Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks

Abstract

5G technology will greatly improve quality of human life by enabling new use cases that will fully leverage on the improved throughput, connections, and latency of the 5G networks. Enhanced Mobile Broadband (eMBB), which supports ultra-high throughput, is one of the most important features in 5G networks. This service is expected to improve users’ quality of experience (QoE) when using resource-intensive and far more interactive applications such as playing online games. It is widely known that 5G networks can be used for gathering network monitoring data and application metrics; however, the correlation between the data and the users’ QoE is not well understood. Since large amount of data can be collected, machine learning approach is well suited for predicting users’ QoE when playing online games in 5G networks. In this paper, an artificial neural network (ANN) model is proposed to predict the users’ QoE based on the network monitoring data of a 5G network during an online gaming session and the model's performance is evaluated. The ANN model consists of four layers which include one input layer, two hidden layers, and one output layer. The Unified Management Expert (UME) system is used to collect the network monitoring data from a 5G NSA indoor private campus network. The proposed ANN model achieves prediction accuracy of close to 80% using 30 most relevant features derived from the radio access network monitoring data.