Intensity directed equalizer for the mitigation of DML chirp induced distortion in dispersion-unmanaged C-band PAM transmission
Bibliographic record
Abstract
The directly modulated laser (DML) is one of the most cost-effective transmitter options in optical communication systems, but it introduces an additional impairment caused by the interaction between frequency chirp and chromatic dispersion for C-band transmission. In this paper, we propose a low-complexity intensity directed equalizer based on feedforward equalizer and decision feedback equalizer (FFE/DFE) to mitigate the chirp induced distortions, and remarkably improve the transmission performance of PAM signals generated by DML. The equalizer is based on the fact that the directly modulated PAM symbols with different intensity levels have different chirp frequencies, which will lead to different inter-symbol interference (ISI) contributions to their adjacent symbols due to the velocity difference caused by chromatic dispersion. To address this phenomenon, the proposed equalizer employs multiple sets of tap coefficients according to the intensity levels of PAM signals. With this equalizer and a commercial 16.8GHz DML, we demonstrate a 56Gb/s PAM4 transmission over a record 43km SSMF in the C-band without optical dispersion compensation under the 3.8 × 10−3 HD-FEC BER threshold.
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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) | 0.000 | 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) | 0.001 | 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".