Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2022-10-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/5866 |
| _version_ | 1797963640131813376 |
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| author | Keat Han Tan Heng Siong Lim Kah Seng Diong |
| author_facet | Keat Han Tan Heng Siong Lim Kah Seng Diong |
| author_sort | Keat Han Tan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T01:31:24Z |
| format | Article |
| id | doaj.art-59ff8e78fb3a45369a60303ed7e825ca |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-11T01:31:24Z |
| publishDate | 2022-10-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-59ff8e78fb3a45369a60303ed7e825ca2023-01-03T09:31:59ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002022-10-011351035104410.14716/ijtech.v13i5.58665866Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G NetworksKeat Han Tan0Heng Siong Lim1Kah Seng Diong2Multimedia University- Multimedia University<br/>-ZTE (M) Corporation Sdn. Bhd.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.https://ijtech.eng.ui.ac.id/article/view/58665gartificial neural network (ann)online gamingquality of experience (qoe) |
| spellingShingle | Keat Han Tan Heng Siong Lim Kah Seng Diong Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks International Journal of Technology 5g artificial neural network (ann) online gaming quality of experience (qoe) |
| title | Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks |
| title_full | Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks |
| title_fullStr | Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks |
| title_full_unstemmed | Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks |
| title_short | Modelling and Predicting Quality-of-Experience of Online Gaming Users in 5G Networks |
| title_sort | modelling and predicting quality of experience of online gaming users in 5g networks |
| topic | 5g artificial neural network (ann) online gaming quality of experience (qoe) |
| url | https://ijtech.eng.ui.ac.id/article/view/5866 |
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