{"ID":2887347,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01776","arxiv_id":"2508.01776","title":"Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.01776","url_pdf":"https://arxiv.org/pdf/2508.01776v2","authors":"[\"Cheima Hammami\",\"Luc Le Magoarou\",\"Philipp del Hougne\"]","published":"2025-08-03T14:28:23Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"physics.app-ph\"]","methods":"[]","has_code":false}