Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
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Abstract (arXiv)
Recently there has been a flurry of research on the use of reconfigurable intelligent surfaces (RIS) in wireless networks to create smart radio environments. In a smart radio environment, surfaces are capable of manipulating the propagation of incident electromagnetic waves in a programmable manner to actively alter the channel realization, which turns the wireless channel into a controllable system block that can be optimized to improve overall system performance. In this article, we provide a tutorial overview of reconfigurable intelligent surfaces (RIS) for wireless communications. We describe the working principles of reconfigurable intelligent surfaces (RIS) and elaborate on different candidate implementations using metasurfaces and reflectarrays. We discuss the channel models suitable for both implementations and examine the feasibility of obtaining accurate channel estimates. Furthermore, we discuss the aspects that differentiate RIS optimization from precoding for traditional MIMO arrays highlighting both the arising challenges and the potential opportunities associated with this emerging technology. Finally, we present numerical results to illustrate the power of an RIS in shaping the key properties of a MIMO channel.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 4; 2025: 1.
8 papers.
- 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
- 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 12
- 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Datacited by 4
- 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3
- 2023A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing Systemcited by 2
- 2024Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC Systemcited by 1
- 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimationcited by 0
- 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation5 Jun 2025
- 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks17 Jun 2024
- 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data10 May 2024
- 2024Large Language Models for UAVs: Current State and Pathways to the Future2 May 2024
- 2024Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System29 Jan 2024
- 2023A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System17 Jul 2023
- 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfaces8 Dec 2021
- 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges14 Nov 2021