# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
###### 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. traffic monitoring; mobile network; quality of experience; big data; LTE +
Footnote †: journal: _electronics_  
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for all operators. In this new framework focused on the user's perspective, Customer Experience Management (CEM) has become an extremely important task for mobile network operators [7]. CEM aims to improve the final user experience by optimizing the use of network resources [8]. One of the main tasks involved in CEM is to find sophisticated indicators at the service level to ensure service performance is properly characterized. Unfortunately, such service performance indicators are usually not available for network operators, unless complex crowd sourcing schemes are deployed [9]. Thus, 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 (i.e., QoE modeling [8; 10]. 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]. For system aspects, QoE models often consist of analytical utility functions relating network-based QoS indicators to user opinion [14]. In its simplest form, this relationship between QoS and QoE is a logarithmic [15] or exponential [16] function. This approach is followed by most frameworks for large-scale, on-line, passive monitoring for each connection [17; 18]. For a comprehensive survey of objective QoE models, the reader is referred to [19]. Most QoE models include parameters reflecting QoS thresholds above/below which QoE remains constant [20]. The values of these thresholds are derived from subjective tests with real users in lab environments, which are time-consuming and may not reflect the true conditions in real life. Moreover, objective QoE models are seldom updated. However, customer expectations continuously increase as a result of handset upgrades, service diversification and new radio technologies. As a consequence, user satisfaction progressively decreases if the provided QoS remains the same. For this reason, QoE models must be continuously updated. In most cases, tuning model parameters would be enough, avoiding more complex actions, such as changing the model structure. Even so, an automatic parameter tuning process is required to avoid subjective tests. Current mobile networks generate a huge amount of information in the form of measurements and interaction registers [21]. However, for simplicity, the majority of this information is discarded, and CEM is often performed based on limited data. Thus, operators are only focused on Configuration Management (CM), Performance Management (PM), Charge Data Record (CDR) and Customer Relationship Management (CRM) data.