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：与主要关注宽带和覆盖范围的4G及之前各代移动通信相比，5G已将无线蜂窝系统的范畴扩展至包含两种新型连接：海量机器类通信（mMTC）和超可靠低时延通信（URLLC）。本文将讨论在6G无线系统框架下这两种连接类型的可能演进。本文由三部分组成。第一部分探讨面向海量设备的连接。虽然5G中的mMTC研究主要集中于大量设备在上行链路中的非协调接入问题，但6G中的流量模式可能变得更加对称，从而催生闭环式海量连接。其驱动因素之一是分布式学习/推理。本文第二部分将讨论面向关键服务的无线连接演进。在5G中，时延与可靠性紧密耦合，而6G将支持具有不同类型时序要求的各种安全关键控制应用，这体现在与信息新鲜度和信息价值相关指标的出现。此外，为确保安全关键控制应用具备超高可靠性，需要对无线信道、队列长度和时延的尾部分布统计进行建模与估计。满足这些严苛要求需要开发新型的基于人工智能的技术，融合优化理论、可解释人工智能、生成式人工智能和数字孪生。第三部分将分析海量连接与关键服务的共存问题。我们将考虑海量设备需要支持混合关键性流量模式的场景，并随后讨论由不同关键性服务共享的无线资源管理问题。