Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions
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Abstract (arXiv)
The search for physical-layer technologies that can play a key role in beyond-5G systems has started. One option is reconfigurable intelligent surfaces (RIS), which can collect wireless signals from a transmitter and passively beamform them towards the receiver. The technology has exciting prospects and is quickly gaining traction in the communication community, but in the current hype we have witnessed how several myths and overstatements are spreading in the literature. In this article, we take a neutral look at the RIS technology. We first review the fundamentals and then explain specific features that can be easily misinterpreted. In particular, we debunk three myths: 1) Current network technology can only control the transmitter and receiver, not the environment in between; 2) A better asymptotic array gain is achieved than with conventional beamforming; 3) The pathloss is the same as with anomalous mirrors. To inspire further research, we conclude by identifying two critical questions that must be answered for RIS to become a successful technology: 1) What is a convincing use case for RIS?; 2) How can we estimate channels and control an RIS in real time?
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMAcited by 1
- 2021Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computingcited by 0
- 2023Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planningcited by 0
- 2025Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devicescited by 0
- 2026A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networkscited by 0
- 2026A 3GPP-Compliant Benchmark Dataset for RIS-Aided Beyond 5G Networks30 Sep 2026
- 2025Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices13 Jul 2025
- 2023Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning16 Oct 2023
- 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfaces8 Dec 2021
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA3 Apr 2021
- 2021Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing6 Feb 2021