面向元宇宙的多接入边缘计算中以人为本的资源分配
Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing
AI总结：
本文提出基于多智能体软演员-评论家与图卷积网络（SAC-GCN）的自适应边缘资源分配方法，通过图建模和自注意力机制优化元宇宙中的资源分配，显著提升用户体验、平衡性和利用率。
摘要：
多接入边缘计算（MEC）是解决元宇宙中计算密集、低延迟渲染任务的一种有前景的方案。然而，如何将边缘有限的通信和计算资源最优地分配给元宇宙中的大量用户是一个相当具有挑战性的问题。在本文中，我们提出了一种基于多智能体软演员-评论家与图卷积网络（SAC-GCN）的自适应边缘资源分配方法。具体而言，SAC-GCN将多用户元宇宙环境建模为一个图，其中每个智能体由一个节点表示。每个智能体通过带有自注意力机制的图卷积网络学习智能体之间的相互作用，以进一步确定元宇宙中一个用户的资源使用情况。通过以虚拟城市公园元宇宙为例，对用户体验、资源分配平衡性和资源利用率进行分析，验证了SAC-GCN的有效性。实验结果表明，SAC-GCN在提升整体用户体验、平衡资源分配以及提高资源利用率方面分别至少优于其他资源分配方法27%、11%和8%。
英文摘要：
Multi-access edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In this paper, we propose an adaptive edge resource allocation method based on multi-agent soft actor-critic with graph convolutional networks (SAC-GCN). Specifically, SAC-GCN models the multi-user metaverse environment as a graph where each agent is denoted by a node. Each agent learns the interplay between agents by graph convolutional networks with self-attention mechanism to further determine the resource usage for one user in the metaverse. The effectiveness of SAC-GCN is demonstrated through the analysis of user experience, balance of resource allocation, and resource utilization rate by taking a virtual city park metaverse as an example. Experimental results indicate that SAC-GCN outperforms other resource allocation methods in improving overall user experience, balancing resource allocation, and increasing resource utilization rate by at least 27%, 11%, and 8%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
