Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach

TL;DR

This paper proposes a deep reinforcement learning-based resource allocation method to minimize latency in delivering real-time virtual world updates to moving users in the IoV environment over 6G wireless networks, enhancing Metaverse interactivity.

Contribution

It introduces a novel deep reinforcement learning approach for resource allocation in mobile Metaverse scenarios with IoV, addressing latency challenges during user handovers.

Findings

Deep RL effectively reduces download latency for virtual scenes.

The approach adapts well to different environmental configurations.

Significant improvement over traditional resource allocation methods.

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.