Performance degradation of source matching in optical CDMA due to source coherence effects
Bibliographic record
Abstract
In this paper we study the performance of source matching technique for an optical code division multiple access (OCDMA) system in the presence of source coherence effects and square law detection process. We use a binary-asymmetric channel (BAC) model for an OCDMA system employing an all-optical passive correlator receiver. Source coherence effects lead to relative-intensity-noise (RIN) and phase-induced-intensity-noise (PIIN), which are included in our analysis. Previous studies only considered multiple access interference (MAI) noise, resulting in a Z-channel (where errors only occur for the transmission of data bit zero) model, and neglected RIN and PIIN. The presence of RIN and PIIN leads to errors occurring for both transmitted data bits one and zero, thus a BAC model. We show that source matching gain depends on the normalized source coherence time, defined as the ratio of the optical source coherence time to the bit duration. Our analysis shows that, while MAI limited analysis predicts that increasing the number of users increases the source matching gain, when taking into account RIN and PIIN, source matching gain is both bit rate and source type dependent, and tends to zero for very high numbers of users.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".