Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

Abstract

The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of QoE, this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal–regulatory, technological–process, content-formatted and performative, contextual–relational and subjective user-influencing factors on QoE sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark QoE as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of QoE. The hypothetical setting is that the individual user’s curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable QoE model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&H. By treating the data with mathematical and machine learning models, the QoE assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of QoEi. The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%.

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

“…Quality of Experience (QoE) is often mentioned as a metric to measure the degree to which user needs are fulfilled (Crespi et al, 2011; De Marez & De Moor, 2007; Mahmud et al, 2019). However, research on measuring and defining QoE is mainly limited to the field of telecommunication or multimedia (Banjanin et al, 2022; Baraković et al, 2010; Rehman Laghari & Connelly, 2012). Consideration of QoE needs to be extended to various domains to fulfill the user needs during the technology development process (Mitra et al, 2013).…”

PCA modeling of extraction and selection of variables influencing LTE network delay in urban mobility conditions

“…Structured inputoutput vectors are processed in the IBM SPSS Modeler software using the Auto Classifier node to automatically create different delay classification models simultaneously. In one pass through the modeling process, Auto Classifier examines different machine learning techniques, with default option settings [3]. In this case, options mean e.g.…”

“…Supported techniques include neural networks, classification and regression trees (C&R Tree), Quick, Unbiased, Efficient Statistical Tree (QUEST), Chi-square Automatic Interaction Detection (CHAID), C5.0, Logistic regression, decision list, Bayesian networks, Discriminants, Nearest neighbors and Support Vector Machines (SVM). Based on the results of model testing, Auto Classifier offers the most accurate solutions and ranks them according to the overall classification accuracy expressed as a percentage [3].…”

“…Contemporary trends are directed towards the creation and prioritization of interpretable models, ahead of complex models that, as a rule, provide better performance. Excessive network delay can significantly negatively affect the Quality of Experience (QoE) of users of telecommunication services, especially in conditions of high mobility in cities [2,3]. This influence is expressed by measures of rigidity that services cause in users: Interpersonal (R1), Behavioral (R2), Structural -longing for structure and coping with the lack of structure (R3), Prospective anxiety (R4), Inhibitory rigidity (R5) [2,3].…”

Predictive Modeling of Delay in an LTE Network by Optimizing the Number of Predictors Using Dimensionality Reduction Techniques

“…Sustainable Quality of Service (QoS) for users is one of the main tasks of mobile operators. This orients them to provide comprehensive support for various applications and services with numerous QoS requirements in order to meet the expected levels of user Quality of Experience (QoE) [1,2]. The development of Long Term Evolution (LTE) technology, which today is based on IP network configuration [3], is an example of such an orientation.…”