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 identify and discuss a set of key requirements for advancing ML in the metaverse-based wireless systems. Moreover, we present a case study of distributed split federated learning for efficiently training meta-space models. Finally, we discuss the future challenges along with potential solutions.
翻译：今天的无线系统在服务质量和物质体验质量方面正构成关键挑战。 元数据具有改造、改造和增加现有无线系统创新的潜力。 元数据是一个集体的虚拟开放空间,它能够利用数字双胞胎、数字变异体和互动经验技术,使无线系统能够使用无线系统。 机器学习(ML)对于模拟双胞胎、变异体和部署交互式经验技术必不可少。 在本文中,我们介绍了ML在促成基于元对流的无线系统方面的作用。 我们确定并讨论了在基于无线的无线系统中推进ML的一套关键要求。 此外,我们还介绍了为高效培训元空间模型而分散的分散化联合学习的案例研究。 最后,我们讨论了未来挑战以及潜在的解决方案。
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