Quality of Experience Enhancement in Wireless Metaverse: A Resource Optimization Scheme
Guizani, Maher Khan, Latif U. Islam, Mohammad A.
Guizani, Maher
Khan, Latif U.
Islam, Mohammad A.
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Abstract
The rapid advancement of metaverse applications in wireless environments necessitates efficient resource management to enhance Quality of Experience (QoE). This paper presents a novel framework for optimizing wireless resource allocation within the metaverse to optimize QoE using convex optimization and matching theory. We formulate a QoE optimization problem considering packet error rate (PER) and immersive experience. Our problem also enables us to trade off between immersive experience and PER while computing QoE. The formulated problem is a mixed-integer non-linear programming (MINLP) problem, which is addressed through decomposition, convex optimization, matching theory, and block successive upper-bound minimization (BSUM). Specifically, for a solution, our proposed model integrates matching theory, BSUM, and convex optimization to optimize the association, transmit power allocation, and resource allocation. Finally, numerical results are provided.
Citation
M. Guizani, L. U. Khan and M. A. Islam, "Quality of Experience Enhancement in Wireless Metaverse: A Resource Optimization Scheme," 2025 International Wireless Communications and Mobile Computing (IWCMC), Abu Dhabi, United Arab Emirates, 2025, pp. 1656-1660, doi: 10.1109/IWCMC65282.2025.11059440.
Source
23rd International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Conference
21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Keywords
convex optimization, Internet of Things, matching theory, metaverse
Subjects
Source
21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025
Publisher
IEEE