Site-Specific Design Optimization of Reconfigurable Intelligent Surface Codebooks
Bibliographic record
Abstract
Multipath propagation is ubiquitous in wireless communications and plays a critical role in channels enabled by reconfigurable intelligent surfaces (RISs). To date, most methods of RIS codebook design, i.e., the selection of each unit cell state, neglect multipath propagation and rely on idealized models that consider only the transmitter–RIS–receiver link. In this letter, we introduce a numerical, site-specific (SS) design paradigm in which RIS codebooks are tailored to the realistic multipath conditions of the deployment site. The framework combines ray-tracing with a convolutional neural network to map RIS codebooks directly to signal strengths at designated receivers, accounting for multipath propagation and practical factors such as cell-to-cell coupling. Using genetic algorithm optimization, we show that SS codebooks consistently achieve higher received signal strengths than conventional, site-agnostic (SA) designs. In addition, our developed framework supports comprehensive pre-deployment assessment of link budgets and design parameters, such as the number of unit cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".