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

2026-02-19

AI Summary

This work addresses the challenge of excessive service latency in augmented reality (AR)-enabled metaverse systems, which arises from constrained network resources and uncertain wireless environments. To mitigate this issue, the paper proposes a STAR-RIS-assisted joint resource optimization framework that simultaneously optimizes base station computing resources, STAR-RIS reflection and transmission coefficients, AR users’ CPU frequencies, and transmit power to minimize end-to-end latency. Notably, this is the first study to integrate simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) into AR-metaverse scenarios. The authors develop a provably convergent penalty function method to solve the resulting non-convex coefficient optimization problem and derive a closed-form solution for CPU frequency allocation. Simulation results demonstrate that the proposed approach significantly outperforms existing benchmark schemes in reducing service latency.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Resource Allocation
• STAR-RIS
• Metaverse
• Augmented Reality
• Latency Minimization

Innovation

• STAR-RIS
• Metaverse
• Resource Allocation
• Latency Minimization
• Alternating Optimization