Propagation Modeling for Reconfigurable Intelligent Surface-Enabled Links Based on an Effective Complex Radar Cross-Section
Bibliographic record
Abstract
Most existing path loss models for radio links enabled by reconfigurable intelligence surfaces (RISs) have been derived by assuming that each unit cell of these surfaces scatters incident waves individually. However, unit cells are mutually coupled and scatter collectively, as expressed by the complex radar cross-section (CRCS) of the surface. The CRCS can be computed by full-wave analysis, but this analysis has to be repeated for each of the many states of the RIS, requiring excessive resources. We present an approximate method to efficiently compute the CRCS of an RIS at any state, without repeated full-wave simulations. Our method accounts for the mutual coupling of unit cells and allows us to estimate the scattered fields in the main scattering direction of the RIS, at an accuracy that is comparable to full-wave analysis. Integrating this method with ray-tracing enables the modeling of wireless propagation in realistic, RIS-enabled communication channels over multiple RIS states.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".