Optical Filter Design for Multi-Color VLC in Reflective Environments With Multiple Lighting Sources
Bibliographic record
Abstract
Multi-color visible light communication (MC-VLC) systems, which aim to transmit data in parallel with multi-color LEDs, use optical filters to separate color channels at the receiver side. However, the relatively wide spectrum of LEDs may cause inter-color interference (ICI). In addition, the passband of the optical filters shifts towards the blue wavelength depending on the angle of incidence (AoI) of the light, which may also increase ICI. In this paper, the problem of designing optical filters for MC-VLC systems in the presence of the blue-shift is investigated. The communication environment is assumed to be a typical room illuminated by multiple fixtures in accordance with lighting standards. Moreover, the walls in the room are reflective, resulting in non-line-of-sight (NLoS) components. The passband edges of the optical filter are optimized to maximize the average signal-to-interference-plus-noise ratio (SINR). The effect of the field-of-view (FOV) of the receiver on the system performance is also analyzed. Finally, a generic optical filter has been designed that can be used in standards-compliant environments for selected LEDs and a given FOV range. The results show that the designed optical filter provides a higher and uniform capacity than those in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".