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User Connection and Resource Allocation Optimization in Blockchain Empowered Metaverse over 6G Wireless Communications

Liangxin Qian, Chang Liu, Jun Zhao

TL;DR

This work tackles cross-layer optimization in a blockchain-enabled Metaverse that hosts NFT applications over 6G by jointly optimizing user associations, offloading ratios, and server computing-resource division under communication and blockchain processing constraints. It introduces the Trust-Cost Ratio (TCR) to balance trust and total delay/energy, and develops the DASHF algorithm, which fuses Dinkelbach fractional programming, alternating optimization, semidefinite relaxation, the Hungarian method, and a novel FP technique. The optimization problem is transformed to a QCQP and solved via SDR, with a convex FP-based second stage to handle nonconvexities, yielding a stationary solution. Extensive simulations confirm that DASHF outperforms baselines in TCR, resource efficiency, and convergence behavior, demonstrating practical gains for NFT-driven Metaverse services in future wireless networks.

Abstract

The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users' resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm's efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.

User Connection and Resource Allocation Optimization in Blockchain Empowered Metaverse over 6G Wireless Communications

TL;DR

This work tackles cross-layer optimization in a blockchain-enabled Metaverse that hosts NFT applications over 6G by jointly optimizing user associations, offloading ratios, and server computing-resource division under communication and blockchain processing constraints. It introduces the Trust-Cost Ratio (TCR) to balance trust and total delay/energy, and develops the DASHF algorithm, which fuses Dinkelbach fractional programming, alternating optimization, semidefinite relaxation, the Hungarian method, and a novel FP technique. The optimization problem is transformed to a QCQP and solved via SDR, with a convex FP-based second stage to handle nonconvexities, yielding a stationary solution. Extensive simulations confirm that DASHF outperforms baselines in TCR, resource efficiency, and convergence behavior, demonstrating practical gains for NFT-driven Metaverse services in future wireless networks.

Abstract

The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users' resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm's efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.
Paper Structure (22 sections, 52 equations, 6 figures, 2 tables, 2 algorithms)

This paper contains 22 sections, 52 equations, 6 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Main Contributions
  4. Blockchain-Empowered Metaverse System and Optimization Problem Formulation
  5. An Overview of the System
  6. The Detailed System Model
  7. Time consumption
  8. Energy consumption
  9. Trust score function
  10. Optimization Problem
  11. Our proposed AO technique to solve the optimization problem
  12. Optimizing $\bm{x}, \bm{\varphi},\bm{\gamma},T$, given $\bm{b},\bm{p_u},\bm{p_s},\bm{f_u},\bm{f_s}$
  13. Optimizing $\bm{b},\bm{p_u},\bm{p_s},\bm{f_u},\bm{f_s},T$, given $\bm{x}, \bm{\varphi},\bm{\gamma}$
  14. Prove that the maximization optimization of (\ref{['prob10']}) is equal to the maximization optimization of (\ref{['prob11']})
  15. Complexity Analysis of the Whole Algorithm
  16. ...and 7 more sections

Figures (6)

  • Figure 1: Optimizing the trust-cost ratio (TCR) of a system consisting of $N$ VR users and $M$ Metaverse servers by the joint optimization of user association, offloading ratio, server computing capacity division, and radio and computing resource allocation. The images in the NFT tasks come from https://monkeykingdom.io/monkeykingdom.
  • Figure 2: The whole communication and computation procedure.
  • Figure 3: 2D distribution of 3 servers and 20 users.
  • Figure 4: Convergence of the proposed Algorithms.
  • Figure 5: Resource consumption and TCR of different baselines and proposed AO Algorithm.
  • ...and 1 more figures