The increasing demand for advanced video-based services necessitates operators to ensure the most suitable network performance while also considering user satisfaction with the service. QoS provides significant insights on the network side to deliver satisfactory user experiences. On the other hand, QoE informs about how a given service is perceived from the user’s perspective. The more advanced video-based services to be offered with the more complex structure of 6G increase the importance of mapping QoS to QoE. This paper presents an XGBoost-based method for predicting QoE based on UE-based, network-based, and application-based QoS measurements obtained from a real and live mobile network. The results indicate that XGBoost is an effective method for user experience estimation.
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XGBoost-based QoE Prediction for Mobile Networks
Semantic Scholar · Computer Science · 2024
Abstract
The increasing demand for advanced video-based services necessitates operators to ensure the most suitable network performance while also considering user satisfaction with the service. QoS provides significant insights on the network side to deliver satisfactory user experiences. On the other hand, QoE informs about how a given service is perceived from the user’s perspective. The more advanced video-based services to be offered with the more complex structure of 6G increase the importance of mapping QoS to QoE. This paper presents an XGBoost-based method for predicting QoE based on UE-based, network-based, and application-based QoS measurements obtained from a real and live mobile network. The results indicate that XGBoost is an effective method for user experience estimation.