Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
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Abstract (arXiv)
Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Nov 2019.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 3.
9 papers.
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2021CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surfacecited by 3
- 2021Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communicationscited by 1
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMAcited by 1
- 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfacescited by 0
- 2025Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systemscited by 0
- 2026RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modelingcited by 0
- 2026Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systemscited by 0
- 2026RAOA: Alternating-Operator Neural Computation with Programmable Radio Propagationcited by 0
- 2026RAOA: Alternating-Operator Neural Computation with Programmable Radio Propagation2 Oct 2026
- 2026Hybrid-Field Sparse Channel Representation and Recovery for XL-RIS-Assisted mmWave MIMO Systems26 Jul 2026
- 2026RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling28 May 2026
- 2025Neural Channel Knowledge Map Assisted Scheduling Optimization of Active IRSs in Multi-User Systems9 Aug 2025
- 2024Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces22 Jan 2024
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2021Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA3 Apr 2021
- 2021Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications1 Apr 2021
- 2021CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface22 Feb 2021