mlsanityData to 7 Oct 2026

Reconfigurable Intelligent Surfaces: Principles and Opportunities

arXiv 2007.03435 ↗First posted 7 Jul 2020eess.SPcited by 34

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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.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 8; 2025: 13; 2026: 5.

34 papers.

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  1. 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 12
  2. 2024Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learningcited by 11
  3. 2022Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learningcited by 7
  4. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  5. 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocationcited by 5
  6. 2024Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Datacited by 4
  7. 2024Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twincited by 3
  8. 2021Ultra-Reliable Indoor Millimeter Wave Communications using Multiple Artificial Intelligence-Powered Intelligent Surfacescited by 2
  9. 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approachcited by 2
  10. 2026Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Taskscited by 2
  11. 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networkscited by 1
  12. 2024An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Modelscited by 1
22 more
  1. 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approachcited by 1
  2. 2025A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISscited by 1
  3. 2025Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systemscited by 1
  4. 2025Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systemscited by 1
  5. 2025RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splattingcited by 1
  6. 2025RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approachescited by 1
  7. 2021Nearest neighbor Methods and their Applications in Design of 5G & Beyond Wireless Networkscited by 0
  8. 2023A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systemscited by 0
  9. 2023Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approachcited by 0
  10. 2023Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planningcited by 0
  11. 2024Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Agingcited by 0
  12. 2025Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimationcited by 0
  13. 2025PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamformingcited by 0
  14. 2025RadioMapMotion: A Dataset and Baseline for Proactive Spatio-Temporal Radio Environment Predictioncited by 0
  15. 2025Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimizationcited by 0
  16. 2025Performance Comparison of Aerial RIS and STAR-RIS in 3D Wireless Environmentscited by 0
  17. 2025Efficient Beamforming Optimization for STAR-RIS-Assisted Communications: A Gradient-Based Meta Learning Approachcited by 0
  18. 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimizationcited by 0
  19. 2026Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approachcited by 0
  20. 2026RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networkscited by 0
  21. 2026Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approachcited by 0
  22. 2026A Particle-Swarm-Assisted Gradient Meta-Learning Algorithm for Joint Transmit Precoding and STAR-RIS Coefficient Optimizationcited by 0