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The launch of commercial 5G networks has unlocked numerous opportunities for heavy data users and high-speed applications. The expected requirements for enhanced mobile broadband (eMBB) are pushing end-users to adopt 5G optimistically. Though already deployed 5G networks have shown high data rates and very low latency, the service-based experience and application behavior have been challenging to monitor. The legacy quality of experience (QoE) and service (QoS) monitoring and evaluation techniques have shown limitations in 5G standalone networks. The current 5G deployment large amount of user plane traffic generated by end-users makes the legacy-monitoring task very costly for mobile network operators (MNOs). And the complexity of the projected future 5G architecture, including advanced technologies such as network functions virtualization (NFV), software-defined networking (SDN), and network slicing, makes traditional service detection and QoE assessment ineffective. In this paper, we discuss a cost-effective hybrid analytical approach to eMBB service detection, analysis, and perceived user QoE measurement from raw traffic in a live 5G standalone (SA) network. We first use flow-level-based packet inspection and machine learning to detect and classify eMBB services from raw traffic. We then use a statistical approach to compute the user quality index (UQI). The concept is tested on traffic captured on a fixed 5G SA network. And the output enabled the MNO to have a 5G QoE assessment structure and awareness to adjust network traffic policies.
Dahj et al. (Sat,) studied this question.