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- W4385453646 abstract "Quality of Experience (QoE) is an important indicator of user satisfaction. In this paper, we propose a learning-based QoE model for voice services based on real-world data that models the Mean Opinion Score (MOS) in terms of the Received Signal Strength Indicator (RSSI). In contrast with earlier studies that used an objective approach to model QoE, one key feature of our study is the use of a hybrid approach, because hybrid models can evaluate QoE accurately by learning human behaviors. Due to the importance of resource allocation from a service provider's point of view, we will also apply our model to a power allocation problem and formulate an optimization problem to maximize the sum of the QoE of users while guaranteeing the minimum data rate for each user. We show that the proposed hybrid model outperforms the conventional objective model in terms of average MOS and outage probability. Furthermore, users are more satisfied with the QoE maximization problem compared to the conventional rate maximization problem. Also, due to the limited power available to meet the needs of all users, we will introduce a joint power allocation and admission control problem. The results provide a trade-off between the number of admitted users and their satisfaction, providing operators with significant insight on how to better utilize their network resources. The main idea behind our proposed approach is to have downlink transmitters monitor outage probability and MOS of the overall system for each connection request in order to determine the service's perceived quality level and then decide whether or not to accept a new connection. Therefore, the key conclusion on the admission control side is that the proper threshold level should be wisely selected by the operator such that a particular number of users are served while also attaining their targeted MOS values. As a result, a delicate balance is required between accepting new users and the subsequent decrease in satisfaction. Overall, the dynamic nature of our approach makes it suited for wireless networks where channel conditions vary frequently and QoE is of key importance." @default.
- W4385453646 created "2023-08-02" @default.
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- W4385453646 date "2023-01-01" @default.
- W4385453646 modified "2023-10-16" @default.
- W4385453646 title "Hybrid QoE-Based Joint Admission Control and Power Allocation" @default.
- W4385453646 doi "https://doi.org/10.1109/tvt.2023.3300775" @default.
- W4385453646 hasPublicationYear "2023" @default.
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