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Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS
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DOI：10.1109/LWC.2026.3652464.png)
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This Letter addresses the physics-consistent optimization of reconfigurable intelligent surfaces (RISs) with mutual coupling (MC) and 1-bit-programmable RIS elements. This combination of constraints is typical of current prototypes but unexplored in theoretical work. First, we present a simple statistical generator for multiport-network-theory (MNT) parameters of rich-scattering, RIS-parametrized channels. We account for reciprocity, passivity, and coherent backscattering; then, we add a simple hyper-parameter to control the MC strength. Second, we benchmark model-agnostic (dictionary search, coordinate descent, genetic algorithm) and model-based (temperature-annealed back-propagation) strategies under varying MC strength, with and without intelligent initialization. Except when MC is negligible, coordinate descent with random initialization offers the best trade-off in performance, runtime, and memory. Our insights can guide wireless practitioners who optimize RIS prototypes and other reconfigurable wave systems.
Keyword:
Reconfigurable intelligent surface
multi-port network theory
1-bit programmability
hardware constraint
coherent backscattering
multi-fidelity optimization
期刊
I
IF:
5.5
论文数:
797
被引数:
0
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PhysFad: Physics-Based End-to-End Channel Modeling of RIS-Parametrized Environments With Adjustable FadingPhysFad: 具有可调衰落的RIS参数化环境的基于物理的端到端信道建模
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