Joint Communication and Sensing in Metaverse over UAVs: A Deep Reinforcement Learning Approach
Jangirova, Sabina Khan, Latif U Jankovic, Branislava Ullah, Waseem Guizani, Mohsen
Jangirova, Sabina
Khan, Latif U
Jankovic, Branislava
Ullah, Waseem
Guizani, Mohsen
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Department
Machine Learning
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Conference proceeding
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Abstract
The development of wireless systems based on metaverse to effectively enable novel applications (e.g., surveillance and remote healthcare) gained significant interest from the research community. A metaverse combines many technologies with a virtual representation of the real world system for enabling applications. Although a metaverse enables many applications, there are challenges associated with its deployment. Therefore, in this paper, we consider sensing in metaverse-empowered unmanned aerial vehicle (UAV) networks. A massive number of sensors are considered to effectively sense the physical world states and share them with the metaverse. A problem is formulated to minimize the cost of sensing in terms of energy and latency. Our problem is based on optimizing the resource allocation and association for sensing units. We present a solution based on double deep Q-network (DDQN) for joint association and resource allocation in sensing for metaverse over UAVs network because the defined problem is non-convex and combinatorial. In the end, we validate our idea with comprehensive simulation results.
Citation
S. Jangirova, L.U. Khan, B. Jankovic, W. Ullah, M. Guizani, "Joint Communication and Sensing in Metaverse over UAVs: A Deep Reinforcement Learning Approach," 2026, pp. 1-6.
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Conference
2025 IEEE Symposium on Computers and Communications (ISCC)
Keywords
40 Engineering, 4006 Communications Engineering, 46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science
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Source
2025 IEEE Symposium on Computers and Communications (ISCC)
Publisher
IEEE