytics Approach

Abstract

The increase in the usage of different mobile internet applications can cause deterioration in the mobile network performance. Such deterioration often declines the performance of the mobile network services that can influence the mobile Internet user's experience, which can make the internet users switch between different mobile network operators to get good user experience. In this case, the success of mobile network operators primarily depends on the ability to ensure good quality of experience (QoE), which is a measure of users' perceived quality of mobile Internet service. Traditionally, QoE is usually examined in laboratory experiments to enable a fixed contextual factor among the participants even though the results derived from these laboratory experiments presented an estimated mean opinion score representing perceived QoE. The use of user experience dataset involving time and location gathered from the mobile network traffic for modelling perceived QoE is still limited in the literature. The mobile Internet user experience dataset involving the time and location constituted in the mobile network can be used by the mobile network operators to make data-driven decisions to deal with disruptions observed in the network performance and provide an optimal solution based on the insights derived from the user experience data. Therefore, this paper proposed a framework for modelling mobile network QoE using the big data analytics approach. The proposed framework describes the process of estimating or predicting perceived QoE based on the datasets obtained or gathered from the mobile network to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile Internet QoE.

Deriving QoE in systems: from fundamental relationships to a QoE-based Service-level Quality Index

“…A common approach is to use the distribution of MOS scores in the system (as obtained from a QoS-to-MOS mapping function) to draw conclusions with respect to the QoE distribution (or other QoE metrics) of users in the system. These metrics are then further used to drive QoE optimization and management decisions [34][35][36]. Similarly, [37] analyzes MOS distributions, but states that "[...] the ultimate goal is to predict the distribution of user ratings".…”

A Quality of Experience Management Framework for Mobile Users

“…Regarding the QoE, a framework to model the mobile Internet network QoE using Big Data is proposed in [22]. Up to now, parameters such as bandwidth, loss, and delay were sufficient to evaluate Quality of Service (QoS) [23].…”