Resource Allocation for STAR-RIS-enhanced Metaverse Systems with Augmented Reality

TL;DR

This paper proposes a resource management framework for STAR-RIS-assisted AR-enabled Metaverse systems, optimizing communication and computation resources to significantly reduce service latency in immersive AR experiences.

Contribution

It introduces a novel joint optimization approach for resource allocation in STAR-RIS-assisted Metaverse systems, including a tractable solution for minimizing latency.

Findings

Achieves significant latency reduction compared to benchmarks.

Effectively optimizes STAR-RIS configuration and resource allocation.

Demonstrates the effectiveness of the proposed joint optimization method.

Abstract

Augmented reality (AR)-enabled Metaverse is a promising technique to provide immersive service experience for mobile users. However, the limited network resources and unpredictable wireless propagation environments are key design bottlenecks of AR-enabled Metaverse systems. Therefore, this paper presents a resource management framework for simultaneously transmitting and reflecting RIS (STAR-RIS)-assisted AR-enabled Metaverse, where the STAR-RIS is configured to improve the communication efficiency between AR users and the Metaverse server located at the base station (BS). Moreover, we formulate a service latency minimization problem via jointly optimizing the computation resource allocation of the BS, coefficient matrix of the STAR-RIS, central processing unit (CPU) frequency and transmit power of the AR users. To tackle the non-convex problem, we utilize an approximate method to transform it to a tractable form, and decouple the multi-dimensional variables via the alternating optimization method. Particularly, the optimal coefficient matrix is obtained by a penalty function-based method with proved convergence, the CPU frequencies of AR users are derived as the closed-form solution, and the transmit power of AR users and computation resource allocation of the BS are obtained by the Lagrange duality method and convex optimization theory. Finally, simulation results demonstrates that the proposed method achieves remarkable latency reduction than several benchmark methods.