Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multilayer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This article contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to the following: 1) reliability theory; 2) short packet communications; 3) inequalities, distribution bounds, and tail approximations; 4) rare-events simulation; 5) queuing theory and information freshness; and 6) large-scale tools, such as stochastic geometry, clustering, compressed sensing, and mean-field (MF) games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout this article, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

中文翻译：

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超可靠低延迟通信的统计工具和方法”教程


超可靠低延迟通信 (URLLC) 构成第五代 (5G) 及以后蜂窝网络的关键服务类别。值得注意的是，设计和支持 URLLC 是一项艰巨的任务，因为根本需要识别和准确表征系统运行的底层统计模型，例如干扰统计、信道条件和协议行为。一般来说，为了提供强大的可靠性和延迟保证，不可避免地需要考虑所有潜在延迟和错误源的多层端到端方法以及适当的统计工具和方法。本文通过提供有关可用于设计和分析 URLLC 系统的几种统计工具和方法的教程，对后一方面的知识体系做出了贡献。具体来说，我们概述了与以下相关的框架：1）可靠性理论； 2）短包通信； 3）不等式、分布界限和尾部近似； 4）稀有事件模拟； 5）排队论和信息新鲜度； 6）大规模工具，例如随机几何、聚类、压缩感知和平均场（MF）游戏。此外，我们经常在讨论的工具/方法范围内提到著名的数据驱动算法。在本文中，我们简要回顾了使用所讨论的工具和方法的最先进的工作，以及它们与 URLLC 系统的链接。此外，我们还讨论了侧重于物理和媒体访问控制层的新颖应用示例。最后，强调了关键的研究挑战和方向，以阐明 URLLC 分析/设计研究在未来几年可能如何发展。