Abstract
In recent years, the number of services in mobile networks has increased exponentially. This increase has forced operators to change their network management processes to ensure an adequate Quality of Experience (QoE). A key component in QoE management is the availability of a precise QoE model for every service that reflects the impact of network performance variations on the end-user experience. In this work, an automatic method is presented for deriving Quality-of-Service (QoS) thresholds in analytical QoE models of several services from radio connection traces collected in an Long Term Evolution (LTE) network. Such QoS thresholds reflect the minimum connection performance below which a user gives up its connection. The proposed method relies on the fact that user experience influences the traffic volume requested by users. Method assessment is performed with real connection traces taken from live LTE networks. Results confirm that packet delay or user throughput are critical factors for user experience in the analyzed services.
Highlights
In recent years, there has been a significant increase in the number of users and services in mobile networks
The minimum Quality of Service (QoS) threshold can be determined as the THP( fDbC) P,DL value that causes CDm( febd)ian to drop and VD( fLbm)edian to remain constant
A novel automatic method for estimating QoS thresholds to be integrated in user utility functions on a per-service basis in an Long Term Evolution (LTE) system is proposed
Summary
There has been a significant increase in the number of users and services in mobile networks. QoS management processes have been replaced by a more modern approach that is focused on QoE This new paradigm has become a key differentiating factor in a competitive market in which networks and services are similar for all operators. CEM tries to understand the factors influencing user quality perception with the aim of describing the relationship between measurable variables and the experience perceived by the end user Such variables may be human (e.g., age, education, etc.), system (e.g., resolution, throughput, delay, etc.) or context (e.g., cost, data charging gap, mobility, etc.) factors [11,12,13]. A novel automatic method is presented to tune QoS thresholds in classical analytical QoE models by analyzing radio connection traces in an Long Term Evolution (LTE) system.
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