4.7 Article

Overhead-Aware Design of Reconfigurable Intelligent Surfaces in Smart Radio Environments

Journal

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 1, Pages 126-141

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3023578

Keywords

6G wireless networks; smart radio environments; reconfigurable intelligent surfaces; resource allocation

Funding

  1. European Commission through the H2020 ARIADNE project [675806]
  2. H2020 REDESIGN Project [789260]
  3. project Excellence Departments 2018-2022
  4. FAR fund of the DIEI department

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Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks, but a major challenge is the overhead required for channel state information estimation and optimized phase shift reporting. This study introduces an overhead-aware resource allocation framework for wireless networks using reconfigurable intelligent surfaces, optimizing system rate and energy efficiency with respect to various parameters while investigating the trade-off between optimized radio resource allocation policies and related overhead.
Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.

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