User Grouping and Reflective Beamforming for IRS-Aided URLLC

Hailiang Xie, Jie Xu, Ya Feng Liu, Liang Liu, Derrick Wing Kwan Ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


This letter studies an intelligent reflecting surface (IRS)-aided downlink ultra-reliable and low-latency communication (URLLC) system, in which an IRS is dedicatedly deployed to assist a base station (BS) to send individual short-packet messages to multiple users. To enhance the URLLC performance, the users are divided into different groups and the messages for users in each group are encoded into a single codeword. By considering the time division multiple access (TDMA) protocol among different groups, our objective is to minimize the total latency for all users subject to their individual reliability requirements, via jointly optimizing the user grouping and blocklength allocation at the BS together with the reflective beamforming at the IRS. We solve the latency minimization problem via the alternating optimization, in which the blocklengths and the reflective beamforming are optimized by using the techniques of successive convex approximation (SCA) and semi-definite relaxation (SDR), while the user grouping is updated by K-means and greedy-based methods. Numerical results show that the proposed designs can significantly reduce the communication latency, as compared to various benchmark schemes, which unveil the importance of user grouping and reflective beamforming optimization for exploiting the joint encoding gain and enhancing the worst-case user performance.

Original languageEnglish
Pages (from-to)2533-2537
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number11
Publication statusPublished - 1 Nov 2021


  • Ultra-reliable and low-latency communication (URLLC)
  • intelligent reflecting surface (IRS)
  • reflective beamforming
  • user grouping

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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