Experimental Study of a novel adaptive decision-directed channel equalizer in 28 GBaud RGI-DP-CO-OFDM transport systems
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".