Experimental Study of a novel adaptive decision-directed channel equalizer in 28 GBaud RGI-DP-CO-OFDM transport systems
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Bibliographic record
Abstract
We report and experimentally investigate the performance of an adaptive decision-directed channel equalizer (ADDCE) in reduced-guard-interval dual-polarization coherent-optical orthogonal-frequency-division-multiplexing (RGI-DP-CO-OFDM) transport systems. ADDCE retrieves an estimation of the phase noise value after an initial decision making stage by extracting and averaging the phase drift of all OFDM sub-channels. Moreover, it updates the channel transfer matrix on a symbol-by-symbol basis. We experimentally compare the performance of the ADDCE and the conventional equalizer (CE) combined with maximum-likelihood (ML) phase noise compensation and inter-subcarrier-frequency-averaging (ISFA) algorithms. The study is conducted at 28 GBaud for RGI-DP-CO-OFDM systems with quadrature-phase-shift-keying (QPSK) and 16 quadrature amplitude modulation (16-QAM) formats. Using ADDCE, zero-overhead laser phase noise compensation is accomplished and the overhead due to training symbol (TSs) insertion is significantly reduced. In addition, ADDCE offers a superior performance over the CE in the presence of synchronization timing errors and residual chromatic dispersion (CD). We also achieve a longer transmission distance than when using the CE. At a forward-error-correction (FEC) threshold of 3.8 × 10−3, using a cumulative overhead of less than 2.6%, transmission distances of 5500 km and 400 km were achieved for the cases of QPSK and 16-QAM RGI-DP-CO-OFDM, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it