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.

翻译：区块链、元宇宙与非同质化代币（NFT）的融合在带来变革性数字机遇的同时，也引发了隐私与资源管理等挑战。针对这些问题，本文聚焦于以NFT为核心、区块链赋能的元宇宙中的用户连接与资源分配优化。通过用户工作卸载机制，我们优化了数据任务、用户连接参数及服务器计算频分方案。在资源分配阶段，我们优化了通信-计算资源分配，包括带宽、发射功率与计算频率。本文引入了信任-成本比（TCR）这一关键指标，该指标综合了用户资源与服务器历史记录的信任评分以及时延与能耗成本，其平衡性确保了用户参与的持续性与系统可信度。我们方法的核心是DASHF算法，该算法整合了Dinkelbach算法、交替优化、半定松弛（SDR）、匈牙利算法以及近期IEEE JSAC论文[2]提出的一种新颖分式规划技术。DASHF最具挑战性的部分在于通过精心设计的变换，将优化问题重构为二次约束二次规划（QCQP），从而能够通过SDR与匈牙利算法求解。大量仿真实验验证了DASHF算法的有效性，并为增强区块链-元宇宙应用（尤其是NFT相关应用）提供了重要启示。