On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels
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Abstract (arXiv)
We analytically derive from first physical principles the functional dependence of wireless channels on the RIS configuration for generic (i.e., potentially complex-scattering) RIS-parametrized radio environments. The wireless channel is a linear input-output relation that depends non-linearly on the RIS configuration because of two independent mechanisms: i) proximity-induced mutual coupling between close-by RIS elements; ii) reverberation-induced long-range coupling between all RIS elements. Mathematically, this "structural" non-linearity originates from the inversion of an "interaction" matrix that can be cast as the sum of an infinite Born series [for i)] or Born-like series [for ii)] whose Kth term physically represents paths involving K bounces between the RIS elements [for i)] or wireless entities [for ii)]. We identify the key physical parameters that determine whether these series can be truncated after the first and second term, respectively, as tacitly done in common cascaded models of RIS-parametrized wireless channels. Numerical results obtained with the physics-compliant PhysFad model and experimental results obtained with a RIS prototype in an anechoic (echo-free) chamber and rich-scattering reverberation chambers corroborate our analysis. Our findings raise doubts about the reliability of existing performance analysis and channel-estimation protocols for cases in which cascaded models poorly describe the physical reality.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2023.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.
6 papers.
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- 2026Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces27 Aug 2026
- 2026Over-The-Air Extreme Learning Machines with XL Reception via Nonlinear Cascaded Metasurfaces25 Jan 2026
- 2025RIS-Enabled Smart Wireless Environments: Fundamentals and Distributed Optimization21 Dec 2025
- 2025Programmable metasurfaces for future photonic artificial intelligence16 May 2025
- 2024On the Detection of Non-Cooperative RISs: Scan B-Testing via Deep Support Vector Data Description5 Nov 2024
- 2023Backpropagation-free Training of Deep Physical Neural Networks20 Apr 2023