Performance of differential pulse-position modulation (DPPM) with concatenated coding over optical wireless communications
Bibliographic record
Abstract
The concatenation of marker and Reed–Solomon codes in order to correct insertion/deletion errors in differential pulse-position modulation (DPPM) over optical wireless communications is presented. The concatenated code decoding algorithms with hard-decision and soft-decision detection are presented. The performance of the hard-decision coded DPPM system is evaluated over both nondispersive and dispersive channels via analysis and simulation. It is shown that the coding gain provided by the concatenated code is approximately 4 dB when the code rate is about 0.7 and the channel is nondispersive. Over a dispersive channel, the coded system performs better than the uncoded system when the ratio of delay spread to bit duration is not high. A soft-decision detector is employed to combat intersymbol interference. The soft-decision decoding algorithm, which has low complexity and can be practically implemented, is described. The performance over nondispersive and dispersive channels is evaluated by analysis and simulation. It is shown that the soft-decision system requires approximately 2 dB less transmit power than the hard-decision system for additive white Gaussian noise and low-dispersive channels. Soft decoding also provides a performance improvement in high-dispersive channels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".