Optimization of Reconfigurable Intelligent Surface Codebooks Using a Mutual Coupling Aware CNN Model of Scattered Fields
Bibliographic record
Abstract
Digital reconfigurable intelligent surfaces (RISs) consist of unit cells that can operate in a finite number of discrete states, typically controlled by switching circuits. The selection of the state of each RIS cell, commonly referred to as the “codebook”, can be made similar to array design, assuming that the far field of the RIS is a weighted combination of the far fields of individual cells. However, the full-wave analysis of even a 1-bit RIS geometry reveals that mutual coupling between cells strongly affects the scattered fields of the RIS, creating a wide range of realizable scattering patterns. We show that a computationally efficient, mutual coupling aware model of RIS scattered fields (empowered by a convolutional neural network) enables the exploration of these patterns by an optimizer, to meet design objectives beyond those achievable by standard codebook optimization approaches. Moreover, we demonstrate the design of 1-bit RIS codebooks that are free of spurious (“quantization”) lobes—a problem that was previously resolved only by modifying the RIS unit cells, increasing their complexity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".