Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2312.06293v1
• Date: Mon, 11 Dec 2023 10:49:47 GMT
• Title: Mobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach
• Authors: Wenhan Yu, Terence Jie Chua, Jun Zhao
• Abstract summary: An interactive and immersive socialization experience is one of the promises of the Metaverse. The computation required for a smooth, seamless and immersive socialization experience in the Metaverse is overbearing. This paper introduces a novel Quality-of-Service (QoS) model for the accumulated experience in multi-user socialization on a multichannel wireless network.
• Score: 10.47302625959368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactive and immersive socialization experience between people is one of the promises of the Metaverse. In spite of the rapid advancements in current technologies, the computation required for a smooth, seamless and immersive socialization experience in the Metaverse is overbearing, and the accumulated user experience is essential to be considered. The computation burden calls for computation offloading, where the integration of virtual and physical world scenes is offloaded to an edge server. This paper introduces a novel Quality-of-Service (QoS) model for the accumulated experience in multi-user socialization on a multichannel wireless network. This QoS model utilizes deep reinforcement learning approaches to find the near-optimal channel resource allocation. Comprehensive experiments demonstrate that the adoption of the QoS model enhances the overall socialization experience.

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