Physically-consistent EM models-aware RIS-aided communication — A survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid development of reconfigurable intelligent surfaces (RISs) has sparked transformative advancements in wireless communication systems. These intelligent metasurfaces, adept at dynamically manipulating electromagnetic (EM) waves, hold vast potential for enhancing network capacity, coverage, and efficiency. However, to fully unleash the capabilities of RIS-aided communication systems, effective optimization is crucial. This article provides a recent development of RIS-assisted communication from the viewpoint of physically-consistent EM models. We delve into the realm of physically-consistent EM models, highlighting their pivotal role in achieving robust and efficient RIS designs. Furthermore, this paper offers a survey of the different optimization models utilized for RIS-assisted wireless communication systems, which consider various EM and physical aspects of RIS. We explore solution approaches aimed at optimizing different objectives like sum-rate/spectral efficiency and energy efficiency, spanning traditional optimization models to machine learning-based methods. Additionally, we discuss some open research issues in this field.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it