Data Collection Framework for End-to-End Radio and Transport Network Quality Monitoring.


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Estimating Quality of Experience (QoE) and Quality of Service (QoS) metrics is crucial for delay-sensitive use cases, and it relies on the available information in an operator's network. However, for low-latency traffic types that use unreliable and best-effort transport solutions for data transfer, obtaining these metrics only based on transport network probing - and ignoring the radio characteristics - is challenging. While end-to-end QoS metrics can be obtained by correlating per session radio and core data, predicting QoE is more difficult and requires the use of machine learning (ML) models. Nevertheless, training these models demands a vast amount of diverse measured reference metric data, which can be hard to acquire. In this study, we propose a delay-critical service-focused data collection framework that automatizes the measurements of networked services under various synthetic network degradation and jointly monitors radio and transport metrics, e.g., radio quality, throughput, latency, and user plane traffic. Our proposed framework runs on general-purpose laptops, eliminating the need for any specialized or expensive hardware. Furthermore, our tool provides a significant amount of data and meta-labels in an easy and automated way, which can be used to train future ML models to estimate QoS and QoE for the examined service. This demo paper demonstrates our proposed data collection framework, with synthetic transport and radio degradation, on a video conferencing use case and provides insight into how the main disturbance types influence the service streams.
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Key words
QoE/QoS, automated network measurement, radio characteristics, data collection framework, synthetic network degradation, radio quality
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