Increasing the Capacity of SAC-OCDMA: Forward Error Correction or Coherent Sources?
Bibliographic record
Abstract
We consider three different strategies for maximizing the capacity while minimizing the cost of spectral amplitude coding optical code-division multiple access (SAC-OCDMA): incoherent sources, multilaser sources and forward error correction (FEC). Due to their low cost and wide optical bandwidth, incoherent sources are often considered for SAC-OCDMA. Such sources exhibit reduced spectral efficiency due to their intensity-noise- limited performance. For single user systems, coherent sources offer greater spectral efficiency and improved performance; this is not necessarily the case for OCDMA. Even coherent sources are ultimately intensity noise limited in SAC-OCDMA due to the beating of coherent signals from different users overlapping in bandwidth. The intensity noise in coherent systems can be eliminated by having the center frequencies of spectral bins be offset from nominal values by a unique differential amount for each user. This requirement, however, leads to exacting requirement for source quality control and stability, and thus greater cost. We examine via simulation how system performance is affected for coherent sources under various assumptions about the precision of frequency offsets during manufacture. Finally, we examine the effectiveness of FEC in combating intensity noise in a cost effective manner. We find that coherent sources must have precise frequency placement to outperform FEC combined with incoherent sources. FEC systems work best in networks with low statistical utilization, while multilaser systems work best under high load.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".