Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis
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Abstract (arXiv)
Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electromagnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2020.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Meta-Learning Based Optimization for Large Scale Wireless Systemscited by 1
- 2025Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Designcited by 0
- 2025Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimizationcited by 0
- 2026Quantization-Aware EE Optimization and SE-EE Tradeoff for MiLAC-Aided MU-MISO Beamformingcited by 0
- 2026Quantization-Aware EE Optimization and SE-EE Tradeoff for MiLAC-Aided MU-MISO Beamforming27 Apr 2026
- 2025Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization17 Oct 2025
- 2025Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design21 Jul 2025
- 2024Meta-Learning Based Optimization for Large Scale Wireless Systems1 Jul 2024