Analysis of Optical CDMA Signal Transmission: Capacity Limits and Simulation Results
Bibliographic record
Abstract
We present performance limits of the optical code-division multiple-access (OCDMA) networks. In particular, we evaluate the information-theoretical capacity of the OCDMA transmission when single-user detection (SUD) is used by the receiver. First, we model the OCDMA transmission as a discrete memoryless channel, evaluate its capacity when binary modulation is used in the interference-limited (noiseless) case, and extend this analysis to the case when additive white Gaussian noise (AWGN) is corrupting the received signals. Next, we analyze the benefits of using nonbinary signaling for increasing the throughput of optical CDMA transmission. It turns out that up to a fourfold increase in the network throughput can be achieved with practical numbers of modulation levels in comparison to the traditionally considered binary case. Finally, we present BER simulation results for channel coded binary and -ary OCDMA transmission systems. In particular, we apply turbo codes concatenated with Reed-Solomon codes so that up to several hundred concurrent optical CDMA users can be supported at low target bit error rates. We observe that unlike conventional OCDMA systems, turbo-empowered OCDMA can allow overloading (more active users than is the length of the spreading sequences) with good bit error rate system performance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".