A no-reference user centric QoE model for voice and web browsing based on 3G/4G radio measurements

This paper presents a novel Quality of Experience (QoE) prediction model, for voice and web browsing based on real Universal Mobile Telecommunications System (UMTS) and Long Term Evolution (LTE) data. It estimates the user perceived quality, in a Mean Opinion Score (MOS) scale, by evaluating several Radio Frequency (RF) channel measurements and Quality of Service (QoS) metrics. Real Drive Test (DT) data and MOS measurements were used as reference data, in order to produce a new QoE prediction model, using machine learning techniques. The Support Vector Regression (SVR) algorithm was used to map the QoS metrics into MOS. The new developed model enables the application of QoE as a more realistic network optimization criteria. The QoE model for voice calls presented a Root Mean Square Error (RMSE) of 11% and a correlation of 62%, when comparing the predicted MOS to the one that was measured. The web browsing model showed an higher correlation (of 92%) and a lower RMSE (of 10%).