An electrically pre-equalized 10-Gb/s duobinary transmission system
Bibliographic record
Abstract
Duobinary signaling is combined with a proposed electrical pre-equalization scheme to extend the reach of 10-Gb/s signals that are transmitted over standard single-mode fiber. The proposed scheme is based on predistorting the duobinary signal using two T/2-spaced finite-impulse response (FIR) filters. The outputs of the FIR filters then modulate two optical carriers that are in phase quadrature. Simulation results show that distances in excess of 400 km at bit-error rates less than 10/sup -15/ are possible. Incorporating a forward-error correction scheme can extend the reach to distances in excess of 800 km. The reach limitation arises not from chromatic dispersion but from fiber nonlinearity, relative intensity noise due to phase-modulation-to-amplitude-modulation noise conversion, and optical amplifier noise accumulation. To demonstrate the feasibility of implementing the proposed scheme, a test chip is implemented in a 0.5-/spl mu/m SiGe BiCMOS technology. The chip incorporates two 10-tap T/2-spaced FIR filters, which are sufficient to equalize a 10-Gb/s duobinary signal that is transmitted over distances in excess of 400 km. The pre-equalization capabilities of the chip are tested by postprocessing the measured chip output to mimic the effects of the optical channel.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".