The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Reconfigurable Intelligent Surfaces: Principles and Opportunities
Open accessAbstract
Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs),1have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multiantenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks.1Without loss of generality, we use the name of RIS in the remainder of this paper.
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Cited by
Simultaneously Transmitting and Reflecting (STAR) RIS Aided Wireless Communications
Mu, X., Liu, Y., Guo, L., Lin, J., & Schober, R. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3118225 · Cited by 896
Self-citationZheng, B., You, C., Mei, W., & Zhang, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3155305 · Cited by 579
Cell-Free Massive MIMO: A Survey
Elhoushy, S., Ibrahim, M., & Hamouda, W. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3123267 · Cited by 448
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks
Liu, X., Liu, Y., & Chen, Y. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3041401 · Cited by 298
Self-citationRIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications
Shi, E., Zhang, J., Du, H., Ai, B., Yuen, C., Niyato, D., Letaief, K. B., & Shen, X. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3404491 · Cited by 224
Metadata
- Authors
- Liu, Y., Liu, X., Mu, X., Hou, T., Xu, J., Di Renzo, M., & Al-Dhahir, N.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2021
- Volume
- 23
- Issue
- 3
- Pages
- 1546-1577
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3159402647
- Cited by
- 1,847
- References
- 10
- FWCI
- 79.71
- Type
- journal article
- Language
- en
- Open access
- green
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