Cited by 1,035Advanced Wireless Communication TechnologiesAdvanced Antenna and Metasurface TechnologiesMillimeter-Wave Propagation and Modeling

Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey

Open access
Gong, Shimin, Lu, Xiao, Hoang, Dinh Thai, Niyato, Dusit, Shu, Lei, Kim, Dong In, Liang, Ying-Chang·2020·IEEE Communications Surveys & Tutorials

Abstract

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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Crossref · 10.1109/comst.2020.3004197

Cites

192 of 192 references verified0 citation errors · 0 not checkable

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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 electromagnetic waves with the metasurface layers.

    “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]

    The Theory and Design section (p. 5) says metasurface elements transform incident electromagnetic waves, and Typical Tunable Functions (p. 7) describes controlling elements to produce desired wave patterns. The copy does not support the more specific claim about a multilayer SIM trained by error backpropagation through meta-atom transmission coefficients; the relevant passages discuss reflecting surfaces and phase control instead. (LLM, conf=0.85)

    Experimental — accuracy not yet measured
  • In HOENN, the power-efficient SIM can preprocess spatial EM waves, reducing the scale requirements and computation load for the electronic neuron network.

    “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]

    The ‘Analog Computing’ passage (p. 8) says a metasurface can perform operations on incident waves with higher energy efficiency than conventional digital signal processing. It does not describe HOENN or a SIM preprocessing spatial EM waves to reduce an electronic neural network’s size or computational load. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Cites

Flagged first, then by citations

1 of the 193 references couldn’t be matched to a known paper.

Research works printed but not linked: 12

  1. [13][13] C. Huang, S. Hu, G. C. Alexandropoulos, A. Zappone, C. Yuen, R. Zhang, M. D. Renzo, and M. Debbah, 'Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends,' 2019. [Online]. Available: https://arxiv.org/abs/1911.12296. - · arXiv
  2. [23][23] P. del Hougne, M. Fink, and G. Lerosey, 'Optimal communication channels in a disordered world with tamed randomness,' Nature Electronics , vol. 2, pp. 36-41, Jan. 2019. -
  3. [28][28] S. Y. Park and D. I. Kim, 'Intelligent reflecting surface-aided phaseshift backscatter communication,' in proc. Int. Conf. Ubiquit. Inf. Manag. Commun. (IMCOM) , Jan. 2020, pp. 1-5. -
  4. [29][29] Q. Zhang, Y.-C. Liang, and H. V. Poor, 'Large intelligent surface/antennas (LISA) assisted symbiotic radio for iot communications,' 2020. [Online]. Available: https://arxiv.org/abs/2002.00340. - · arXiv
  5. [86][86] W. Tang, M. Z. Chen, X. Chen, J. Y. Dai, Y. Han, M. D. Renzo, Y. Zeng, S. Jin, Q. Cheng, and T. J. Cui, 'Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement,' 2019. [Online]. Available: https://arxiv.org/abs/1911.05326 - · arXiv
  6. [121][121] P. Wang, J. Fang, X. Yuan, Z. D. Chen, H. Duan, and H. Li, 'Intelligent reflecting surface-assisted millimeter wave communications: Joint active and passive precoding design,' 2019. [Online]. Available: https://arxiv.org/abs/1908.10734 - · arXiv
  7. [127][127] S. Abeywickrama, R. Zhang, and C. Yuen, 'Intelligent reflecting surface: Practical phase shift model and beamforming optimization,' IEEE Trans. Commun. , 2020. -
  8. [152][152] Y. Li, M. Jiang, Q. Zhang, and J. Qin, 'Joint beamforming design in multi-cluster MISO NOMA intelligent reflecting surface-aided downlink communication networks,' ArXiv , vol. abs/1909.06972, 2019. [Online]. Available: https://arxiv.org/abs/1909.06972 - · arXiv
  9. [173][173] X. Lu, E. Hossain, T. Shafique, S. Feng, H. Jiang, and D. Niyato, 'Intelligent reflecting surface (IRS)-enabled covert communications in wireless networks,' to appear in IEEE Network , 2020. -
  10. [186][186] Q. Wu and R. Zhang, 'Joint active and passive beamforming optimization for intelligent reflecting surface assisted SWIPT under QoS constraints,' to appear in IEEE J. Sel. Area. Commun. , 2020. -
  11. [189][189] Q. Zhang, W. Saad, and M. Bennis, 'Reflections in the sky: Millimeter wave communication with UAV-carried intelligent reflectors,' in proc. IEEE GLOBECOM , Dec. 2019. -
  12. [200][200] N. Van Huynh, D. T. Hoang, X. Lu, D. Niyato, P. Wang, and D. I. Kim, 'Ambient backscatter communications: A contemporary survey,' IEEE Commun. Surv. Tut. , vol. 20, no. 4, pp. 2889-2922, Fourthquarter 2018. -

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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 · 2019: 1
FWCI
53.77
Type
journal article
Language
en
Open access
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 › Aerospace 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

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