Physically-Consistent Modeling and Optimization of Non-Local RIS-Assisted Multi-User MISO Systems
Bibliographic record
Abstract
Mutual Coupling (MC) emerges as an inherent feature in Reconfigurable Intelligent Surface (RIS) structures, particularly when they are fabricated with sub-wavelength inter-element spacing. Hence, their realistic modeling and efficient optimization need to accurately incorporate MC-induced effects. In addition, the design of electromagnetics-compliant transmit/receive radiation patterns constitutes another critical factor for efficient RIS operation. These radiation patterns together with MC naturally lead to the emergence of non-local RIS structures, whose operation can be effectively described via non-diagonal phase configuration matrices. In this paper, we present a physically-consistent joint optimization framework for the MC and the radiation patterns of non-local RIS structures for the case of RIS-assisted multi-user Multiple-Input Single-Output (MISO) communication systems. Both conventional reflective as well as transmissive RIS setups are considered. Assuming the availability of statistical properties of the wireless environment for the targeted RIS deployment, we particularly devise a novel offline optimization approach for the static scattering S-parameters of the RIS, which is followed by a dynamic, per-channel-realization optimization of the metasurface’s response-tunable elements and the transmitter’s active precoder. Our extensive simulation results, using both parametric and geometric channel models, showcase the validity of the proposed two-step optimization framework over benchmark schemes, indicating that improved performance can be achievable without the need for optimizing the MC and the radiation patterns of the RIS on the fly, which can be rather cumbersome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".