Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS
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| Format: | Preprint |
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2025
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| _version_ | 1866909982945968128 |
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| author | Hammami, Cheima Magoarou, Luc Le del Hougne, Philipp |
| author_facet | Hammami, Cheima Magoarou, Luc Le del Hougne, Philipp |
| contents | 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, 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_01776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS Hammami, Cheima Magoarou, Luc Le del Hougne, Philipp Signal Processing Applied Physics 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, 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. |
| title | Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS |
| topic | Signal Processing Applied Physics |
| url | https://arxiv.org/abs/2508.01776 |