Multi-Objective Optimization of URLLC-Based Metaverse Services
Multi-Objective Optimization of URLLC-Based Metaverse Services
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DOI：
10.1109/tcomm.2023.3300839
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发表时间：
2023-07
影响因子：
8.3
通讯作者：
Xinyu Gao;Wenqiang Yi;Yuanwei Liu;L. Hanzo
中科院分区：
文献类型：
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作者：
Xinyu Gao;Wenqiang Yi;Yuanwei Liu;L. Hanzo
元界旨在构建一个完全沉浸式的虚拟共享空间，让用户能够参与各种活动。为了成功地为每个用户部署服务，元界服务提供商和网络服务提供商通常首先对用户进行本地化，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 和用户之间创建反射链接，以增强视线。此外，Metaverse中新的关键性能指标（KPI），例如与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）设计中经常被忽视，而在下一代URLLC（xURLLC）体系中必须仔细考虑这些指标。在本文中，我们的设计目标是联合优化发射功率、RIS相移和解码错误概率，以同时最小化总服务成本和传输延迟并接近帕累托前沿（PF）。我们设想了一个双级中央控制器，其目的是首先定位用户，然后支持BS和用户之间的通信。在第一阶段，我们定位元界用户，其中调用随机梯度下降（SGD）算法来准确定位用户。在第二阶段，提出了一种基于元学习的位置相关的多目标软执行者和批评者（MO-SAC）算法，以接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：1）所提出的解决方案在总服务成本和传输延迟之间进行了权衡，为不同的实际场景提供了一组候选最优解决方案。 2）与基准测试相比，所提出的基于元学习的MO-SAC算法能够适应新的无线环境。 3）所描绘的近似PF发现了Metaverse KPI之间的关系，这为其部署提供了指导。
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 1) The proposed solution strikes a tradeoff between the total service cost and transmission latency, which provides a candidate group of optimal solutions for diverse practical scenarios. 2) The proposed meta-learning-based MO-SAC algorithm is capable of adaption to new wireless environments, compared to the benchmarkers. 3) The approximate PF depicted discovered the relationships among the KPIs for the Metaverse, which provides guidelines for its deployment.