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、元宇宙及非同质化代币（NFTs）的融合带来了变革性数字机遇，同时也伴随着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。通过用户任务卸载，我们优化了数据任务、用户连接参数及服务器计算频率分配；在资源分配阶段，则优化了通信与计算资源分布，包括带宽、发射功率及计算频率。我们引入信任-成本比（TCR）这一关键指标，该指标将来自用户资源及服务器历史记录的信任评分与延迟和能耗成本相结合，从而确保用户持续参与及信任的平衡。本文核心的DASHF算法融合了Dinkelbach算法、交替优化、半定松弛（SDR）、匈牙利方法以及近期IEEE JSAC论文[2]中的新型分数规划技术。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），从而利用SDR与匈牙利算法求解。大量仿真验证了DASHF算法的有效性，揭示了增强区块链-元宇宙应用（特别是涉足NFTs的场景）的关键洞察。