Bandlimited Optical Intensity Modulation Under Average and Peak Power Constraints
Bibliographic record
Abstract
Bandlimited optical intensity channels, arising in applications such as visible light communications, require that all signals satisfy a bandwidth constraint as well as average, peak, and non-negativity amplitude constraints. In this paper, a 2-D signal space for bandlimited optical intensity channels is presented. A novel feature of this model is that the non-negativity and peak constraints are relaxed, and the signal space parameterizes the probability of the negative or peak amplitude excursions in the output. Although the intensity channel only supports non-negative amplitudes, the impact of clipping on system performance is shown to be negligible if the likelihood of the negative excursion is small. A tractable approximation using finite series is used to accurately compute the probability of clipping under average and peak optical power constraints. The optical power and spectral efficiencies using hexagonal lattice constellations are computed. Schemes designed in this paper have higher average and peak optical power gains than $M$ -ary pulse amplitude modulation (PAM) using previously established techniques for spectral efficiencies greater than 2.5 and 3.5 bits/s/Hz, respectively. For high spectral efficiency, e.g., greater than 6 bits/s/Hz, the proposed scheme attains a more than 2-dB average and peak optical power gain over 16-PAM using the previous approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".