Power reduction techniques for multiple-subcarrier modulated diffuse wireless optical channels
Bibliographic record
Abstract
In this paper, two novel techniques are proposed to reduce the average optical power in wireless optical multiple- subcarrier modulated (MSM) systems, namely in-band trellis coding and out-of-band carrier design. Data transmission is confined to a bandwidth located near DC. By expanding the signal set and coding over the increased degrees of freedom, an in-band trellis coding technique achieved an average optical power reduction up to 0.95 dBo over conventional MSM systems while leaving the peak optical power nearly unaffected. With a symbol-by-symbol bias, the received DC level can be detected to provide a degree of diversity at the receiver. In this manner, an additional average optical power reduction up to 0.50 dBo together with a peak power reduction of 0.46 dBo is achieved. Moreover, the unregulated bandwidth available in wireless optical channels is exploited and out-of-band carrier signals are designed outside the data bandwidth to reduce the average optical power. Average optical power reduction as high as 2.56 dBo is realized at the expense of 4 out-of-band carriers and an increase in the peak optical power. Finally, combining the three techniques achieves the best average optical power reduction of 2.63 dB optical.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".