Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces
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Abstract (arXiv)
A realistic performance assessment of any wireless technology requires the use of a channel model that reflects its main characteristics. The independent and identically distributed Rayleigh fading channel model has been (and still is) the basis of most theoretical research on multiple antenna technologies in scattering environments. This letter shows that such a model is not physically appearing when using a reconfigurable intelligent surface (RIS) with rectangular geometry and provides an alternative physically feasible Rayleigh fading model that can be used as a baseline when evaluating RIS-aided communications. The model is used to revisit the basic RIS properties, e.g., the rank of spatial correlation matrices and channel hardening.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Sep 2020.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 1; 2025: 3.
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- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2021Active Sensing for Communications by Learningcited by 9
- 2025Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisitioncited by 3
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approachcited by 2
- 2025Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systemscited by 2
- 2021Channel Estimation and Hybrid Architectures for RIS-Assisted Communicationscited by 1
- 2021Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairmentscited by 1
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networkscited by 1
- 2022Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learningcited by 0
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approachcited by 0
- 2023Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representationscited by 0
- 2024Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surfacecited by 0
- 2025Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices13 Jul 2025
- 2025Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systems15 Jun 2025
- 2025Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition14 Jan 2025
- 2024Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface6 Feb 2024
- 2023Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations2 Jun 2023
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach11 Apr 2023
- 2022Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning17 Sep 2022
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks1 Sep 2022
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach13 Apr 2022
- 2021Active Sensing for Communications by Learning8 Dec 2021
- 2021Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments13 Jul 2021
1 more
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfacescited by 12
- 2021Active Sensing for Communications by Learningcited by 9
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approachcited by 2
- 2021Channel Estimation and Hybrid Architectures for RIS-Assisted Communicationscited by 1
- 2021Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairmentscited by 1
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networkscited by 1
- 2022Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learningcited by 0
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approachcited by 0
- 2023Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representationscited by 0
- 2024Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surfacecited by 0
- 2025Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devicescited by 0
- 2025Lightweight Deep Learning-Based Channel Estimation for RIS-Aided Extremely Large-Scale MIMO Systems on Resource-Limited Edge Devices13 Jul 2025
- 2024Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface6 Feb 2024
- 2023Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations2 Jun 2023
- 2023Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach11 Apr 2023
- 2022Reconfigurable Intelligent Surface-assisted Classification of Modulations using Deep Learning17 Sep 2022
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks1 Sep 2022
- 2022Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces8 May 2022
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach13 Apr 2022
- 2021Active Sensing for Communications by Learning8 Dec 2021
- 2021Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments13 Jul 2021
- 2021Channel Estimation and Hybrid Architectures for RIS-Assisted Communications14 Apr 2021