Bibliographic record
Abstract
We propose for the first time to use a silicon photonics (SiP) solution for a passive optical network to both reduce signal-signal beat interference (SSBI) and recuperate a part of the downlink carrier for use in the uplink. The Kramers-Kronig (KK) receiver for direct detection of advanced modulation formats overcomes SSBI at the cost of a moderate carrier to signal ratio (>6 dB) and high oversampling (4×). We propose an optical SSBI solution that achieves better performance than KK and requires only standard sampling and low (3 dB) carrier to signal power ratio. The receiver is conceived for the downlink in passive optical networks, where carrier signal must be husbanded for re-use in the uplink. Using cost effective and power efficient SiP, the receiver filters the incoming signal, suppresses SSBI, and routes a portion of the carrier for use in the uplink. We experimentally examine the SSBI suppression in this paper. While previous demonstrations used bulky, discrete components, we achieve significant Q-factor improvement with a simple SiP solution. We examine the optimal frequency offset between the carrier and the microring resonator center frequency. The robustness to frequency drift, as well as the impact of imperfect filtering, is discussed and quantified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".