Intelligent reflecting surface design for 6G-assited Internet of things

Zhaorui Wang, Liang Liu, Hang Li, Shuguang Cui

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

In the era of the 6G cellular networks, the base station (BS) should be able to provide seamless connectivity for a large number of Internet of things (IoT) devices distributed in various areas. However, constrained by their locations, some IoT devices may possess unreliable communication channels to the BS due to the obstacles between them. It is a big obstacle on the roadmap towards the massively connected IoT networks in the era of 6G. To tackle this challenge, the possibility was studied to employ the newly emerging technique of intelligent reflecting surface (IRS) for enhancing the strength of the IoT devices’ signals at the BS. Specifically, the transmit power minimization problem in an uplink IoT communication system was focused on, where the overhead for channel estimation was taken into account. On one hand, for the case where the BS was equipped with a small or moderate number of antennas, an iterative algorithm was proposed to jointly optimize the user power allocation, the IRS reflecting coefficients, and the BS beamforming vectors. On the other hand, for the case where the BS had a massive number of antennas, i.e., massive multiple-input multiple-output (MIMO), a low-complexity design of the user power allocation, the IRS reflecting coefficients, and the BS beamforming vectors was provided in a closed-form. Numerical results validate the great potential of IRS for enhancing massive connectivity in the future 6G-assisted IoT networks.

Original languageEnglish
Pages (from-to)84-95
Number of pages12
JournalChinese Journal on Internet of Things
Volume4
Issue number2
DOIs
Publication statusPublished - 30 Jun 2020

Keywords

  • 6G
  • intelligent reflecting surface
  • Internet of things
  • massive MIMO technology
  • resource allocation

ASJC Scopus subject areas

  • Information Systems
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Networks and Communications

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