An Innovative Receiver for Incoherent SAC-OCDMA Enabling SOA-Based Noise Cleaning: Experimental Validation
Bibliographic record
Abstract
We propose a new low complexity receiver for spectral amplitude optical coded division multiple access (SAC OCDMA) that enables intensity noise reduction using semiconductor optical amplifiers (SOAs). Compared to the standard receiver requiring two optical filters at the receiver side, our receiver requires only one optical filter. While a 1.4-dB power penalty in incurred, network capacity is unchanged, i.e., BER floors due to intensity noise have the same level. The primary motivation for the low complexity receiver is not reduced component count, but rather modifying the receiver so that promising SOA noise mitigation techniques might be employed to increase system capacity. SOA noise cleaning suffers from a major limitation: filtering after the SOA can negate most of the signal enhancement, the so-called post SOA filtering issue. The only solution to date for the post-SOA filtering effect in SAC-OCDMA is prohibitively complex McCoy ,J.Lightw.Technol., vol. 25, no. 1, pp. 394-401, Jan. 2007, i.e., requiring multiple SOAs per client. We demonstrate that our proposed receiver drastically limits the client side filtering, thus maintaining noise suppression and overcoming the post-SOA filtering effect. We compare BER at up to 10 Gb/s with and without noise cleaning. When a noise cleaning module is used, BER improvement of several orders-of-magnitude is observed when only a few users are active in the network. Examination of the noise properties, however, leads us to conclude that highly populated networks will have diminished improvement.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".