# Reconfigurable Intelligent Surfaces: Principles and Opportunities

arXiv: https://arxiv.org/abs/2007.03435 · HTML: https://mlsanity.com/paper/2007.03435/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 30 of 35)

- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11318/) (2024)
- [Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning](https://mlsanity.com/paper/2207.08057/) (2022)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation](https://mlsanity.com/paper/2407.13123/) (2024)
- [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)
- [Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin](https://mlsanity.com/paper/2401.08023/) (2024)
- [Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfaces](https://mlsanity.com/paper/2104.00075/) (2021)
- [Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach](https://mlsanity.com/paper/2204.06390/) (2022)
- [Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Tasks](https://mlsanity.com/paper/2602.06819/) (2026)
- [DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks](https://mlsanity.com/paper/2209.00511/) (2022)
- [An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models](https://mlsanity.com/paper/2405.17439/) (2024)
- [Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach](https://mlsanity.com/paper/2406.13280/) (2024)
- [A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs](https://mlsanity.com/paper/2505.04401/) (2025)
- [Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems](https://mlsanity.com/paper/2506.03586/) (2025)
- [Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems](https://mlsanity.com/paper/2506.22448/) (2025)
- [RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting](https://mlsanity.com/paper/2508.16849/) (2025)
- [RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches](https://mlsanity.com/paper/2511.20305/) (2025)
- [Nearest neighbor Methods and their Applications in Design of 5G & Beyond Wireless Networks](https://mlsanity.com/paper/2107.07869/) (2021)
- [A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems](https://mlsanity.com/paper/2302.14706/) (2023)
- [Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach](https://mlsanity.com/paper/2308.13616/) (2023)
- [Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning](https://mlsanity.com/paper/2310.10330/) (2023)
- [Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging](https://mlsanity.com/paper/2406.07387/) (2024)
- [Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation](https://mlsanity.com/paper/2506.05195/) (2025)
- [PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming](https://mlsanity.com/paper/2510.22948/) (2025)
- [RadioMapMotion: A Dataset and Baseline for Proactive Spatio-Temporal Radio Environment Prediction](https://mlsanity.com/paper/2511.17526/) (2025)
- [Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization](https://mlsanity.com/paper/2512.01202/) (2025)
- [Performance Comparison of Aerial RIS and STAR-RIS in 3D Wireless Environments](https://mlsanity.com/paper/2512.08755/) (2025)
- [Efficient Beamforming Optimization for STAR-RIS-Assisted Communications: A Gradient-Based Meta Learning Approach](https://mlsanity.com/paper/2512.17928/) (2025)
- [RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization](https://mlsanity.com/paper/2512.18788/) (2025)

## Abstract (arXiv)

Reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs), or large intelligent surfaces (LISs), have received significant attention for their potential to enhance the capacity and coverage of wireless networks by smartly reconfiguring the wireless propagation environment. Therefore, RISs are considered a promising technology for the sixth-generation (6G) of communication networks. In this context, we provide a comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies. We describe the basic principles of RISs both from physics and communications perspectives, based on which we present performance evaluation of multi-antenna assisted RIS systems. In addition, we systematically survey existing designs for RIS-enhanced wireless networks encompassing performance analysis, information theory, and performance optimization perspectives. Furthermore, we survey existing research contributions that apply machine learning for tackling challenges in dynamic scenarios, such as random fluctuations of wireless channels and user mobility in RIS-enhanced wireless networks. Last but not least, we identify major issues and research opportunities associated with the integration of RISs and other emerging technologies for applications to next-generation networks.