Research and Analysis of URLLC Technology Based on Artificial Intelligence

Zhengyu Zhu, Gengwang Hou, Zheng Chu, Xingwang Li, Gangcan Sun, Wanming Hao, Paramjit S. Sehdev

Research output: Journal PublicationArticlepeer-review

7 Citations (Scopus)


Ultra-reliable low-latency communications (URLLC), as one of the three application scenarios of the fifth generation (5G) mobile networks, has attracted extensive attention in the industry. URLLC can play an important role in many scenarios such as traffic safety, remote surgery, and unmanned driving. However, the existing technology is not enough to fully meet its business requirements. Artificial intelligence (AI) is promising to solve the technical problems in the URLLC communication scenario. As a branch of machine learning, deep learning has made a lot of achievements in academia and industry in recent years. It has become one of the landmark technologies of AI. Deep learning can optimize the system end to end, effectively process massive data, and cope with complex channel changes. It is considered as one of the practical tools to deal with physical layer communication. In this article, we first introduce the performance indicators and critical technologies of URLLC in the physical layer. Then we expound on the advantages of deep learning in solving technical problems in the physical layer. Finally, the challenge and opportunity of URLLC based on AI in the sixth-generation (6G) mobile networks are presented.

Original languageEnglish
Article number9409834
Pages (from-to)37-43
Number of pages7
JournalIEEE Communications Standards Magazine
Issue number2
Publication statusPublished - Jun 2021
Externally publishedYes

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Law
  • Management of Technology and Innovation


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