Low Power Consumption and Beam-Sustainable Reconfigurable Intelligent Surface for Fixed Wireless Communication at Millimeter-Wave 5G Band

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Abstract

We present a low-power-consumption and beamsustainable reconfigurable intelligent surface (RIS) for reliable millimeter-wave 5G fixed wireless communication in non-line-ofsight (NLOS) scenarios. Conventional RIS designs require continuous power to maintain beamforming, with power consumption divided into two main parts: power drawn by the control board and power dissipated by unit cells. The strategies for reducing power consumption differ based on the adjustable components used. In this study, a liquid crystal (LC)-based RIS is employed to achieve low power consumption, alongside a customized bias circuit that incorporates switches and capacitors. The proposed circuit operates using bias pulses, allowing charges to be stored in the capacitors, which in turn ensures beam sustainability. Moreover, owing to switches, the proposed RIS, consisting of an N × N array, requires only 2N bias nodes for twodimensional beamforming, unlike traditional RIS designs. This innovative biasing approach, referred to as an active-matrix scheme, introduces a low-power-consumption, beam-sustainable RIS for the first time.

Original languageEnglish
Title of host publication2025 IEEE/MTT-S International Microwave Symposium, IMS 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages307-310
Number of pages4
ISBN (Electronic)9798331514099
DOIs
StatePublished - 2025
Event2025 IEEE/MTT-S International Microwave Symposium, IMS 2025 - San Francisco, United States
Duration: 15 Jun 202520 Jun 2025

Publication series

NameIEEE MTT-S International Microwave Symposium Digest
ISSN (Print)0149-645X

Conference

Conference2025 IEEE/MTT-S International Microwave Symposium, IMS 2025
Country/TerritoryUnited States
CitySan Francisco
Period15/06/2520/06/25

Keywords

  • beamsustaining
  • low power consumption
  • millimeter-wave 5G
  • Reconfigurable intelligent surface (RIS)
  • reducing bias nodes

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