High-Bit-Rate Dense SS-WDM PON Using SOA-Based Noise Reduction With a Novel Balanced Detection
Bibliographic record
Abstract
The major drawback of incoherent broadband sources (BBSs) is their inherent intensity noise. Semiconductor optical amplifiers (SOAs) can be exploited at the transmitter to mitigate this noise. Optical filtering at the receiver, however, leads to the return of most of suppressed noise. Wider filtering at the receiver is the best known strategy to maintain performance gains, at the price of reduced spectral efficiency due to the tradeoff between noise cleaning and adjacent channel crosstalk. We introduce a novel balanced receiver for wavelength division multiplexing (WDM) systems that maintains greater noise cleaning and leaves spectral efficiency unchanged. Unlike standard receivers, our balanced scheme does not filter the desired signal. In this paper, we first demonstrate that the newly proposed receiver is equivalent to standard WDM receivers when no SOA for noise cleaning is present at the transmitter. Although a 2.9-dB power penalty is incurred, network capacity is unchanged, i.e., bit error rate (BER) floors due to intensity noise are the same. When SOAs are employed to mitigate severe intensity noise, we show that our receiver outperforms the wide filtering strategy by two orders of magnitude. Dense WDM capacity is demonstrated up to 10 Gb/s using a thermal source, a saturated SOA, and the balanced detection scheme. A BER of 10-6is achieved at 10 Gb/s; further improvement is possible using low overhead forward error correction or a better SOA design. This demonstrates the ability of spectrum-sliced wavelength division multiplexing (SS-WDM) passive optical networks (PONs) to operate at 10 Gb/s at good spectral efficiency. Error performance better than 10-9is achieved up to 8 Gb/s with 30-GHz optical channel bandwidth and 100-GHz spacing.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".