Nested PPM for Visible Light Communication With Heterogeneous Optical Receivers
Bibliographic record
Abstract
We consider visible light communication (VLC) using phosphorescent white light-emitting diodes (LEDs) and two different receiver types – a simple receiver for moderate data rate requirements and a sophisticated receiver for higher data rate requirements equipped with an optical blue filter. We address the question, whether it is viable to support both heterogeneous receiver types simultaneously, using a single LED-based transmitter, and characterize the trade-offs associated with a common waveform. In particular, with regard to a simplistic transmitter and receiver structure, we propose a “nested” pulse-position modulation (nPPM) scheme and show that it improves upon conventional time sharing, when the bit rate of the simple receiver is supposed to be retained, while realizing a higher bit rate for the sophisticated receiver. An analysis of the available signal-to-noise ratio at the receiver for a practical setting combined with analytical and simulated error performance results corroborates the feasibility of our approach. Furthermore, we devise an end-to-end signal model, which includes the electrical properties of the LED as well as interference effects associated with optical filtering, and assess the influence on the resulting error performance. Due to its simplicity, our nPPM scheme may be particularly relevant for future mass-market VLC deployments as well as for proprietary solutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".