Light-Emitting Commutating Diodes for Optical Wireless Communications Within LED Drivers
Bibliographic record
Abstract
Although visible light communication (VLC) systems provide high density links for use in Internet-of-Things (IoT) devices, the design of high rate VLC transmitters that maintain luminaire efficacy is an open problem. In this article, a novel approach to the integration of VLC within light-emitting diode (LED) drivers is proposed through the replacement of freewheeling/blocking diodes with light-emitting devices termed a light-emitting commutating diodes (LECDs). In this manner, communications and illumination can be provided using a simple, cost effective design while employing no additional components. The subtle change of LED driver control signals facilitates the transmission of data from LECDs while simultaneously supporting illumination functions. Lighting controls such as dimming are maintained and combined with modulation through the use of overlapping pulse position modulation (OPPM) and performance is quantified. Prototype buck and boost converters with LECDs are implemented and their efficacy is measured. Though current commercial LEDs are not intended for such signalling applications, we experimentally demonstrate their feasibility in this application and suggest methods to make such converters reliable. It is demonstrated that the addition of an LECD improves the efficacy of the luminaire as compared to conventional LED drivers while simultaneously enabling a VLC downlink.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".