AI-Assisted Low Information Latency Wireless Networking

Zhiyuan Jiang, Siyu Fu, Sheng Zhou*, Zhisheng Niu, Shunqing Zhang, Shugong Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

The 5G Phase-2 and beyond wireless systems will focus more on vertical applications such as autonomous driving and the Industrial Internet of Things, many of which are categorized as uRLLC. In this article, an alternative view of uRLLC is presented, information latency, measuring the distortion of information resulting from time lag of its acquisition process, which is more relevant than conventional communication latency of uRLLC in wireless networked control systems. An AI-assisted SMART is presented to address the information latency optimization challenge. Case studies of typical applications (i.e., dense platooning and intersection management) in AD are demonstrated, which show that SMART can effectively optimize information latency, and more importantly, information latency-optimized systems outperform conventional uRLLC-oriented systems significantly in terms of AD performance such as traffic efficiency, thus pointing out a new research and system design paradigm.

Original languageEnglish
Article number9023932
Pages (from-to)108-115
Number of pages8
JournalIEEE Wireless Communications
Volume27
Issue number1
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

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