METHOD FOR ANALYSIS OF INTERACTION OF QOE AND QOS PARAM-ETERS ON THE BASIS OF MACHINE MANAGEMENT ALGORITHMS
DOI:
https://doi.org/10.18372/2310-5461.56.17130Keywords:
cellular network, network architecture, procedures, QoE, QoS, monitoringAbstract
Today, most telecommunication service providers are interested in making sure that customers are satisfied with the services they receive. It is obvious that users of a certain service will continue to choose the same provider if their expectations of service quality have been met. Therefore, in order to meet customer expectations, providers need to constantly measure the current level of quality of the services they provide. In order to provide the best quality services by telecommunications providers, companies need to consider not only network quality and QoS, but also QoE. Surveying subscribers in view of the expansion of the network and the increase in the number of services provided is no longer a productive process, thus there is a need to ensure consistent end-to-end monitoring of the quality of the user experience. Based on these considerations, this paper proposed to use a machine learning model called "Random Forest" for data analysis, and also step-by-step described the stages of analysis based on historical data. In the course of the study, it was possible to test the QoE prediction method depending on the QoS parameters. It turned out that it is possible to reduce the data set proposed in the second section without affecting the accuracy and performance of the calculations performed by the machine learning model. However, the remaining parameters are predicted with 100% accuracy, which is an excellent result. Thus, the proposed method using machine learning algorithms can be used in its work by telecommunication providers and mobile operators to ensure end-to-end monitoring of the level of user satisfaction with the services provided by the provider.
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