标题： 基于多属性拍卖的车联网元宇宙双胞胎迁移资源分配：一种基于GPT的DRL方法 显示英文标题

标题： Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach

摘要： 车载元宇宙被开发以通过连接车辆和路边基础设施（例如，路侧单元（RSUs））提供沉浸式和安全的体验，从而增强现代汽车工业。 为了与虚拟空间无缝同步，车辆孪生体（VTs）被构建为物理实体的数字表示。 然而，资源密集型的VTs更新以及车辆的高移动性需要大量的计算、通信和存储资源，尤其是在RSUs覆盖范围有限的情况下进行迁移时更为明显。 为了解决这些问题，我们提出了一种基于属性感知的拍卖机制，在考虑价格和非货币属性（例如，位置和声誉）的情况下优化VTs迁移期间的资源分配。 在该机制中，我们提出了一个多属性资源市场中车辆用户和元宇宙服务提供商的两阶段匹配。 首先，资源属性匹配算法获得资源属性的完美匹配，即买方和卖方可以参与双荷兰拍卖（DDA）。 然后，我们使用基于生成式预训练变换器（GPT）的深度强化学习（DRL）算法训练一个DDA拍卖师，以在拍卖过程中高效调整拍卖钟。 我们在不同设置下将社会福利和拍卖信息交换成本与最先进的基线进行了比较。 仿真结果表明，我们提出的基于GPT的DRL拍卖方案比其他方案表现更好。 显示英文摘要

摘要： Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles and roadside infrastructures, e.g., RoadSide Units (RSUs). For seamless synchronization with virtual spaces, Vehicle Twins (VTs) are constructed as digital representations of physical entities. However, resource-intensive VTs updating and high mobility of vehicles require intensive computation, communication, and storage resources, especially for their migration among RSUs with limited coverages. To address these issues, we propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration by considering both price and non-monetary attributes, e.g., location and reputation. In this mechanism, we propose a two-stage matching for vehicular users and Metaverse service providers in multi-attribute resource markets. First, the resource attributes matching algorithm obtains the resource attributes perfect matching, namely, buyers and sellers can participate in a double Dutch auction (DDA). Then, we train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently during the auction process. We compare the performance of social welfare and auction information exchange costs with state-of-the-art baselines under different settings. Simulation results show that our proposed GPT-based DRL auction schemes have better performance than others.