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Smart and Reconfigurable Wireless Communications: From IRS Modeling to Algorithm Design

  • Xianghao Yu
  • , Vahid Jamali
  • , Dongfang Xu
  • , Derrick Wing Kwan Ng
  • , Robert Schober

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Intelligent reflecting surfaces (IRSs) have been introduced into wireless communications systems due to their great potential to smartly customize and reconfigure radio propagation environments in a cost-effective manner. Despite the promising advantages of IRSs, academic research on IRSs is still in its infancy. In particular, the design and analysis of IRS-assisted wireless communication systems critically depend on accurate and tractable modeling of the IRS. In this article, we first present and compare three IRS models, namely the conventional independent diffusive scatterer-based model, physics-based model, and impedance network-based model, in terms of their accuracy, tractability, and implementation simplicity. Furthermore, a new framework based on partitioning the IRS into tiles and employing codebooks of transmission modes is introduced to enable scalable IRS optimization. Then we investigate the impact of the three considered IRS models on system design, where several crucial technical challenges for the efficient design of IRS-assisted wireless systems are identified and the corresponding solutions are unraveled. Furthermore, to illustrate the properties of the considered models and the efficiency of the proposed solution concepts, IRS-assisted secure wireless systems and simultaneous wireless information and power transfer systems are studied in more detail. Finally, several promising future research directions for IRS-assisted wireless systems are highlighted.
Original languageEnglish
Pages (from-to)118-125
JournalIEEE Wireless Communications
Volume28
Issue number6
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes

Funding

This work was supported in part by the Chinese Scholarship Council (CSC), in part by funding from the UNSW Digital Grid Futures Institute, under a cross-disciplinary fund scheme and the Australian Research Council’s Discovery Project (DP210102169), and in part by the DFG Project SCHO 831/15-1.

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