Optical impulse modulation for indoor diffuse wireless communications
Bibliographic record
Abstract
Current lasers and LEDs have far higher pulse rates than can be supported by the lowpass indoor diffuse optical wireless channel. Although high-frequency emissions are attenuated by the channel and are not detected by the receiver, a key insight of this paper is that these bands can be used to satisfy the channel non-negativity constraint. We define optical impulse modulation (OIM) in which data are confined to the lowpass region while the highpass region, which is attenuated by the channel, is used to satisfy the channel amplitude constraints. A mathematical framework for OIM is presented, and a simple suboptimal receiver filter is designed which is channel independent. Using a well-known exponential model for indoor diffuse optical channels, at a normalized delay spread of 0.2, the gain in optical average power of OIM with a simple lowpass receiver is shown to be 4.9 dBo which exceeds the gain of rectangular on-off keying (Rect-OOK) with a complex decision feedback equalizer. From an information theory point of view, at the same normalized delay spread of 0.2, the information rate of OIM with a lowpass receiver is shown to be 11.5% higher than that of Rect-OOK with a more complex whitened matched filter receiver.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".