论文标题
无线元的机器学习：基本面，用例和未来方向
Machine Learning for Wireless Metaverse: Fundamentals, Use Case, and Future Directions
论文作者
论文摘要
当今的无线系统在服务质量和体验质量方面面临着关键的挑战。 Metavers具有重塑，转换和添加创新的潜力。元元是一个集体的虚拟开放空间，可以使用数字双胞胎，数字化身和交互式体验技术启用无线系统。机器学习（ML）对于对双胞胎，化身和部署交互式体验技术进行建模是必不可少的。在本文中，我们介绍了ML在启用元基于荟萃的无线系统中的作用。我们讨论了在基于荟萃分析的无线系统中推进ML的关键基本概念。此外，我们提出了一项案例研究，以进行元式感测深的增强学习。最后，我们讨论未来的方向以及潜在的解决方案。
Today's wireless systems are posing key challenges in terms of quality of service and quality of physical experience. Metaverse has the potential to reshape, transform, and add innovations to the existing wireless systems. A metaverse is a collective virtual open space that can enable wireless systems using digital twins, digital avatars, and interactive experience technologies. Machine learning (ML) is indispensable for modeling twins, avatars, and deploying interactive experience technologies. In this paper, we present the role of ML in enabling metaverse-based wireless systems. We discuss key fundamental concepts for advancing ML in the metaverse-based wireless systems. Moreover, we present a case study of deep reinforcement learning for metaverse sensing. Finally, we discuss the future directions along with potential solutions.
