Metaverse aims for building a fully immersive virtual shared space, where the users are able to engage in various activities. To successfully deploy the service for each user, the Metaverse service provider and network service provider generally localise the user first and then support the communication between the base station (BS) and the user. A reconfigurable intelligent surface (RIS) is capable of creating a reflected link between the BS and the user to enhance line-of-sight. Furthermore, the new key performance indicators (KPIs) in Metaverse, such as its energy-consumption-dependent total service cost and transmission latency, are often overlooked in ultra-reliable low latency communication (URLLC) designs, which have to be carefully considered in next-generation URLLC (xURLLC) regimes. In this paper, our design objective is to jointly optimise the transmit power, the RIS phase shifts, and the decoding error probability to simultaneously minimise the total service cost and transmission latency and approach the Pareto Front (PF). We conceive a twin-stage central controller, which aims for localising the users first and then supports the communication between the BS and users. In the first stage, we localise the Metaverse users, where the stochastic gradient descent (SGD) algorithm is invoked for accurate user localisation. In the second stage, a meta-learning-based position-dependent multi-objective soft actor and critic (MO-SAC) algorithm is proposed to approach the PF between the total service cost and transmission latency and to further optimise the latency-dependent reliability. Our numerical results demonstrate that ...

翻译：元宇宙旨在构建一个完全沉浸式的虚拟共享空间，使用户能够参与各种活动。为了成功为每位用户部署服务，元宇宙服务提供商和网络服务提供商通常首先定位用户，然后支持基站（BS）与用户之间的通信。可重构智能表面（RIS）能够在BS与用户之间创建反射链路，以增强视距传输。此外，元宇宙中的新关键性能指标（KPI），例如其能耗相关的总服务成本和传输延迟，在超可靠低延迟通信（URLLC）设计中常被忽视，而在下一代URLLC（xURLLC）体制中必须加以仔细考虑。在本文中，我们的设计目标是联合优化发射功率、RIS相位偏移和解码错误概率，以同时最小化总服务成本和传输延迟，并逼近帕累托前沿（PF）。我们构思了一个双阶段中央控制器，首先定位用户，然后支持BS与用户之间的通信。在第一阶段，我们定位元宇宙用户，其中调用随机梯度下降（SGD）算法以实现精确的用户定位。在第二阶段，提出了一种基于元学习的位置依赖多目标软演员-评论家（MO-SAC）算法，以逼近总服务成本和传输延迟之间的PF，并进一步优化延迟相关的可靠性。我们的数值结果表明...