为大量设备和关键服务提供无线 6G 连接

IF 25.9 1区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Proceedings of the IEEE Pub Date : 2024-11-05 DOI:10.1109/JPROC.2024.3484529
Anders E. Kalør;Giuseppe Durisi;Sinem Coleri;Stefan Parkvall;Wei Yu;Andreas Mueller;Petar Popovski
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引用次数: 0

摘要

与主要关注宽带和覆盖方面的4G之前的几代相比,5G扩展了无线蜂窝系统的范围,包括两种新的连接类型:大规模机器类型通信(mmtc)和超可靠的低延迟通信(urllc)。本文讨论了在6G无线系统的保护伞下这两种类型的连接的可能演变。本文由三部分组成。第一部分涉及大量设备的连接。5G的mMTC研究主要集中在大量设备的上行链路不协调接入问题上,而6G的流量模式可能会变得更加对称,从而导致闭环的大规模连接。这种交通模式的驱动因素之一是分布式/分散学习和推理。本文的第二部分将讨论关键服务的无线连接的发展。虽然5G的延迟和可靠性紧密耦合,但6G将支持各种具有不同时间要求的安全关键型控制应用,这一点可以从信息新鲜度和信息价值相关指标的出现中得到证明。此外,为了确保安全关键控制应用的超高可靠性,需要对无线信道、队列长度和延迟的尾部统计数据进行建模和估计。为了满足这些严格的要求,需要开发新的基于人工智能(AI)的技术,包括优化理论、可解释人工智能(XAI)、生成人工智能和数字孪生(dt)。第三部分分析了海量连接与关键业务的共存。具体来说,我们考虑了大量设备需要支持混合临界流量模式的场景。随后讨论了由不同关键服务共享的无线资源的管理。
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Wireless 6G Connectivity for Massive Number of Devices and Critical Services
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 toward embracing two new types of connectivity: massive machine-type communications (mMTCs) and ultrareliable low-latency communications (URLLCs). This article discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. This article consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses 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 type of traffic pattern is distributed/decentralized learning and inference. The second part of this article discusses 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. In addition, ensuring ultrahigh 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 artificial intelligence (AI)-based techniques, incorporating optimization theory, explainable AI (XAI), generative AI, and digital twins (DTs). The third part analyzes the coexistence of massive connectivity and critical services. Specifically, we consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.
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来源期刊
Proceedings of the IEEE
Proceedings of the IEEE 工程技术-工程:电子与电气
CiteScore
46.40
自引率
1.00%
发文量
160
审稿时长
3-8 weeks
期刊介绍: Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.
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