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：相较于前四代移动通信系统主要聚焦于宽带和覆盖范围，第五代移动通信系统（5G）将无线蜂窝系统的应用范畴拓展至两种新型连接模式：大规模机器型通信（mMTC）与超可靠低延迟通信（URLLC）。本文旨在探讨这两种连接模式在第六代移动通信系统（6G）框架下的可能演进路径。全文分为三部分：第一部分聚焦大规模设备连接问题。尽管5G中mMTC的研究主要围绕海量设备上行链路无协调接入的挑战，但6G中的流量模式可能趋于对称，从而催生闭环式大规模连接。分布式学习/推理技术正是推动这一演进的关键驱动力之一。第二部分将探讨关键业务无线连接的发展趋势。当5G中延迟与可靠性呈现强耦合特征时，6G将支持具有不同时序要求的多种安全关键控制应用，例如与信息新鲜度和信息价值相关的新兴指标的出现。此外，为满足安全关键控制应用的超高可靠性需求，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。实现这些严苛要求需要融合优化理论、可解释人工智能、生成式人工智能和数字孪生等新型AI技术。第三部分将分析大规模连接与关键业务的共存问题。我们将探讨海量设备需同时支持混合关键性流量模式的场景，并就此展开关于不同关键性业务共享无线资源管理策略的讨论。