Machine Learning for Wireless Metaverse: Fundamentals, Use Case, and Future Directions
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866913725836951552 |
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| author | Khan, Latif U. Yaqoob, Ibrar Salah, Khaled Hong, Choong Seon Niyato, Dusit Han, Zhu Guizani, Mohsen |
| author_facet | Khan, Latif U. Yaqoob, Ibrar Salah, Khaled Hong, Choong Seon Niyato, Dusit Han, Zhu Guizani, Mohsen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_03703 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | 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 Networking and Internet Architecture 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. |
| title | Machine Learning for Wireless Metaverse: Fundamentals, Use Case, and Future Directions |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2211.03703 |