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

Sun Mao, Lei Liu, Kun Yang, F. Richard Yu, Duist Niyato, Chau Yuen

TL;DR

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).

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.

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

TL;DR

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).

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.
Paper Structure (16 sections, 8 theorems, 35 equations, 8 figures, 2 tables, 3 algorithms)

This paper contains 16 sections, 8 theorems, 35 equations, 8 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Local Processing Model
  4. STAR-RIS-aided Communication Model
  5. Edge Processing at Metaverse Server
  6. Problem Formulation
  7. Proposed Solution
  8. Transmit Power Control
  9. Transmit/Reflect Coefficient Matrix Optimization of STAR-RIS
  10. CPU-cycle Frequency Optimization of AR Users
  11. Computation Resource Allocation of BS
  12. Updation of Auxiliary Variable $y_k$
  13. Algorithm, Convergence, and Complexity
  14. Service Latency Minimization Problem for FDMA-based Systems
  15. Numerical Results
  16. ...and 1 more sections

Key Result

Lemma 1

Denoting $f(b)=-ba+\ln(b)+1$, it yields that

Figures (8)

  • Figure 1: System model.
  • Figure 2: Maximum service latency of AR users versus system bandwidth.
  • Figure 3: Maximum service latency of AR users versus maximum transmit power of AR users.
  • Figure 4: Maximum service latency of AR users versus computation model size.
  • Figure 5: Maximum service latency of AR users versus maximum computing frequency of BS.
  • ...and 3 more figures

Theorems & Definitions (8)

  • Lemma 1
  • Theorem 1
  • Theorem 2
  • Theorem 3
  • Theorem 4
  • Theorem 5
  • Theorem 6
  • Theorem 7