Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems towards embracing two new types of connectivity: massive machine-type communication (mMTC) and ultra-reliable low-latency communications (URLLC). This paper will discuss the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. The paper consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G was predominantly focused on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this is distributed learning/inference. The second part of the paper will discuss the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. Additionally, ensuring ultra-high reliability for safety critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel AI-based techniques, incorporating optimization theory, explainable AI, generative AI and digital twins. The third part will analyze the coexistence of massive connectivity and critical services. We will consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This will be followed by a discussion about the management of wireless resources shared by services with different criticality.

翻译：相较于主要聚焦于宽带和覆盖增强的前四代（至4G）移动通信系统，5G将无线蜂窝系统的应用范围扩展至两种新型连接模式：大规模机器类通信（mMTC）与超可靠低延迟通信（URLLC）。本文旨在探讨这两类连接模式在6G无线系统框架下的潜在演进方向。全文分为三个部分：第一部分聚焦海量设备的连接问题。尽管5G mMTC研究主要侧重于大量设备的上行非协调接入问题，但6G的流量模式可能趋于对称化，进而催生闭环式大规模连接。分布式学习/推理正是推动这一演进的关键因素之一。第二部分讨论关键服务的无线连接技术演进。与5G中延迟与可靠性紧密耦合的特性不同，6G将支持具有多种时序要求的安全关键控制应用，这体现为信息新鲜度与信息价值等新型指标的出现。此外，为保障安全关键控制应用的超可靠性能，需对无线信道、队列长度及延迟的尾部统计特性进行建模与估计。满足这些严苛要求需要开发融合优化理论、可解释人工智能、生成式人工智能与数字孪生的新型AI技术。第三部分将分析大规模连接与关键服务的共存问题。我们将考虑海量设备需支持混合关键性流量模式的场景，并以此为基础探讨不同关键性服务共享无线资源的管理策略。