Impact of the Refractive Index on the Achievable Rate of Liquid Crystal-Based Digital-RIS Indoor VLC Systems
Bibliographic record
Abstract
In wireless networks, reconfigurable intelligent surfaces (RISs) have recently been proven to have a significant impact. The RIS has been incorporated into wireless communication systems to improve security, increase coverage, reduce interference, and improve transmission quality. Digital RIS (DRISs) have also proven to provide better reflection management. However, the impact of the DRIS intrinsic parameters on the system's achievable rate, has not yet been investigated. Due to their availability and easily reconfigurable properties, liquid crystals (LCs) appear as suitable materials for use in DRIS, specifically in optical communication systems. This paper analyzes the effect of the LC's refractive index on the achievable rate of LC-based DRIS indoor visible light communication systems. Based on a set of discrete phase shifts, required refractive indices have been evaluated. The reflected power and achievable rates are determined with respect to incoming light wavelengths at 510 nm, 550 nm, and 670 nm. According to the obtained numerical results, there is no linear relationship between refractive indices, corresponding transition coefficients, phase shifts, light power, and achievable rate.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".