Polarization-Discriminated RSOA–EAM for Colorless Transmitter in WDM–PON
Bibliographic record
Abstract
The integrated reflective semiconductor optical amplifier (RSOA) and electro-absorption modulator (EAM) is viewed as an appealing solution to the colorless transmitter on the optical network unit (ONU) side of wavelength-division multiplexed (WDM) passive optical networks (PONs), for its broad modulation bandwidth and high optical gain. However, the conventional RSOA–EAM usually exhibits a poor upstream signal eye-diagram because it can hardly simultaneously saturate the downstream signal and boost the upstream signal as required. By exploiting the polarization-depended RSOA gain, we propose a polarization-discriminated RSOA–EAM to improve the quality of the upstream signal eye-diagram. In this device, the transverse electric polarized downstream signal is saturated by the high gain in the RSOA active region made of compressively strained multiple quantum wells, whereas the upstream signal is linearly amplified after polarization rotation. We find that, as the quality of the upstream signal eye-diagram improves with an increased polarization rotation angle, its power drops, which indicates that there exists an optimized rotation angle to reach a compromise between the upstream signal integrity and power. Simulation results show that the dynamic extinction ratio and output power of the upstream signal can reach 8.3 dB and 11 dBm, respectively, through the proposed device with its rotation angle set at an optimum value (80°), which exceeds the specification (6 dB and 4 dBm) of the upstream transmitter as required by the next-generation PON stage two. The quality of the upstream signal eye-diagram measured in Q-factor is improved by 10 dB compared to the conventional RSOA–EAM design without polarization rotation introduced.
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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 | 0.000 |
| 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".