Machine Learning for Wireless Metaverse: Fundamentals, Use Case, and Future Directions
Khan, Latif U. Yaqoob, Ibrar Salah, Khaled Hong, Choong Seon Niyato, Dusit Han, Zhu Guizani, Mohsen
Khan, Latif U.
Yaqoob, Ibrar
Salah, Khaled
Hong, Choong Seon
Niyato, Dusit
Han, Zhu
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Today’s wireless systems are posing key challenges in terms of quality of service and quality of physical experience. Metaverse has the potential to reshape, transform, and add innovations to the existing wireless systems. A metaverse is a collective virtual open space that can enable wireless systems using digital twins, digital avatars, and interactive experience technologies. Machine learning (ML) is indispensable for modeling twins, avatars, and deploying interactive experience technologies. In this paper, we present the role of ML in enabling metaverse-based wireless systems. We discuss key fundamental concepts for advancing ML in the metaverse-based wireless systems. Moreover, we present a case study of deep reinforcement learning for metaverse sensing. Finally, we discuss the future directions along with potential solutions.
Citation
L.U. Khan et al., "Machine Learning for Wireless Metaverse: Fundamentals, Use Case, and Future Directions," in IEEE Internet of Things Magazine, doi: 10.1109/MIOT.2025.3575882
Source
IEEE Internet of Things Magazine
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Keywords
Avatars, blockchain, digital twins, machine learning, Metaverse
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Publisher
IEEE