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ToolsOkogbaa, Fred Chimzi, Ahmed, Qasim Zeeshan, Khan, Fahd Ahmed, Abbas, Waqas Bin, Che, Fuhu, Zaidi, Syed Ali Raza and Alade, Temitope (2022) Design and application of intelligent reflecting surface (IRS) for beyond 5G wireless networks: A review. Sensors, 22 (7). ISSN 1424-8220
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Abstract
The existing sub-6 GHz band is insufficient to support the bandwidth requirement of emerging data-rate-hungry applications and Internet of Things devices, requiring ultrareliable low latency communication (URLLC), thus making the migration to millimeter-wave (mmWave) bands inevitable. A notable disadvantage of a mmWave band is the significant losses suffered at higher frequencies that may not be overcome by novel optimization algorithms at the transmitter and receiver and thus result in a performance degradation. To address this, Intelligent Reflecting Surface (IRS) is a new technology capable of transforming the wireless channel from a highly probabilistic to a highly deterministic channel and as a result, overcome the significant losses experienced in the mmWave band. This paper aims to survey the design and applications of an IRS, a 2-dimensional (2D) passive metasurface with the ability to control the wireless propagation channel and thus achieve better spectral efficiency (SE) and energy efficiency (EE) to aid the fifth and beyond generation to deliver the required data rate to support current and emerging technologies. It is imperative that the future wireless technology evolves toward an intelligent software paradigm, and the IRS is expected to be a key enabler in achieving this task. This work provides a detailed survey of the IRS technology, limitations in the current research, and the related research opportunities and possible solutions.
| Item Type: | Article |
|---|---|
| Additional Information: | Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Uncontrolled Keywords: | 5g networks,intelligent reflecting surfaces (irs),iot,mmwave,urllc,analytical chemistry,information systems,atomic and molecular physics, and optics,biochemistry,instrumentation,electrical and electronic engineering,sdg 7 - affordable and clean energy ,/dk/atira/pure/subjectarea/asjc/1600/1602 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 22 Nov 2023 04:39 |
| Last Modified: | 10 Dec 2024 01:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93713 |
| DOI: | 10.3390/s22072436 |
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