# Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2005.00938 · HTML: https://mlsanity.com/paper/2005.00938/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2020.

## Built on by (most cited first, 9 of 9)

- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data](https://mlsanity.com/paper/2405.06299/) (2024)
- [Autoencoder-based Communications with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.04441/) (2021)
- [A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System](https://mlsanity.com/paper/2307.08474/) (2023)
- [Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System](https://mlsanity.com/paper/2402.09440/) (2024)
- [Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation](https://mlsanity.com/paper/2506.05195/) (2025)
- [Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model](https://mlsanity.com/paper/2509.11016/) (2025)

## 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.