Analyzing the Fullband E-Model and Extending it for Predicting Bursty Packet Loss

Michael, Thilo; Mittag, Gabriel; Möller, Sebastian

doi:10.1109/qomex48832.2020.9123104

Cited by 1 publication

(1 citation statement)

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“…However, due to the simulative nature of our approach, such full reference metrics cannot be applied in our case. Nevertheless, the general relationship between the MOS and its impacting factors, i.e., delay and packet loss rate, is the same for both models [25], [26]. Hence, we can expect a similar performance of the applied ML models, independent of the specific QoE model applied.…”

Section: A Simulation Scenarios and Ground-truth Data Collectionmentioning

confidence: 95%

ML-Based QoE Estimation in 5G Networks Using Different Regression Techniques

Schwarzmann

Marquezan

Trivisonno

et al. 2022

IEEE Trans. Netw. Serv. Manage.

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Monitoring and providing customers with a satisfying Quality of Experience (QoE) is a crucial business incentive for mobile network operators (MNOs). While the MNO is capable of monitoring a vast amount of network-related key performance indicators (KPIs), it typically does not have access to applicationspecific performance metrics. Among others, this is due to practical obstacles, such as missing standardized interfaces between the network and the application. Existing QoE models allow to map collected KPIs to the user-perceived quality. However, they are not dynamic, cumbersome to obtain, and often rely on application-level information, such as the stalling duration in the case of video streaming.The 5G networking architecture provides new features which can potentially overcome current limitations of in-network QoE monitoring. More specifically, the Application Function (AF) provides a standardized interface for communicating between 5G systems and third parties, such as application providers. The Network Data Analytics Function (NWDAF) is capable of collecting a vast number of network statistics from other 5G network functions and is dedicated to training and deploying Machine Learning (ML) models. This opens new possibilities, unimaginable for earlier mobile network generations, to dynamically learn the relationship between network KPIs and QoE by utilizing ML. Besides elaborating on how the new capabilities introduced with 5G can support an ML-based QoE estimation, we perform a simulation-based feasibility study which evaluates the estimation accuracy of different state-of-the-art regression techniques. In addition, we discuss them with respect to various qualitative aspects from an MNO's point of view.

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Section: A Simulation Scenarios and Ground-truth Data Collectionmentioning

confidence: 95%