Improved Dimming Scheme based on Non-DC Free RLL Codes for VLC
Bibliographic record
Abstract
The dimming control is crucial for visible light communication (VLC) channels to maintain data transmission at different levels of lighting brightness. However, ensuring the dimming control while keeping a flicker-free channel remains a challenging task in VLC. Conventional techniques use the interleaving of compensation symbols (CS) applied on balanced codewords, usually obtained via run-length limited (RLL) codes. However, CS increases the redundancy used and are just thrown away during the decoding. Moreover, the interleaving step may increase the latency of the system. In this letter, new families of non-DC free RLL codes with variable weights are proposed. The dimming control can therefore be achieved without CS. Simulations stipulate that the proposed 4B7B code in FEC-coded channels achieve dimming ratios of 29% and 71% while improving on redundancy and error correction performance. For a 29% and 71% dimming ratios, at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> , gains in dB of 0.8 and 0.6 versus 3 and 2.6 are reported between the coded proposed RLL against the coded 1B2B and 4B6B with CS respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".