While the Mean Opinion Score (MOS) is the most well-known way to quantify Quality of Experience (QoE), it only provides average insight. In this paper, we will demonstrate that instead of only relying on the MOS value, predicting users’ perceived quality through probabilistic metrics provides service providers with more accurate insight to improve their network decisions, relying on more accurate measures of how many of them are satisfied or not with the provided service. Furthermore, a hybrid non-intrusive ensemble learner based on the selection of multiple base learners is designed to estimate the QoE of the VoIP signal. The performance of our proposed model is compared to that of individual learners, demonstrating that the proposed scheme outperforms earlier schemes. In addition, in contrast with earlier schemes that relied on their own laboratory-generated dataset, another key advantage of our approach is that it extracts a wide variety of different system parameters, such as noise type and echo delay, and signal parameters, such as fundamental frequency, only from the degraded signal. Finally, we show how the more accurate predicted QoE values can be used by service providers to properly modify network parameters to get closer to the required QoE levels.

In wireless communications, focusing on end-user satisfaction and maximizing network operators' revenue are emerging business challenges. In this paper, we investigate the price-based power allocation problem where, first, base stations (BSs) set prices by maximizing users' utility modeled by their mean opinion score (MOS). Then, each user's optimal power is set by maximizing operator revenue while ensuring the minimum data rate for each user. We propose a hybrid MOS-based pricing method to model users' utility instead of the conventional achievable rate approach. Our hybrid approach applies a machine learning algorithm to model the MOS of the call service in terms of the received signal strength indicator (RSSI). In terms of MOS and outage probability, our proposed method outperforms the rate-based pricing method and the conventional objective MOS models. In addition, we consider a joint admission control and price-based power allocation problem. When a new user requests to connect, a central controller determines whether or not to accept the new connection based on the system's MOS and average outage probability. The results demonstrate a trade-off between the number of users admitted and their level of satisfaction, providing operators with crucial knowledge about how to use their network resources more effectively.