Resource Allocation for Mobile Metaverse with the Internet of Vehicles
over 6G Wireless Communications: A Deep Reinforcement Learning Approach
- URL: http://arxiv.org/abs/2209.13425v1
- Date: Tue, 27 Sep 2022 14:28:04 GMT
- Title: Resource Allocation for Mobile Metaverse with the Internet of Vehicles
over 6G Wireless Communications: A Deep Reinforcement Learning Approach
- Authors: Terence Jie Chua, Wenhan Yu, Jun Zhao
- Abstract summary: The Metaverse relies on a core approach, digital twinning, which is a means to replicate physical world objects, people, actions and scenes onto the virtual world.
With the development of Mobile Augmented Reality (MAR), users can interact via the Metaverse in a highly interactive manner, even under mobility.
We design an environment with multiple cell stations, where there will be a handover of users' virtual world graphic download tasks between cell stations.
- Score: 8.513938423514636
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Improving the interactivity and interconnectivity between people is one of
the highlights of the Metaverse. The Metaverse relies on a core approach,
digital twinning, which is a means to replicate physical world objects, people,
actions and scenes onto the virtual world. Being able to access scenes and
information associated with the physical world, in the Metaverse in real-time
and under mobility, is essential in developing a highly accessible, interactive
and interconnective experience for all users. This development allows users
from other locations to access high-quality real-world and up-to-date
information about events happening in another location, and socialize with
others hyper-interactively. Nevertheless, receiving continual, smooth updates
generated by others from the Metaverse is a challenging task due to the large
data size of the virtual world graphics and the need for low latency
transmission. With the development of Mobile Augmented Reality (MAR), users can
interact via the Metaverse in a highly interactive manner, even under mobility.
Hence in our work, we considered an environment with users in moving Internet
of Vehicles (IoV), downloading real-time virtual world updates from Metaverse
Service Provider Cell Stations (MSPCSs) via wireless communications. We design
an environment with multiple cell stations, where there will be a handover of
users' virtual world graphic download tasks between cell stations. As
transmission latency is the primary concern in receiving virtual world updates
under mobility, our work aims to allocate system resources to minimize the
total time taken for users in vehicles to download their virtual world scenes
from the cell stations. We utilize deep reinforcement learning and evaluate the
performance of the algorithms under different environmental configurations. Our
work provides a use case of the Metaverse over AI-enabled 6G communications.
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