Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques
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Abstract (arXiv)
The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2022.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2025Integrating Stacked Intelligent Metasurfaces and Power Control for Dynamic Edge Inference via Over-The-Air Neural Networkscited by 2
- 2023Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfacescited by 1
- 2022Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methodscited by 0
- 2023Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennascited by 0
- 2025Over-the-Air Goal-Oriented Communicationscited by 0
- 2026Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approachcited by 0
- 2026Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach30 Jun 2026
- 2025Over-the-Air Goal-Oriented Communications23 Dec 2025
- 2025Integrating Stacked Intelligent Metasurfaces and Power Control for Dynamic Edge Inference via Over-The-Air Neural Networks23 Sep 2025
- 2023Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas30 Oct 2023
- 2023Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces18 May 2023
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2022Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods28 Apr 2022