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Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey
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This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.
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“By adequately training and configuring the transmission coefficients of the meta-atoms using error backpropagation, a SIM is capable of performing desired functions through the interactions of spatial EM waves with the metasurface layers [15], [16].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [16]
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“In HOENN, the power-efficient SIM can preprocess spatial EM waves, reducing the scale requirements and the computation load for the electronic neuron network [16].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [16]
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Research works printed but not linked: 10
- lu, 2020, Intelligent Reflecting Surface (IRS)-Enabled Covert Communications in Wireless Networks
- 10.1109/GLOBECOM38437.2019.9013626 · DOI
- wu, 2019, Joint active and passive beamforming optimization for intelligent reflecting surface assisted SWIPT under QOS constraints
- zhang, 2020, Large Intelligent Surface/Antennas (LISA) Assisted Symbiotic Radio for IoT Communications
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Metadata
- Authors
- Gong, S., Lu, X., Hoang, D. T., Niyato, D., Shu, L., Kim, D. I., & Liang, Y.-C.
- Affiliations
- Sun Yat-sen University (CN)
- University of Alberta (CA)
- University of Technology Sydney (AU)
- Nanyang Technological University (SG)
- Nanjing Agricultural University (CN)
- Sungkyunkwan University (KR)
- University of Electronic Science and Technology of China (CN)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 4
- Pages
- 2283-2314
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3027118737
- Cited by
- 1,035
- References
- 193
- Citations by year
- 2026: 77 · 2025: 164 · 2024: 167 · 2023: 194 · 2022: 212 · 2021: 184 · 2020: 36
- FWCI
- 53.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61972434, U1801261)
- Shenzhen Basic Research Program (JCYJ20190807154009444)
- Shenzhen Talent Peacock Plan Program (KQTD2015071715073798)
- PCL Future Greater-Bay Area Network Facilities for Largescale Experiments and Applications (LZC0019)
- National Research Foundation (NRF), Singapore, through Singapore Energy Market Authority (EMA), Energy Resilience, Singapore (NRF2017EWT-EP003-041, NRF2015-NRF-ISF001-2277)
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure (NSoE DeSTSCI2019-0007)
- A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing (RGANS1906)
- Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (M4082187 (4080))
- National Research Foundation of Korea (NRF) Grant
- Korean Government (2014R1A5A1011478)
- Key Areas of Research and Development Program of Guangdong Province, China (2018B010114001)
- Central Universities Funding (ZYGX2019Z022)
- Higher Education Discipline Innovation Project (B20064)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Antenna and Metasurface Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Keywords
- reconfigurable intelligent surface, smart radio environments, phase shift control, rate maximization, power minimization, beyond 5G, wireless network design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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