论文标题
在无线通信的元元中，异步混合增强学习的潜伏期和可靠性优化
Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications
论文作者
论文摘要
无线通信和高性能扩展现实（XR）方面的技术进步赋予了元发展的发展。对元应用的需求以及实际的数字化现实场景的实时数字孪生正在增加。然而，将2D物理世界图像复制到3D虚拟对象上是计算密集型的，需要计算卸载。传输对象维度（2D而非3D）的差异导致上行链路（UL）和下行链路（DL）中的不对称数据大小。为了确保系统的可靠性和低潜伏期，我们考虑了一个异步的关节UL-DL方案，在UL阶段，由多个扩展现实用户（XUS）捕获的物理世界图像的较小数据大小将被上传到元控制台（MC），以解释和呈现。在DL阶段，大小的3D虚拟对象需要传输到XUS。我们设计了一种新型的多代理增强学习算法结构，即异步参与者混合评论家（AAHC），以优化与计算阶段的计算卸载和通道分配有关的决策，并优化DL阶段的DL传输功率。广泛的实验表明，与拟议的基准相比，AAHC以令人满意的训练时间获得了更好的解决方案。
Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual objects is computationally intensive and requires computation offloading. The disparity in transmitted object dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we consider an asynchronous joint UL-DL scenario where in the UL stage, the smaller data size of the physical world images captured by multiple extended reality users (XUs) will be uploaded to the Metaverse Console (MC) to be construed and rendered. In the DL stage, the larger-size 3D virtual objects need to be transmitted back to the XUs. We design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC), to optimize the decisions pertaining to computation offloading and channel assignment in the UL stage and optimize the DL transmission power in the DL stage. Extensive experiments demonstrate that compared to proposed baselines, AAHC obtains better solutions with satisfactory training time.
