RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials
Not on the map. Why?
Abstract (arXiv)
The prospects of using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems have recently received much attention from academia and industry. Most papers make theoretical studies based on elementary models, while the prototyping of RIS-aided wireless communication and real-world field trials are scarce. In this paper, we describe a new RIS prototype consisting of 1100 controllable elements working at 5.8 GHz band. We propose an efficient algorithm for configuring the RIS over the air by exploiting the geometrical array properties and a practical receiver-RIS feedback link. In our indoor test, where the transmitter and receiver are separated by a 30 cm thick concrete wall, our RIS prototype provides a 26 dB power gain compared to the baseline case where the RIS is replaced by a copper plate. A 27 dB power gain was observed in the short-distance outdoor measurement. We also carried out long-distance measurements and successfully transmitted a 32 Mbps data stream over 500 m. A 1080p video was live-streamed and it only played smoothly when the RIS was utilized. The power consumption of the RIS is around 1 W. Our paper is vivid proof that the RIS is a very promising technology for future wireless communications.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1; 2025: 3; 2026: 3.
12 papers.
- 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3
- 2022Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebookscited by 0
- 2022Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learningcited by 0
- 2023Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approachcited by 0
- 2023A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communicationscited by 0
- 2024TRGR: Transmissive RIS-aided Gait Recognition Through Wallscited by 0
- 2025Learning Beamforming Codebooks for Active Sensing with Reconfigurable Intelligent Surfacecited by 0
- 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimationcited by 0
- 2025Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networkscited by 0
- 2026RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networkscited by 0
- 2026Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systemscited 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
- 2026Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems26 Jul 2026
- 2026RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks25 Jun 2026
- 2025Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networks27 Aug 2025
- 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation5 Jun 2025
- 2025Learning Beamforming Codebooks for Active Sensing with Reconfigurable Intelligent Surface24 Mar 2025
- 2024TRGR: Transmissive RIS-aided Gait Recognition Through Walls31 Jul 2024
- 2023A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications11 Sep 2023
- 2023Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach25 Aug 2023
- 2022Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning17 Sep 2022
- 2022Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks7 May 2022
- 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfaces8 Dec 2021