An Efficient Flicker-Free FEC Coding Scheme for Dimmable Visible Light Communication Based on Polar Codes
Bibliographic record
Abstract
Visible light communication (VLC) can provide short-range optical wireless communication, together with illumination using light-emitting diode (LED) lightings. Since conventional forward error correction (FEC) codes cannot provide two lighting related features, dimming support, and flicker mitigation, the existing coding schemes for reliable VLC usually rely on auxiliary coding techniques, which cause a complicated structure and a low transmission efficiency. In this paper, based on polar codes, an efficient and flicker-free FEC coding scheme for dimmable VLC is proposed to increase the transmission efficiency and simplify the coding structure. Taking advantage of polar codes' recursive encoding structure, the proposed scheme can guarantee the equal probability and the short runs of 1 s and 0 s for arbitrary code rate without extra coding components. Numerical results show that the proposed scheme can have twice higher transmission efficiency than the existing schemes. Furthermore, at a dimming ratio of 25% or 75%, the coding gain of the proposed scheme is about 4.6 dB and 1.4 dB higher than that of the Reed-Solomon (RS) codes based scheme and the low-density parity check code based scheme, respectively.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".