A real-time QoE methodology for AMR codec voice in mobile network

This paper studies a general strategy to predict voice Quality of Experience (QoE) for various mobile networks. Particularly, based on data-mining for Adaptive Multi-Rate (AMR) codec voice, a novel QoE assessment methodology is proposed. The proposed algorithm consists of two parts. The first part is devoted to assessing speech quality of fixed rate codec mode (CM) of AMR while in the other one a adaptive rate CM is designed. Measuring basic network parameters that have much impact on speech quality, QoE can be monitored in real time for operators. Meanwhile, based on the measurement data sets from real mobile network, the QoE prediction strategy can be implemented and QoE assessment model for AMR codec voice is trained and tested. Finally, the numerical results suggest that the correlation coefficient between predicted values and true values is greater than 90% and root mean squared error is less than 0.5 for fixed and adaptive rate CM.